This PDF file contains the front matter associated with SPIE Proceedings Volume 10633, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

Ground penetrating radar (GPR) subsurface sensing is a promising nondestructive evaluation (NDE) technique for inspecting and surveying underground utilities in complex urban environments, as well as for monitoring other key infrastructure such as bridges and railroads. A challenge of such technique lies on image formation from the recorded GPR data. In this work, a fast back projection algorithm (BPA) for three-dimensional GPR image construction is explored. The BPA is a time-domain migration method that has been effectively used in GPR image formation. However, most of the studies in the literature apply a computationally intensive BPA to a two-dimensional dataset under the assumption that an in-plane scattering occurs underneath the GPR antennas. This assumption is not precise for 3D GPR image formation as the GPR radiation scatters in multiple directions as it reaches the ground. In this study, a generalized form for an approximation to determine the scattering point in an air-coupled GPR system is developed which considerably reduces the required computations and can accurately localize the scattering point position. The algorithm is evaluated by applications on GPR data synthesized using GprMax, a finite-difference time domain (FDTD) simulator.

Three-dimensional radar imaging is becoming increasingly important in modern warfare systems, leading to an increased need for deeper understanding of the 3D scattering behavior of targets as simple as a cylinder, to as complex as a main battle tank or air defense unit. Fully polarimetric, three dimensional radar signature data have been collected using 1/16th scale models of tactical targets in several indoor compact radar ranges, corresponding to data from S-band to W-band. ISAR image pairs, collected at slightly different elevations, were interferometrically processed into 3D imagery. The data collection, analysis, and 3D visualization methods are presented. Additionally, the results of mathematical 3D correlation are described. A detailed analysis of both measured and predicted 3D radar data on the UMass Lowell nominal rocket simulator target will be presented.

Implementation of convolutional neural networks (CNNs) as classifiers has only recently found application in SAR multi-target classification. Despite the creation of several successful architectures, a general approach to CNN design and training has not been determined. In this paper, the basics of CNN architecture and learning algorithms are discussed. The MSTAR data set is used to demonstrate the effect of individual parameter changes to overall network performance.

Video-SAR systems that employ backprojection to generate frames offer efficient re-use of processed data in the image domain, minimal distortion due to wavefront curvature, a fixed frame viewing angle, and several other advantages over Doppler-based methods such as polar formatting. However, one challenge that persists is real-time autofocusing SAR imagery at high frame rates on small airborne radars with limited computational capacity. The work herein describes a method to condition the I & Q data before backprojecting onto the image grid so that blurring due to errors on the motion measurement system is minimized. The method takes advantage of the extremely efficient FFTs on Graphical Processing Units (GPU) to transform a select number of ranges lines to the Doppler domain so that phase gradient autofocus can be performed. The subsequent autofocus vector is fedback to the most recent portion of the aperture where phases of the I & Q samples are adjusted to mimic a correction in motion measurements. The data are then processed as usual with backprojection to ensure well-focused video frames, even during periods of severe turbulence, changes in GPS satellite sequencing or IMU errors.

Stepped-frequency waveforms are widely used in ultra-wideband (UWB) radar to obtain high resolution range profiles (HRRPs). In this paper, we consider high range resolution (HRR) improvement, i.e. sidelobe reduction and resolution enhancement, in stepped-frequency radar via weighted SParse Iterative Covariance-based Estimation (weighted SPICE) approach. Weighted SPICE is a unifying approach for four user parameter-free algorithms, namely SPICE, LIKES, SLIM and IAA. The latter three algorithms can be interpreted as weighted versions of SPICE with different data-dependent weights. Weighted SPICE is originally proposed for spectral estimation and array processing. We show that it can be used to obtain HRRPs in stepped-frequency radar as well, and comparisons among these four methods for HRR processing are also discussed in this paper. Additionally, different from spectral estimation applications, the main goal of HRR processing is to estimate the reflection coefficients {x_k } of the targets rather than the powers {p_k }. Thus, estimators which are able to obtain estimates {bxk } from {bpk }, such as Linear minimum mean square error (LMMSE) estimator and Capon estimator, are needed after obtaining {bpk } via weighted SPICE. As a minimum variance unbiased estimator, Capon is shown to have a better performance than LMMSE from the perspective of HRR processing. Numerical examples are presented to evaluate the performance of using all weighted SPICE algorithms with the LMMSE and Capon estimators, and IAA with Capon estimator is shown to outperform other weighted SPICE methods.

Compared with the ground-based Inverse Synthetic Aperture Radar (ISAR), although there are many advantages in the aerostat borne ISAR, but its radar platform is a degree of instability, which will interfere with ISAR imaging. For aerostat borne ISAR autofocus imaging, a high-resolution imaging algorithm based on phase retrieval principle is proposed in this paper. Theoretical analysis shows that the translational motion or vibration of the radar platform does not affect the magnitude of ISAR echo. Therefore, phase retrieval algorithm can eliminate the instability of the radar platform. Compared with the traditional algorithms, the results show that the proposed method in this paper can obtain better imaging results without estimating the motion parameters of the radar platform.

This paper describes the signal processing framework for scientific application of an airborne radar originally used for aviation hazard monitoring. The specific radar being developed is the “PARADOX” radar, which is based on the GSX-70 and GSX-80 radar system hardware from Garmin International. The processing framework is designed to enhance the resolution, improve signal processing, and achieve “research-grade” data quality control for both application domains. The processing framework is also applicable to other multi-mission radars that seek optimal weather measurement performance using hardware designed for other functions. A validation method of the processing framework through case analysis and comparison with ground-based radar measurements is described.

This paper presents a study on target track accuracy improvement for air-to-air (A/A) radar. A Multi-hypothesis Post-processing (MHPP) approach is proposed for improving the air target track accuracy. The MHPP approach consists of a Target Maneuver Detector (TMD) and a Target Maneuver-adapted Track Smoother (TMATS). TMD applies a simple tracking filter to the Kalman Filter (KF) outputs for making a decision on the presence of target maneuver. The TMD’s decision is then utilized to guide TMATS to improve the overall track accuracy. TMATS is a filterbank that takes into account multiple hypotheses on the target maneuvering status. In particular, TMATS is constructed with multiple smoothing/tracking filters, each of which is dedicated to a different target maneuvering scenario. The final track outputs are selected from a particular TMATS component according to the target maneuvering status. Monte Carlo simulations are conducted to demonstrate the effectiveness of the proposed MHPP approach. The robustness of the proposed MHPP approach against the degree of target maneuvering is also verified with simulations.

Binary phased codes have many applications in communication and radar systems. These applications, including spread spectrum communication and low probability of intercept radar, require low sidelobes and long code lengths. Many techniques for finding long binary phased codes with low sidelobes have been investigated in literatures. These techniques include exhaust search, neural network, and evolutionary methods, and they all have high computational cost. In this paper, we propose particle swarm optimization (PSO) to select long low sidelobe binary phased codes with reasonable computational cost. We investigate two techniques for initialization: random number approach and linear chirp approach and show that linear chirp initialization performs significantly better than random number approach. By implementing the proposed techniques, we demonstrate that PSO approach with linear chirp initialization can find binary codes with sidelobes equal to or lower than the neural network and genetic algorithm techniques in literatures.

The energy allocation of a transmit waveform ultimately dictates the effectiveness by which it extracts a target in a cluttered environment. The quantification of information present in the radar cross section offers notable advantages as a fitness function for the design of efficiently energy distributed waveforms for target identification. A robust method of suppression of both the temporal and spectral characteristics for target attenuating radar returns (clutter) is developed in this paper. By means of a priori knowledge of a target spectral response, a method of clutter mitigation for target identification using ultra-wide band (UWB) radar is developed. The robust design method takes after the Taguchi Method after and has seen growing use in biotechnology, statistics, and engineering as a method for both design and analysis. The Taguchi algorithm (TA) is created, based on an orthogonal matrix level design, in conjunction with the mutual information (MI) used as a criterion for convergence. This method efficiently allocates available resources within bins in which target spectral characteristics dominate those of which are undesired. As cognitive UWB radars constantly received clutter echoes and experience external noise sources, the mutual information is calculated adaptively during optimization between a transmit waveform given knowledge of the target, and the received waveform.

It is well-known that ultra-wideband (UWB) radar suffers substantial disturbances due to spectral overlap with common radio frequency interferers (RFI), such as commercial radio/TV broadcasts, cell phones, and ISM equipment. We can expect RFI to become more prevalent as the cost of the technology decreases and devices become more widely commercialized. Fortunately, the energy of typical RFI is concentrated in narrow frequency bands – i.e. sparse in the frequency domain – which lends the RFI removal task to a sparsity-driven estimation approach. Moreover, the radar echoes tend to be sparse in the time domain. The recent SParse Iterative Covariance-based Estimation (SPICE) algorithm is employed to exploit these properties for effective RFI mitigation. We compare the performance of SPICE with that of the robust principal component analysis (RPCA) in a simulated interference environment consisting of an actual ambient RFI recording scaled and added to an unadulterated radar signal. SPICE is a user-parameter free algorithm, making it easy to use in practical applications such as RFI mitigation; while, in contrast, RPCA requires a tuning parameter, whose optimal value was found to depend on the signal-to-interference ratio of the contaminated data. Moreover, SPICE is computationally more efficient than RPCA.

The U.S. Army Research Laboratory (ARL) recently designed and tested a new prototype radar, the Spectrally Agile Frequency-Incrementing Reconfigurable (SAFIRE) radar system, based on a stepped-frequency architecture to address challenges when operating under spectrally congested and spectrally restricted RF environments. SAFIRE is a vehicle-based, low-frequency, ultra-wideband (UWB) synthetic aperture radar (SAR) with frequencies spanning from 300 MHz to 2 GHz, where many frequency bands within this spectrum are either employed by other systems (congested RF environment) or prohibited (spectrally restricted RF environment). In this paper, we present recent SAFIRE stepped-frequency radar data collected from an arid test site, the adverse effects of SAR under congested and spectrally restricted RF environments to SAR imagery, and the application of our spectral information recovery technique to mitigate artifacts.

Information elasticity is a new concept which characterizes the role of information in making effective decisions in sensor processing. Information elasticity is defined as the ratio of the fractional increase in decision effectiveness to the fractional increase in information. Increasing the quantity of information used in radar processing has the ability to decrease the performance of the radar in certain contexts, depending on what constraints and objectives exist. Because of this phenomenon (known as information overload), it is advantageous to find the optimal amount of information tailored to the specific context the radar is used in. This paper analyzes the process of finding the point of information overload using the information elasticity model. This model is used in observation of different contexts in pseudorandom code pulse compression. In this model, the length of the pseudorandom code represents the amount of information. Increasing this quantity affects both the quality of pulse compression and constraints of the system. We observe this relationship between the constraints and information quantity by developing constraint functions. In this paper, two decision metrics are created for pseudorandom pulse compression, the first based on the peak to side-lobe ratio and the second based on the detection region of the radar.

Information elasticity is a new concept proposed by us to assess the role of information in making effective decisions in sensor processing. Information elasticity is defined as the ratio of the fractional increase in decision effectiveness to the fractional increase in information. One such application is the role of bandwidth when applied to target detection in ground clutter. Continuous, uniformly distributed, land clutter, such as snow, sand, or dirt, has a normalized radar cross section (NRCS) that increases with frequency in the microwave region of the electromagnetic (EM) spectrum. If a target’s radar cross section (RCS) increases slower than the clutter that surrounds it as the signal bandwidth increases, this can limit the amount of useful fractional bandwidth that can be used for achieving high range resolution. Using equations for the RCS of a typical sphere target and the overall NRCS of land clutter, it is possible to determine the signal-to-clutter ratio (SCR) at differing fractional bandwidth percentages. If the size of the frequency bins measured is kept constant, a higher fractional bandwidth will have more frequency bins causing an increase in processing time and therefore a decrease in the elasticity. Several different target shapes and types of distributed clutter were simulated, and it is shown that there can be a decrease in elasticity when the fractional bandwidth exceeds a certain value. Furthermore, factoring in the sensor processing time shows that there exists a tipping point beyond which information overload occurs resulting in negative elasticity.

In this paper, the concept of a post-selection operator current is introduced that is applicable to remote sensing physics as well as quantum mechanics. The post-selection operator (or cross-correlation for classical applications) current density (PSOCD) is defined in this paper, which also allows an operator current density to be. Both polarization operators and three dimensional operators have explicit Lie algebras, which impose lead to an algebra for the current which we discuss. When there is an evolution equation for signal such as the ambiguity function, the time evolution of a post-selected operator can be characterized. The underlying geometry that is implicit with this evolution is also applicable to other evolutionary/transport equations such as the Fokker-Planck equation, Brownian motion, and other equations that are used to model noise.

This paper presents the hardware design of a coherent 24 GHz radar system developed at the University of St Andrews to obtain micro-Doppler data. The system is based on the Analog Devices EV-RADAR-MMIC2 evaluation board which is based around a chipset of three integrated circuits: a two channel transmitter, a four channel receiver and a fractional-N frequency synthesizer. The evaluation board is combined with a number of other components to enable coherent operation with a PC-based data acquisition card and to boost the output power to increase the operational range. Three identical custom-made smooth-walled conical horn antennas for transmit and co- and cross-polar receive signals were designed and built for the radar system. It is shown that the performance of these high gain (24.5 dBi) antennas agrees extremely well with the design simulations. Finally, field trial results comprising human, bird and drone micro-Doppler data are shown to validate the system performance.

Automatic target recognition (ATR) using micro-Doppler analysis is a technique that has been a topic of great research over the past decade, with key applications to border control and security, perimeter defense, and force protection. Patterns in the movements of animals, humans, and drones can all be accomplished through classification of the target’s micro-Doppler signature. Typically, classification is based on a set of fixed, pre-defined features extracted from the signature; however, such features can perform poorly under low signal-to-noise ratio (SNR), or when the number and similarity of classes increases. This paper proposes a novel set of data-driven frequency-warped cepstral coefficients (FWCC) for classification of micro-Doppler signatures, and compares performance with that attained from the data-driven features learned in deep neural networks (DNNs). FWCC features are computed by first filtering the discrete Fourier Transform (DFT) of the input signal using a frequency-warped filter bank, and then computing the discrete cosine transform (DCT) of the logarithm. The filter bank is optimized for radar using genetic algorithms (GA) to adjust the spacing, weight, and width of individual filters. For a 11-class case of human activity recognition, it is shown that the proposed data-driven FWCC features yield similar classification accuracy to that of DNNs, and thus provides interesting insights on the benefits of learned features.

In areas of conflict around the globe, buried or obscured explosive hazards pose a frequent danger to both civilians and military personnel. Research in radar technology to preemptively detect these hazards has been ongoing for more than two decades. The U.S. Army Research Laboratory (ARL) is currently developing a low noise, ultra-wideband, spectrally-agile radar system to be implemented on an aerial platform. An airborne ground- penetrating radar (GPR) simulation was developed to aid future hardware design efforts. Measured antenna beam patterns are input into the simulation and used to calculate the antenna’s footprint on the ground. With the antenna footprint specified, resolution cells are created within the footprint based on synthetic aperture radar (SAR) phenomenology. A 2D-Gaussian function is used to represent the main lobe of the antenna (which is derived from the 3-dB beam-width of the antenna in the E- and H-planes). The radar cross section (RCS) of each resolution cell is then found using a model for normalized clutter RCS, which incorporates the system geometry. Point-like and distributed targets can be inserted into the simulation by adjusting the RCS of specific resolution cells. Finally, these parameters are implemented in a signal model, and different waveforms can be simulated, and their peak side lobe level (PSLL) and integrated side lobe ratio (ISLR) can be compared.

The Microwaves and Radar Institute of German Aerospace Center (DLR) is currently developing an experimental radar system called IoSiS (Imaging of Satellites in Space), for the purpose of gathering high-resolution radar images of objects in a low earth orbit. The basic purpose of the instrument is the analysis of satellite structures for detection of possible mechanical damages or irregularities generated by space debris, for example. Furthermore investigations on unknown objects or satellites can be performed. Based on inverse synthetic aperture radar (ISAR) geometry, the ground-based pulse radar creates high-resolution range profiles over a certain azimuth angle by tracking the space object or satellite using a steerable antenna system. The guided tracking of objects during overpass, whose trajectory is sufficiently known, allows wide azimuth observation angles. Thus high azimuth resolution in the order of the range resolution can be achieved. The range resolution is given by the radar bandwidth of up to 4.4 GHz resulting in a theoretical range resolution of up to few centimeters. Considering very high-resolution imaging of objects in a low earth orbit, several error sources have to be taken into account in order to achieve desired image quality. This paper outlines main challenges of the imaging process and discusses main error sources and its influence on the ISAR image. Such error sources, like atmospheric distortion or inaccurate orbit information, primarily generate severe blurring of the ISAR image making proper focusing very challenging. Therefore, proper error correction is essential.

The US Army Research Laboratory (ARL) has recently developed the Spectrally Agile Frequency-Incrementing Reconfigurable (SAFIRE) radar system during its ongoing research to provide ground vehicular standoff detection and classification of obscured and/or buried explosive hazards. The system is a stepped-frequency radar (SFR) that can be reconfigured to omit operation within specific sub-bands of its 1700 MHz operating band (300 MHz to 2000 MHz). It employs two transmit antennas and an array of 16 receive antennas; the antenna types are quad-ridged horn and Vivaldi, respectively. The system is vehicle-mounted and can be interchanged between forward- or side-looking configurations. In order to assess and evaluate the performance of the SAFIRE radar system in a realistic deployment scenario, ARL has collected SAFIRE data using militarily-relevant threats at an arid US Army test site. This paper presents an examination of radar imagery from these data collection campaigns. A discussion on the image formation techniques is presented and recently processed radar imagery is provided. A summary of the radars performance is presented and recommendations for further improvements are discussed.

This paper presents the design and calibration of a short-range, K band (18-26 GHz) frequency modulated continuous wave (FMCW) radar prototype with a theoretical range resolution of 2 cm, making it suitable for security screening applications. Additionally, this work examines the theoretical considerations for expanding the calibration algorithm for potential application in synthetic aperture radar (SAR). Radar based security scanners rely on large signal bandwidth to achieve range (depth) resolution capabilities on the order of a few centimeters; a system capable of 2 cm range resolution – an approximate requirement for a security sensor - must operate with 8 GHz of bandwidth. The radar presented in this paper uses commercially available off-the-shelf components to achieve the required bandwidth while minimizing prototyping cost. However, with 36% fractional bandwidth at 22 GHz, frequency dependent amplitude ripple and non-linear phase responses of the RF front end distort the radar signal, counteracting the benefit gained from wideband operation and degrading the attained resolution. The effects of this signal distortion are mitigated by implementing a Hilbert transform based calibration procedure that involves estimating the complex analytical signal of the measured radar response and then removing the unwanted components using a reference signal. Extending this algorithm for SAR requires recovering relative phase information that is lost during calibration and the procedure is discussed in this paper. At 2.5 m distance, the calibrated radar successfully resolves targets separated by 2.5 cm - a significant improvement from un-calibrated data wherein the same targets were indistinguishable even with 20 cm separation.

In this effort we report the new Ship Relative Instant Multispectral Positioning System for rapid initialization and real-time acquisition of relative pose for navigation inputs to the flight control system based on infrared multispectral guidance and novel electro-optic sensors with the goal of meeting the challenging Navy requirements for fully automated landing of the fixed wing aircraft on an aircraft carrier. The selection of eye safe infrared wavelength band around 1.5 microns for the system operation minimizes the risks for the pilots, maximizes signal-to-noise ratio due to lower solar background, expands the system reach due to lower scattering in the atmosphere and allows for the overall system cost reduction due to the use of readily available components from developed industry of optical communications. The feasibility of rapidly initializing detection, tracking and relative pose estimation through concept modeling and breadboard prototype testing will be presented.

This talk goes through the components in generic single-scan target tracking algorithms from filtering to data association, track initiation, and termination. In many areas, reference is made to functions in the open-source copyleft-free Tracker Component Library (available online) so that attendees can rapidly apply the algorithms that are discussed.

AKELA Inc. is a small company located in California that specializes in developing unique radar systems for the DoD and other government customers. Most of our research is focused on standoff through the wall (STTW) sensing and ground penetrating radar (GPR) over an ultra wide frequency range. Our systems range in size from a briefcase up to a truck-mounted antenna array. While many of the systems are monostatic, AKELA has also built distributed systems for perimeter monitoring and bistatic RCS measurements. This presentation will give an overview of AKELA’s current and past research programs in the STTW and GPR fields.

General Atomics, ASI has emerged as a leading purveyor of size limited airborne radars for remotely piloted vehicles (RPV). The Lynx B20A is a multi-modal system that includes SAR, video SAR, dismount MTI, and Maritime Wide Area Search (MWAS). GA-ASI is also developing the Due Regard Radar (DRR) for the Predator® B that provides the remote pilot with situational awareness of approaching aircraft over a field of view similar to that of an onboard pilot. GA-ASI hopes to significantly expand the role of RPVs for military and civilian use in the coming decades. Radar will be the lynchpin to achieving this goal.

ARTEMIS, Inc. has been supporting radar programs for nearly two decades with development and manufacturing. We create high-performance synthetic aperture radar (SAR) and MTI radar systems that are designed for size, weight, and power (SWaP) limited applications. The SlimSAR line of radars are compact, modular, multi-mode, multi-frequency-band, multi-polarization SARs, flexible in design and operation, with variations and configurations that allow for operation from 1,000 feet up to over 25,000 feet, and frequency bands through block conversion at UHF, L-band, and X-band (and any other desired frequency-band). The SlimSAR is capable of a variety of advanced radar application modes.

Acoustic-electromagnetic interaction is evaluated for radar detection of electronic targets. The transmitter consists of a radar-wave generator emitting a single electromagnetic (EM) frequency and an acoustic-wave generator emitting a single audio frequency. The EM wave and the acoustic wave interact at the target. The target re-radiates a new EM wave which consists of the original EM wave modulated by the acoustic wave. This re-radiated wave is captured by the radar’s receive antenna. The presence of measurable EM energy at any discrete multiple of the audio frequency away from the original radio-frequency (RF) carrier indicates target detection. Detection is demonstrated for purely-metallic as well as for RF electronic targets at a distance of 10 ft.

A data collection system using software defined radios to perform multi-static radar measurements is presented. The basic architecture and operational capabilities of the selected software defined radios (SDRs) are described. Issues associated with device synchronization are discussed, and waveform implementation procedures are also outlined. Finally, results of preliminary experiments are presented, indicating the potential of SDRs for realizing a cost-effective radar system testbed. In particular, it is demonstrated that by rearranging the SDR configuration, it becomes possible to realize various receive array configurations for detection of moving targets.

It is common practice to use a metal conducting sphere for radar calibration purposes. The aspect-independence of a sphere allows for a more accurate and repeatable calibration of a radar than using a nonspherical calibration artifact. In addition, the radar cross section (RCS) for scattering spheres is well-known and can be calculated fairly easily using far field approximations. For Doppler radar testing, it is desired to apply these calibration advantages to a dynamic target. To accomplish this, a spherical polyhedron is investigated as the calibration target. This paper analyzes the scattering characteristics for various spherical polyhedral geometries. Each geometry is analyzed at 3.6 GHz in two states: contracted and expanded. For calibration purposes, it is desired that the target have a consistent monostatic RCS over the entirety of its surface. The RCS of each spherical polyhedral is analyzed and an optimized geometry, for calibration purposes, is chosen.

Through Wall Imaging radars enable users from the areas of the police, military or disaster control to identify textures behind the wall that is urgently needed for later actions from a safe distance. A major challenge of the Through Wall Imaging technology is to get rid of the negative parts of the backscattered data so that the image can be interpreted correctly and unambiguously. An important factor affecting the image degradation is the first wall structure, which leads to distortion in the signal when the electromagnetic waves pass through the wall. In order to reconstruct a measured radar image of a complex target scene behind the wall as accurately as possible, it is mandatory to know the specific composition of the wall. The paper will give an overview of an investigation of wall structure determination by using microwave radiation including the theory of wave propagation in layered media and simulation results for radar range signatures of artificial and well defined wall structures. Furthermore measurement results of radar range signatures of real materials like calcium silicate brick and hollow brick as well as radar range signatures of layered materials in the frequency range of 1 to 8 GHz and 2 to 18 GHz are presented.

Many types of dark regions occur naturally or artificially in Synthetic Aperture Radar (SAR) and Coherent Change Detection (CCD) products. Occluded regions in SAR imagery, known as shadows, are created when incident radar energy is obstructed by a target with height from illuminating resolution cells immediately behind the target in the ground plane. No return areas are also created from objects or terrain that produce little scattering in the direction of the receiver, such as still water or flat plates for monostatic systems. Depending on the size of the dark region, additive and multiplicative noise levels are commonly measured for SAR performance testing. However, techniques for radar performance testing of CCD using dark regions are not common in the literature. While dark regions in SAR imagery also produce dark regions in CCD products, additional dark regions in CCD may further arise from decorrelation of bright regions in SAR imagery due to clutter or terrain that has poor wide-sense stationarity (such as foliage in wind), man-made disturbances of the scene, or unintended artifacts introduced by the radar and image processing. By comparing dark regions in CCD imagery over multiple passes, one can identify unintended decorrelation introduced by poor radar performance rather than phenomenology. This paper addresses select dark region automated measurement techniques for the evaluation of radar performance during SAR and CCD field testing.

The principle of reciprocity for antennas allows one to take advantage of the duality between the broadcast and receive functions of an antenna, where the requirements are known sufficiently for one function to be accomplished. Since these can be viewed as dual functions to each other, knowing one is sufficient to determine how to do the other provided the antenna can be treated as a linear system. Philip Woodward did foundational research in the use of information theory to the design of radar receivers as well as determining a methodology for synthesizing an antenna pattern in the far field given one started with an antenna array in the near field. He also determined that using an information theory formulation how knowledge of the far field pattern that was broadcast is sufficient to determine the receiver characteristics needed to detect signal back at the receiver (Woodward's Synthesis Principle). The Aharonov Ansatz in scattering theory is based upon the principle any (sensor) measurement process. In principle, detector design can be "matched" to signal interaction or to the design of an apparatus or optimized so that there is a mathematical solution to the receiver design (in the classical sense). The Aharonov Ansatz applied to antenna theory, especially to antenna design, suggests a new paradigm is possible based on the desire to detect weak signals with an antenna or conceal or officiate using a broadcast signal. When combined with the potential to realize an antenna pattern based on metamaterials, it becomes possible to synthesize fairly arbitrary electromagnetic characteristics. This provides a radical approach to usage of waveform synthesis or waveform reception for antennas. In particular, we outline a methodology for both these functions for an analytical formulation of a metamaterial design.

In this paper, a tunable antipodal Vivaldi antenna is presented for frequency scanning in UWB applications. The upper and lower patches of tapered slot antenna (TSA) consist of two different gradient edges from which frequency scanning function is obtained. Microstrip line of 50 Ω impedance is used for feeding the antenna. The tuning is achieved by varying the DC biasing of Ferrite substrate. The main beam of the E-plane radiation patterns at single operating frequency of 8 GHz, are -40°, 15 °, and -10 °, respectively, which clearly shows the frequency scanning function of the antenna.

This study summarizes recent progress on the Drone Detection and Mitigation Radar (DDMR) concept, with focus on polarimetric radar signature of small UAS at different frequency bands, micro-Doppler impact, and the optimization of sweeping noise jamming solution of the communication links. New results of measurements are presented and effort to achieve the optimal effectiveness of the sweep jamming is summarized.

Ultrawideband noise signals have shown proven benefits in the realm of remote sensing for decades--aiding in the detection and localization of potentially harmful concealed objects. The characteristics of these waveforms show promise in the area of nondestructive testing for the detection of defects within multilayered structures. In this paper, we develop an approach to identify noise waveforms that will perform as effectively, or outperform, other common waveforms used in microwave imaging. Experimental data are gathered using a microwave imaging system operating in the X-band frequency range to detect the presence of water in dielectric materials and air voids in concrete walls reinforced with glass-fiber reinforced polymers.

This paper discusses the theoretical considerations for direction-of-arrival (DOA) estimation using antenna arrays in the presence of mutual coupling. In arrays, the relative proximity of antenna elements results in some manner of near-field mutual coupling that can negatively impact the array performance. In particular, mutual coupling can degrade the quality of DOA estimations and reduce the ability of the array to perform high-quality correlation processing and direction finding. The expected variance of an array performing DOA estimation is inversely related to the Fisher information matrix of the system. Theoretical radiated fields of a linear antenna array were compared to more realistic behavior of the equivalent architectures produced in electromagnetics simulation software. The mutual coupling between all the elements in an array can be a difficult phenomenon to precisely define, as it is an iterative process with many higher-order effects. To circumvent this, a mutual coupling matrix is defined as the relation between the theoretical radiation characteristic of an array and its simulated counterpart. An inverse solution method was used to solve for the mutual coupling interactions. The expected impact of mutual coupling in a DOA estimation context was then mathematically established by propagating the mutual coupling matrix through calculation of the Fisher information matrix and compared to the case of no mutual coupling. It was found that taking mutual coupling into consideration yields a higher Cramer-Rao Lower Bound and as a result a greater RMS angle error in a DOA estimation context. Mutual coupling was also found to have a somewhat greater impact on the Cramer-Rao Bound at S-band than at X-band.

Quantum hyper-entanglement, multi-photon entanglement and quantum networks are combined to develop enhanced sensing capabilities. Quantum hyper-entanglement refers to entanglement in more than one degree of freedom, e.g. polarization, energy-time, orbital angular momentum (OAM), and frequency. Multi-photon entanglement involves entanglement typically between more than two photons, whereas hyper-entanglement is generally described as occurring between two photons referred to as the signal and ancilla photon. Multi-photon states representing an increasing hierarchy of robustness are discussed. These include N00N states, M&N states and linear combinations of M&N states. A M&N state, the (N, 1) states is shown to be very useful. A method of constructing M&N states using Shrodinger kitten states is considered. Multi-photon entanglement and hyper-entanglement are combined for enhanced sensing. This combined approach permits improvement in measures of effectiveness (MOEs) like SNR, signal to interference ratio (SIR), time-on-target (TOT), Holevo bound, and system range. The combined approach yields significant improvements in resolution both by permitting an effective reduction in wavelength used for measurement as well as decreasing the related Cramer Rao lower bound. This in turn permits sensors based on these concepts to have enhanced parameter estimation capabilities. These parameters can include range, bearing and elevation. Additional enhancements are found by combining the multi-photon hyper-entangled states with the idea of a quantum network. Quantum networks are a collection of nodes that may have quantum memory. This approach can significantly reduce loss; offer noise and interference resistance; decrease measurement error; and reduce size, weight, power and costs (SWAPC) for the overall system.

Cognitive radar is a novel concept for next-generation radar systems, which as part of the perception-action cycle to improve the measurement process based on dynamic changes in the environment. Although most work in this area to-date have focused on adaptation on the transmitted waveform, in this paper, we propose adaptive control of novel multifunctional reconfigurable antennas (MRAs) as a mechanism for action within the cognitive radar framework. Reconfigurable parasitic layer based MRAs have the capability of dynamically and simultaneously changing its electromagnetic characteristics (mode of operation), e.g. antenna beam pattern, polarization, center frequency, or a combination of thereof. Different modes of an MRA are controlled via RF switches interconnecting the pixels of the reconfigurable parasitic layer. This enhanced capability can be controlled using adaptive mode selection schemes. In particular, an array of MRAs provides more degrees of freedom, where each element of an array can be controlled to generate one of many modes depending on the environmental measured variables as a feedback mechanism. In this work, a designed and fabricated reconfigurable parasitic layer based MRA operating over 4.94-4.99 GHz band with 25 different radiation patterns, i.e., modes of operation, is utilized for cognitive direction-of-arrival (DoA) estimation and target tracking. A novel computationally efficient iterative mode selection (IMS) technique for MRA arrays is developed, where the modes are cognitively selected to minimize the DoA estimation error in target track. It is demonstrated that the proposed cognitive mode selection for MRA arrays achieves remarkably lower estimation errors compared to uniform pattern arrays without adaptive capability.

Next generation cognitive radar/radio systems rely on dynamic spectrum access (DSA) to adaptively and ef- ficiently utilize the radio frequency (RF) spectrum. Such technology must detect, predict, and avoid channels occupied by RF interference. Conventional spectrum sensing methods may fail to determine signal occupancy states during transition periods. Predicting RF activity reduces the probability of interference during such transition periods and improves the overall efficiency of DSA schemes. This work employs a one-step ahead prediction approach to determine future busy or idle states through linear support vector regression (SVR). Supervised learning forecasts future signal energy which then acts as a decision statistic to determine occupancy in a sub-band of interest. The scheme’s prediction accuracy is evaluated with respect to input signal-to-noise ratio (SNR) and RF activity as a function of mean busy/idle time. Generalizing RF activity as an alternating renewal process allows exponential random variables to generate simulated data for SVR training and testing. The results show that this approach predicts RF activity with high accuracy over various signal traffic statistics and SNRs. Prediction accuracy is also evaluated with respect to the expected busy/idle transitions given activity statistics.

Conventional signal processing to estimate radar Doppler frequency often assumes uniform pulse/sample spacing. This is typically more for the convenience of the processing. More recent performance enhancements in processor capability allow optimally processing nonuniform pulse/sample spacing, thereby overcoming some of the baggage that attends uniform sampling, such as Doppler ambiguity and SNR losses due to sidelobe control measures.

Radar receivers with multiple receive channels generally strive to make the receive channels as ideal as possible, and as alike as possible. This is done via prudent hardware design, and system calibration. Towards that end, we require a quality metric for ascertaining the goodness of a radar channel, and its match to sibling channels. We propose a relevant and useable metric to do just that.

Radar target detection is determined by the energy received from the target and compared with the energy of background noise. The radar range equation accounts for the signal-to-noise ratio (SNR) due to transmitter, return path, receiver, integration, losses, and radar cross sections of targets. Frequency Modulated Continuous Wave (FMCW) radars are effective in distinguishing between moving targets and clutter. However, a weak target in the presence strong clutter can be easily overwhelmed, especially when the target is slow moving. In addition, a slow moving target can be undetected in the presence of wind-blown foliage. Wind-blown foliage can contribute to Doppler shifts caused by movements of branches and leaves, which can be challenging in target detection.

In this paper, we will discuss clutter mitigation caused by wind-blown foliage and clutter mitigation with slow-moving targets. Traditional approaches, such a pulse canceler essentially is a low-pass filter is designed to remove slow moving clutter and is not effective in mitigating foliage clutter during windy conditions. In this paper, we introduce a method to pre-process radar returns with a wavelet transform. The wavelet transform produces subband channels that are progressively smaller which can reduce the order of operations. The subband channels will further be processed with coherent integration and coherent subtraction to mitigate strong clutter introduced by stationary objects that close to a target. We will also investigate the mitigation of clutter due to wind-blown foliage using subband channels to estimate covariance, and extract singular value decompositions. A detection of wind-blown clutter is kept track in temporal bookkeeping, called a clutter map. The entry in the clutter map is deleted when clutter is not present or the expiration of the entry is reached.

Applications seeking to exploit electromagnetic scattering characteristics of an imaging or detection problem typically require a large number of electromagnetic simulations. Because these simulations are often computationally intensive, valuable resources are required to perform the simulations in an efficient and timely manner, which is not always freely available or accessible. In this work, we investigate the utility of deep learning for electromagnetic simulation prediction. Specifically, we explore using artificial neural networks to learn the connection between a generic object and its resulting bistatic radar cross section, thereby removing the need to repeatedly perform timely simulations. Such a system would be trained in an offline setting and consequently enable rapid bistatic radar cross section predictions for new objects in the future. While deep learning can be seen as a computationally expensive technique, this cost is only experienced during the training of the system and not subsequently in the acquisition of results. The goal of this work is to learn the applicability of deep learning for electromagnetic simulation prediction as well as its potential limitations. Several simple objects are investigated and a thorough statistical analysis will be used to assess the performance of our proposed method.

An ultra-wide-band (UWB) multiple-input multiple-output (MIMO) radar with a sparse array is designed and manufactured for three-dimensional near-field imaging applications such as concealed weapon detection. Contrary to existing mmW imaging radars, UWB components working in the lower microwave frequencies are more cost effective and yield images with resolutions satisfactory for contraband detection while not raising concerns related to personal privacy. A UWB sparse array provides resolution values equivalent to a fully populated array with a similar aperture size albeit with much fewer antenna elements, while yielding lower sidelobe levels compared to a narrowband sparse array. Performance of the proposed system is studied using a full-wave electromagnetic simulation environment which is capable of modelling the antenna array, the environment and the target in 3D while allowing modifications in mechanical and electrical properties of the materials. For image reconstruction, Kirchhoff-migration and back-projection algorithms are used and performances of these algorithms are compared. The effects of the spatial and temporal frequency response of the antenna array as well as array calibration on the image quality are also studied. A prototype of UWB MIMO sparse antenna array in Archimedean spiral configuration with an RF switch matrix is manufactured. Measurements are performed using a stepped-frequency continuous waveform (SFCW) transceiver for various metallic and non-metallic targets. It is observed that these targets are identifiable in the images formed based on measurement data. Consequently, promising concealed weapon detection performance is demonstrated with full-wave electromagnetic simulation and experimental results.

Signal processing techniques employed by a software-defined radar are presented. First, the radar system is described in brief, illustrating how software-defined radios (SDRs) are leveraged to implement a baseline radar functionality. Next, multiple, required processing steps are presented, showing how target signatures can be extracted from raw radar measurements. All of these techniques are applied to the moving target indication (MTI) problem, and examples of multiple moving target signatures are displayed.

Classical Directions of Arrival (DoA) algorithms estimate the time delays associated with the signal received at an array of sensors through phase information. Most existing wideband algorithms decompose the signals received by an antenna array into multiple narrowband frequencies, and then the wideband DOAs are estimated by coherent or incoherent combination of signal and noise subspace information at multiple source frequencies. A novel algorithm for finding the direction of arrival (DoA) for wide-band chirp sources is introduced in this study, where frequency shift rather than phase shift is utilized to estimate the signal time delays between sensors, eliminating many limitations due to phase ambiguity like spatial sampling, leading to finer angular resolution between multiple sources. The proposed algorithm processes the data using Discrete Chirp Fourier Transform (DCFT) that invokes the exact chirp model in the signal leading to more precise estimates compared to general wideband DoA methods that do not exploit the chirp model. Use of Compressed Sensing (CS) enables exploitation of the sparsity in the DCFT-domain data for highly accurate DoA estimation. Reduced number of measurements are required for CS optimization processing by making use of the sparsity of the DCFT coefficients. The proposed approach eliminates the need for correlation, iterations, and time-frequency analysis needed by many classical chirp signal parameter estimation algorithms. Theoretical derivation is given and simulation results of the new algorithm for single and multiple wide-band chirp sources show significant performance enhancement even in highly noisy environment.