This paper examines the benefits of using reconnaissance and targeting imagery in the delivery of air-to-ground guided munitions. In particular, the paper considers the use of third-party imagery to improve the accuracy of scene-matching object localisation algorithms and to improve the delivery accuracy of air-launched seeker-guided weapons. The analysis focuses on a simulated engagement, consisting of an infrared imager placed on an airborne reconnaissance platform, a fast-jet delivery aircraft equipped with a modern electro-optical targeting pod, a seeker-guided weapon model, and a ground target moving in a highly cluttered environment. The paper assesses different strategies for utilizing the target position data from the three imaging systems (reconnaissance, targeting pod and weapon seeker).
This paper reviews a research program aimed at extracting and utilizing object localization information from sequences of visible band and infrared imagery. The techniques are entirely passive and are based on the relative positions of objects and features taken from a pre-prepared scene database. The techniques used in this project are based on existing techniques for navigation by Scene Matching and Area Correlation (SMAC) and have been adapted for the object localisation task. The paper also considers the use of a Multiple Hypothesis Tracking (MHT) system for the automatic tracking of the known ground features.
Most modern fast jet aircraft have at least one infrared camera, a Forward Looking Infra Red (FLIR) imager. Future aircraft are likely to have several infrared cameras, and systems are already being considered that use multiple imagers in a distributed architecture. Such systems could provide the functionality of several existing systems: a pilot flying aid, a modern laser designator/targeting system and a missile approach warning system. This paper considers image-processing techniques that could be used in a distributed aperture vision system, concentrating on the harmonisation of high resolution, narrow field of view cameras with low-resolution cameras with wide fields of view. In this paper, consideration is given to the accuracy of the registration and harmonisation processes in situations where the complexity of the scene varies over different terrain types, and possible use of supplementary motion information from inertial measurement systems.
The Kalman filter, which is optimal with respect to Gaussian distributed noisy measurements, is commonly used in the Multiple Hypothesis Tracker (MHT) for state update and prediction. It has been shown that when filtering noisy measurements distributed with asymptotic power law tails the Kalman filter underestimates the state error when the tail exponent is less than two and overestimates it when the tail exponent is greater that two. This has severe implications for tracking with the MHT which uses the estimated state error for both gating and probability calculations. This paper investigates the effects of different tail exponent values on the processes of track deletion and creation in the MHT.
KEYWORDS: Data fusion, Filtering (signal processing), Error analysis, Sensors, Target recognition, Matrices, Target detection, Detection and tracking algorithms, Global Positioning System, Monte Carlo methods
This paper examines the requirement for accurate estimates of the statistical correlations between measurements in a distributed air-to-ground targeting system. The study uses results from a distributed multi-platform targeting simulation based on a level-1 data fusion system to assess the extent to which correlated measurements can degrade system performance, and the degree to which these effects need to be included to obtain a required level of accuracy. The data fusion environment described in the paper incorporates a range of target tracking and data association algorithms, including several variants of the standard Kalman filter, probabilistic association techniques and Reid's multiple hypothesis tracker. A variety of decentralized architectures are supported, allowing comparison with the performance of equivalent centralized systems. In the analysis, consideration is given to constraints on the computational complexities of the fusion system, and the availability of estimates of the measurement correlations and platform-dependent biases. Particular emphasis is placed on the localisation accuracy achieved by different algorithmic approaches and the robustness of the system to errors in the estimated covariance matrices.
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