Proceedings Article | 23 June 1997
KEYWORDS: Sensors, Target detection, Data fusion, Surveillance, Classification systems, Data modeling, Binary data, Defense systems, Radar, Optical sensors
This paper presents an approach to detection, tracking, classification and sensor management based on recursive evaluation of a joint multitarget probability. This joint multitarget probability is the conditional probability pc(subscript 1n,...,cn(x1,...,xn/Z) that there are exactly n targets of class c1,...,cn located in cells x1,...,xn based on a set of observations Z. This is applied to the problem of estimating the state of a collection of targets moving between discrete cells on a line. A cell can contain more than one target. For the model problem, there are two target classes and the number of targets is not known a priori. The targets are modeled as moving independently with Markov transitions to nearest- neighbor cells. There is one sensor with two modes that can only be used one at a time. These are: a detection mode which can determine whether a cell contains targets but provides no information about target class; and a classification mode that provides little information about the presence or absence of targets but can differentiate between the target classes. For each sensor dwell, the sensor samples a single cell. The conditional probability pmn,c1,...,cn(z/x1,...,xn) for sensor output z when mode m is used, given the target location and classes is known. Bayes' rule is applied directly to update the multitarget density for each output. As a basis for sensor management, the expected discrimination gain when a cell is sampled with a particular sensor can be computed. The sensor and cell to maximize the expected gain for each dwell can then be selected. In comparison to directly sampling all of the cells, optimizing the discrimination significantly increases the probability of detecting and localizing the targets.
