This paper presents procedures to calculate the probability that the measurement originating from an extraneous
target will be (mis)associated with a target of interest for the cases of Nearest Neighbor and Global association. It
is shown that these misassociation probabilities depend, under certain assumptions, on a particular - covariance
weighted - norm of the difference between the targets' predicted measurements. For the Nearest Neighbor
association, the exact solution, obtained for the case of equal innovation covariances, is based on a noncentral
chi-square distribution. An approximate solution is also presented for the case of unequal innovation covariances.
For the Global case an approximation is presented for the case of "similar" innovation covariances. In the general
case of unequal innovation covariances where this approximation fails, an exact method based on the inversion of
the characteristic function is presented. The theoretical results, confirmed by Monte Carlo simulations, quantify
the benefit of Global vs. Nearest Neighbor association. These results are applied to problems of single sensor as
well as centralized fusion architecture multiple sensor tracking.
In this paper we compare the performance of two Multidimensional Assignment Algorithms (MDA), the Lagrangean Relaxation based S-D algorithm and the Sequential m-best 2-D algorithm, applied to a realistic problem in missile defense surveillance. The benchmark problem consists of a set of sources that provide "event" (track) estimates of multiple launches, via a number of communication networks to a Fusion Center (FC) which has to perform data association prior to fusion. The network model used "loses" the information tag that distinguishes reports from the same source transmitted through different networks, i.e., the track identity (ID) assigned by the source is not passed on. Only a track ID assigned by the network, and the source ID accompany the track. Thus detection and elimination of track duplications at the FC is needed. The proposed hierarchical approach to the problem requires the solution of several MDA problems before calculating the fused estimate, so accuracy of the solution of each is crucial. Examples with several launches, sources and networks are presented to compare the performance of the two assignment algorithms.
This paper formulates a benchmark data association problem in a missile defense surveillance problem. The specific problem considered deals with set of sources that provide "event" (track) estimates via a number of communication networks to a Fusion Center (FC) which has to perform data association prior to fusion. A particular feature of the network model is that the information to distinguish among reports from the same source transmitted through different networks is not available at the FC: the track identity (ID) assigned by the
source is not passed on, but only a track ID assigned by the network, and the source ID accompany the track. This makes it necessary to detect and eliminate track duplications at the FC among the messages with the same source ID but different network ID. The resulting data, organized into sensor lists, is associated using a likelihood
based cost function with one of the several existing multidimensional assignment (MDA) methods. A comparison of the following two association criteria: <i>Mahalanobis distance </i>(<i>"chi-square"</i>) and <i>likelihood ratio</i> (LR) is carried out. <i>It is shown that the LR yields significantly superior results.</i> The tracks obtained after association are fused using a Maximum Likelihood approach. An additional complication is that false reports can be also transmitted by the sources. Examples with several launches, sources and networks are presented to illustrate the proposed solution and compare the performances of two assignment algorithms - the Lagrangean relaxation based <i>S</i>-D and the sequential <i>m</i>-best 2-D - on this realistic problem.
Sonar tracking using measurements from multistatic sensors has shown promise: there are benefits in terms of robustness, complementarity (covariance-ellipse intersection) and of course simply due to the increased probability of detection that naturally accrues from a well-designed data fusion system. It is not always clear what the placement of the sources and receivers that gives the best fused measurement covariance for any target--or at least for any target that is of interest--might be. In this paper, we investigate the problem as one of global optimization, in which the objective is to maximize the information provided to the tracker. We assume that the number of sensors is known, so that the optimization is done in a continuous space. We consider di.erent scenarios and numbers of sensors. The strong variability of target strength as a function of aspect is integral to the cost function we optimize. Numerical results are given, these suggesting that certain sensor geometries should be used. We have a number of intuitive suggestions that do not involve optimization for sensor layout.