In this paper, we consider the problem of multitarget tracking in a multi-static passive radar system using Doppler-only measurements. In a multi-static configuration, the observability and estimation accuracy of target states can be significantly improved by simultaneously exploiting all available measurements. Track-before-fuse and fuse-before-track are the two fusion paradigms proposed in the literature to utilize such multi-static measurements. The fuse-before-track approach involves a minimal information loss and thus achieves a better accuracy and robustness than the track-before-fuse counterpart. However, despite the obvious advantages in terms of estimation accuracy and robustness, the centralized measurement fusion approach is difficult due to the prohibitive computational cost. As such, the track-before-fuse approach has been commonly used in multi-static passive radar tracking systems using Doppleronly measurements. In this paper, we exploit a group-sparsity based algorithm to simultaneously utilize the Doppler shift measurements at all bistatic pairs to obtain the target state estimates directly in Cartesian coordinate system. The estimated target states at each sampling instant are then fed as the inputs to the linear Gaussian mixture probability hypothesis filter, which removes the false measurements and correctly associates the measurements to the respective targets. Simulation results are provided to validate the ability of the proposed method to successfully handle the multi-target tracking problem in a challenging environment characterized by missed detection and false measurements.978-1-4799-8232-5/151$31.00@2015IEEE
Multi-static passive radar (MPR) systems typically use narrowband signals and operate under weak signal conditions, making them difficult to reliably estimate motion parameters of ground moving targets. On the other hand, the availability of multiple spatially separated illuminators of opportunity provides a means to achieve multi-static diversity and overall signal enhancement. In this paper, we consider the problem of estimating motion parameters, including velocity and acceleration, of multiple closely located ground moving targets in a typical MPR platform with focus on weak signal conditions, where traditional time-frequency analysis-based methods become unreliable or infeasible. The underlying problem is reformulated as a sparse signal reconstruction problem in a discretized parameter search space. While the different bistatic links have distinct Doppler signatures, they share the same set of motion parameters of the ground moving targets. Therefore, such motion parameters act as a common sparse support to enable the exploitation of group sparsity-based methods for robust motion parameter estimation. This provides a means of combining signal energy from all available illuminators of opportunity and, thereby, obtaining a reliable estimation even when each individual signal is weak. Because the maximum likelihood (ML) estimation of motion parameters involves a multi-dimensional search and its performance is sensitive to target position errors, we also propose a technique that decouples the target motion parameters, yielding a two-step process that sequentially estimates the acceleration and velocity vectors with a reduced dimensionality of the parameter search space. We compare the performance of the sequential method against the ML estimation with the consideration of imperfect knowledge of the initial target positions. The Cramér-Rao bound (CRB) of the underlying parameter estimation problem is derived for a general multiple-target scenario in an MPR system. Simulation results are provided to compare the performance of the sparse signal reconstruction-based methods against the traditional time-frequency-based methods as well as the CRB.
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