Multi-static passive radar (MPR) systems typically use narrowband signals and operate under weak signal conditions,\nmaking them difficult to reliably estimate motion parameters of ground moving targets. On the other hand, the\navailability of multiple spatially separated illuminators of opportunity provides a means to achieve multi-static\ndiversity and overall signal enhancement. In this paper, we consider the problem of estimating motion parameters,\nincluding velocity and acceleration, of multiple closely located ground moving targets in a typical MPR platform with\nfocus on weak signal conditions, where traditional time-frequency analysis-based methods become unreliable or\ninfeasible. The underlying problem is reformulated as a sparse signal reconstruction problem in a discretized\nparameter search space. While the different bistatic links have distinct Doppler signatures, they share the same set\nof motion parameters of the ground moving targets. Therefore, such motion parameters act as a common sparse\nsupport to enable the exploitation of group sparsity-based methods for robust motion parameter estimation. This\nprovides a means of combining signal energy from all available illuminators of opportunity and, thereby, obtaining\na reliable estimation even when each individual signal is weak. Because the maximum likelihood (ML) estimation of\nmotion parameters involves a multi-dimensional search and its performance is sensitive to target position errors,\nwe also propose a technique that decouples the target motion parameters, yielding a two-step process that\nsequentially estimates the acceleration and velocity vectors with a reduced dimensionality of the parameter search\nspace. We compare the performance of the sequential method against the ML estimation with the consideration\nof imperfect knowledge of the initial target positions. The Cram�©r-Rao bound (CRB) of the underlying parameter\nestimation problem is derived for a general multiple-target scenario in an MPR system. Simulation results are\nprovided to compare the performance of the sparse signal reconstruction-based methods against the traditional\ntime-frequency-based methods as well as the CRB.
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