Abstract
Abstract 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.
Highlights
Multi-static passive radar (MPR) has recently attracted significant research interests primarily due to its low cost and covertness compared to a conventional radar system
With the a priori knowledge that Doppler signatures corresponding to multiple illuminators of opportunity share the same set of motion parameters, i.e., velocity and acceleration of targets, as a common sparse support, we develop a new motion parameter estimation technique exploiting the group sparsity-based signal reconstruction
By exploiting the fact that the Doppler signatures of the targets corresponding to different bistatic links share the same target motion parameters as a common sparse support in the discretized parameter search space, the underlying problem is reformulated as a group sparse signal reconstruction problem
Summary
Multi-static passive radar (MPR) has recently attracted significant research interests primarily due to its low cost and covertness compared to a conventional radar system. With the a priori knowledge that Doppler signatures corresponding to multiple illuminators of opportunity share the same set of motion parameters, i.e., velocity and acceleration of targets, as a common sparse support, we develop a new motion parameter estimation technique exploiting the group sparsity-based signal reconstruction. This enables us to effectively fuse data corresponding to all available illuminators to accumulate sufficient signal energy without further extending the CPI. Diag(.) and tr(.), respectively, denote the diagonal and trace operations
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