Abstract
Over the past few years, the global navigation satellite system (GNSS)-based passive radar (GBPR) has attracted more and more attention and has developed very quickly. However, the low power level of GNSS signal limits its application. To enhance the ability of moving target detection, a multi-static GBPR (MsGBPR) system is considered in this paper, and a modified iterated-corrector multi-Bernoulli (ICMB) filter is also proposed. The likelihood ratio model of the MsGBPR with range-Doppler map is first presented. Then, a signal-to-noise ratio (SNR) online estimation method is proposed, which can estimate the fluctuating and unknown map SNR effectively. After that, a modified ICMB filter and its sequential Monte Carlo (SMC) implementation are proposed, which can update all measurements from multi-transmitters in the optimum order (ascending order). Moreover, based on the proposed method, a moving target detecting framework using MsGBPR data is also presented. Finally, performance of the proposed method is demonstrated by numerical simulations and preliminary experimental results, and it is shown that the position and velocity of the moving target can be estimated accurately.
Highlights
Compared with other opportunistic illuminators, the global navigation satellite system (GNSS) is a good choice for passive radar given its permanent global coverage, plentiful satellite resources and ease for synchronization [1,2,3,4]
The GNSS-based passive radar (GBPR) system is based on the bi-static model, where a single GNSS satellite is used as the transmitter
The Random Finite Set (RFS) and Finite Set Statistics (FISST) frameworks proposed by Mahler [17,18] have gained much attention and several popular filters such as the probability hypothesis density (PHD) filter [19], the cardinalized PHD (CPHD) filter [20], the multi-Bernoulli (MB) filter [21], the Poisson MB mixture (PMBM) filter [22], and the generalized labeled MB (GLMB) filter [23,24], have been developed based on the FISST framework
Summary
Compared with other opportunistic illuminators, the global navigation satellite system (GNSS) is a good choice for passive radar given its permanent global coverage, plentiful satellite resources and ease for synchronization [1,2,3,4]. The Random Finite Set (RFS) and Finite Set Statistics (FISST) frameworks proposed by Mahler [17,18] have gained much attention and several popular filters such as the probability hypothesis density (PHD) filter [19], the cardinalized PHD (CPHD) filter [20], the multi-Bernoulli (MB) filter [21], the Poisson MB mixture (PMBM) filter [22], and the generalized labeled MB (GLMB) filter [23,24], have been developed based on the FISST framework These filters belong to the track-before-detect (TBD) technique [25], which is suitable for low signal-to-noise ratio (SNR) applications, as it minimizes information loss by sidestepping the detection implementation [25,26,27,28,29]. In this paper, motivated by previous works on 2-D coherent processing, the multi-sensor MB filter is employed in the MsGBPR system for moving target detection. With the method in [14], the 2-D coherent integration gain of several even tens of seconds of echo data can still be achieved
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