Transmitter Subset Selection in FM-Based Passive Radar Networks for Joint Target Parameter Estimation

Abstract

Passive radar network systems utilize multiple transmitters of opportunity and multichannel receivers to offer remarkable performance improvement due to the advantage of signal and spatial diversities. The frequency modulation (FM) commercial radio signals have become attractive for passive radar applications owing to their wide-spread availability and the favourable Doppler resolution. In this paper, two transmitter subset selection schemes, balancing the trade-off between target parameter estimation accuracy and infrastructure utilization, are proposed for FM-based passive radar networks. In the first, the subset size of selected transmitters employed in the estimation process is minimized by effectively selecting a subset of transmitters such that the required target parameter estimation mean-square error (MSE) threshold is attained. In the second, an optimal subset of transmitters of a predetermined size κ is selected such that the estimation MSE is minimized. These problems are formulated as a knapsack problem (KP), where the coherent Cramér-Rao lower bound (CRLB) is used as a performance metric. Both transmitter subset selection schemes are tackled with greedy selection algorithms by successively selecting transmitters so as to minimize the performance gap between the CRLB and a predetermined MSE threshold or a predetermined subset size. Numerical simulations demonstrate that the problem of transmitter subset selection is not only a function of the transmitted waveforms but also of the relative geometry between the target and the passive radar network systems, which leads to reductions in both computational load and signal processing costs.

Modified Cramér‐Rao lower bounds for joint position and velocity estimation of a Rician target in OFDM‐based passive radar networks

“…In Samczynski et al [], the concept of passive radar using noncooperative pulse radars is proposed, and the experiment results are also provided. Furthermore, the authors in Shi et al [] propose two transmitter of opportunity subset selection strategies for frequency modulation (FM)‐based distributed passive radar network systems, which are formulated as knapsack problems and tackled with greedy selection approaches. Overall speaking, the previous studies have laid a solid foundation for the research of passive radar systems, and it should be mentioned that multiple signals of opportunity can be exploited as illuminators to remarkably improve the system performance.…”

Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment

“…It is shown that the target estimation accuracy will be increased with an increase in reflection coefficient, number of transmitter-receiver pairs, the choice of the transmitter-receiver pairs and duration time. Furthermore, the work in [28] proposes two transmitter of opportunity selection algorithms for FM-based passive radar network systems, which are formulated as knapsack problems (KPs) and tackled with greedy selection approaches. On the basis of the research mentioned above, almost all of previous works focus on stationary platforms.…”

Cramér‐Rao Lower Bounds for Joint Target Parameter Estimation in FM‐Based Distributed Passive Radar Network with Antenna Arrays

“…Additionally, the MCRLB evaluation of an orthogonal frequency division multiplexing-based passive radar network can be found in Filip and Shutin (2016) and Shi, Salous, et al, 2016b). Two transmitter of opportunity subset selection schemes for FM-based passive radar network systems are proposed in Shi, Wang, Sellathurai, and Zhou (2016), which are formulated as knapsack problems and tackled with greedy selection approaches. Xie et al (2017) investigate the problem of joint optimization of receiver placement and illuminator selection for a passive radar network.…”

“…Furthermore, a passive radar system will offer spatial and signal diversities when it is deployed in a multistatic architecture, which is able to enhance its detection and estimation performance. In Filip and Shutin (), Gogineni et al (), Javed et al (), Shi, Wang, and Zhou (), Shi, Wang, Sellathurai, and Zhou (), Shi et al (), and Xie et al (), the CRLB has been studied and applied to passive radar systems. The work in Shi, Wang, and Zhou () presents the CRLB analysis for the joint target position and velocity estimation in a frequency modulation (FM)‐based passive radar network, and it is indicated that more antennas mean better estimation performance.…”