Emitter selection criteria for passive multistatic synthetic aperture radar imaging

Stevens, Sean R.; Jackson, Julie Ann

doi:10.1049/iet-rsn.2014.0014

Cited by 11 publications

(4 citation statements)

References 29 publications

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“…Passive radar, also known as passive coherent location (PCL), utilizes existing noncooperative illuminators of opportunity (IOs) rather its own dedicated transmitter for target detection and estimation [1]- [6]. Such radars have a number of advantages compared with their conventional active counterparts.…”

Section: Introductionmentioning

confidence: 99%

Cramér–Rao Lower Bound Analysis for Stochastic Model Based Target Parameter Estimation in Multistatic Passive Radar With Direct-Path Interference

et al. 2019

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This paper addresses the problem of Cramér-Rao lower bound (CRLB) analysis for joint target location and velocity estimation in a multistatic passive radar system comprised of multiple noncooperative illuminators of opportunity (IOs) and multiple geographically separated receivers. Unlike other existing studies, special attention in this paper is paid to a more ubiquitous scenario, in which no reference channel exists in receiver networks. Besides, the situation where the measurements collected at the receivers are contaminated by the interference directly illuminated from the IOs is taken into account. Namely, each receive station simultaneously obtains direct-path interference (DPI) from all the IOs and echo signals reflected by the target. Furthermore, the IO waveform is modeled as a stochastic process in which samples of the unknown IO waveform are treated as a complex Gaussian sequence. Finally, the effects of multipath clutter on the signal model and CRLB are well analyzed. The numerical results are provided to prove that the joint CRLB is not only a function of the signal-to-noise ratio (SNR), DPI-to-noise ratio (DNR), and clutterto-noise ratio (CNR) but also associated with both IO waveform parameters and relative geographical distribution of the system.

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Section: Introductionmentioning

confidence: 99%

Cramér–Rao Lower Bound Analysis for Stochastic Model Based Target Parameter Estimation in Multistatic Passive Radar With Direct-Path Interference

et al. 2019

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“…For the short synthetic aperture time of this bistatic radar, GEOSAR on the small orbital inclination (≤5°) may be approximately considered a static situation, and only the receiver contributes to the all azimuth modulations [4,5]. The classical bistatic imaging algorithm cannot be employed to carry out this radar echo signal [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Bistatic Imaging Method for GEOSAR in the Strip Mode and UAVSAR in the Steering Beam Mode

Zhuo-qun

Sheng

et al. 2018

International Journal of Antennas and Propagation

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The bistatic configuration with a geosynchronous orbital SAR (GEOSAR) transmitter and unmanned aerial vehicle SAR (UAVSAR) receiver can continuously image in any dangerous and interesting district. In this paper, the new imaging method in the case with the smaller orbital inclination of geosynchronous earth orbit and the steering beam working mode of UAVSAR was mainly studied and analyzed. GEOSAR can be approximately expressed as a static state, and only the receiver provides all the Doppler information. UAVSAR works in the steering beam modes, such as spotlight, sliding spotlight, and TOPS (Terrain Observation by Progressive Scan) mode. The azimuth bandwidth increased by the steering beam causes an aliasing situation in the azimuth frequency domain. To solve this problem, the proposed imaging method corrects the azimuth frequency aliasing using the scaling transform and the bulk azimuth compression. Compared with the traditional imaging method, the simulation validates perfectly the effectiveness of the bistatic imaging algorithm.

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“…In [29,30], sensor selection was studied and applied to passive radar systems for the first time, where the optimal transmitter-receiver pair is selected to improve the target tracking performance of the whole system. Stevens et al present a multistatic criteria and a framework for selecting the best available subset of emitters for use in passive multistatic synthetic aperture radar (SAR) imaging [31].…”

Section: Introductionmentioning

confidence: 99%

“…In practical applications, a given set of receivers may only have N R narrowband receive channels to capture both direct path reference signals and target echo signals, which limits the maximum number of transmitters of opportunity to be selected [31]. To this end, this paper aims to investigate the optimal transmitter subset selection for target parameter estimation in FM-based passive radar network systems, where the passive radar networks are comprised of multiple transmitters of opportunity and multichannel receivers.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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Emitter selection criteria for passive multistatic synthetic aperture radar imaging

Cited by 11 publications

References 29 publications

Cramér–Rao Lower Bound Analysis for Stochastic Model Based Target Parameter Estimation in Multistatic Passive Radar With Direct-Path Interference

Cramér–Rao Lower Bound Analysis for Stochastic Model Based Target Parameter Estimation in Multistatic Passive Radar With Direct-Path Interference

A Bistatic Imaging Method for GEOSAR in the Strip Mode and UAVSAR in the Steering Beam Mode

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

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