Non‐cooperative target recognition in multistatic radar systems

Stinco, Pietro; Greco, Maria; Gini, Fulvio; Manna, Marco

doi:10.1049/iet-rsn.2013.0063

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…1, at low-cost. As a multistatic system, it also has increased resilience to clutter, and multiple looks at a target for an improved chance of correct classification [8]. Furthermore, using a dual-frequency design, OTAD avoids the problem of direct signal interference usually associated with passive bistatic nodes [9].…”

Section: Over-the-air Derampingmentioning

confidence: 99%

Over-the-air deramping for multistatic perimeter surveillance

Ash

Ritchie

Brennan

et al. 2015

2015 IEEE Radar Conference

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This paper explores the use of an over-the-air deramping (OTAD) system as a solution for perimeter surveillance. Over-the-air deramping is a technique for wirelessly synchronising distributed passive FMCW radar nodes to a dual-frequency master FMCW transmitter node. Such a system gives the benefits of simultaneous monostatic and multistatic measurements for improved clutter resilience, and multiple looks at a target for reduced susceptibility to signal fading due to target scintillation. To prove the latter, a simultaneous monostatic and OTAD bistatic node were set up with a 5 m baseline, and a walking person was measured. The results show that signal fading occurs in both the monostatic and the bistatic node, but rarely at the same time. Hence, combining the measurements from the two nodes gives a consistent response from the target. This demonstrates the benefit of an OTAD system for a robust perimeter surveillance system.

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Section: Over-the-air Derampingmentioning

confidence: 99%

Over-the-air deramping for multistatic perimeter surveillance

Ash

Ritchie

Brennan

et al. 2015

2015 IEEE Radar Conference

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“…While the addition of antennas to form co-located Multiple Input/Multiple Output (MIMO) radars only enables the estimation of the targets' location vectors, multistatic radars with widely separated TX and RX nodes studied in this paper provide a complete estimation of all target parameters (location and velocity vectors). Moreover, multistatic radars provide spatial diversity by viewing targets from different angles, thereby enhancing the robustness of estimation [1,2,3] and helping target tracking [4], and recognition [5].…”

Section: Introductionmentioning

confidence: 99%

Sparsity-Driven Moving Target Detection in Distributed Multistatic FMCW Radars

Carnieres¹,

Feuillen²,

Jacques³

et al. 2019

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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We investigate the problem of sparse target detection from widely distributed multistatic Frequency Modulated Continuous Wave (FMCW) radar systems (using chirp modulation). Unlike previous strategies (e.g., developed for FMCW or distributed multistatic radars), we propose a generic framework that scales well in terms of computational complexity for high-resolution space-velocity grid. Our approach assumes that (i) the target signal is sparse in a discrete space-velocity domain, hence allowing for non-static target detection, and (ii) the resulting multiple baseband radar signals share a common support. By simplifying the representation of the FMCW radar signals, we propose a versatile scheme balancing complexity and detection accuracy. In particular, we design a low-complexity, factorized alternative for the Matching Pursuit algorithm leveraging this simplified model, as well as an iterative methodology to compensate for the errors caused by the model simplifications. Extensive Monte-Carlo simulations of a K-band radar system show that our method achieves a fast estimation of moving target's parameters on dense grids, with controllable accuracy, and reaching state-of-the-art performances compared to previous sparsity-driven approaches.

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“…Recently, several identification schemes using BS‐HRRPs have been proposed [5–8]. In [5], the SCs were extracted from the MS and BS radar signals using the one‐dimensional (1D) fast Fourier transform (FFT)‐based CLEAN algorithm.…”

Section: Introductionmentioning

confidence: 99%