Passive Detection in Multi-Static ISAC Systems: Performance Analysis and Joint Beamforming Optimization

Fuente: arXiv
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Autores principales: He, Renjie, Wang, Yiqiu, Tao, Meixia, Sun, Shu
Formato: Preprint
Publicado: 2025
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author He, Renjie
Wang, Yiqiu
Tao, Meixia
Sun, Shu
author_facet He, Renjie
Wang, Yiqiu
Tao, Meixia
Sun, Shu
contents This paper investigates the passive detection problem in multi-static integrated sensing and communication (ISAC) systems, where multiple sensing receivers (SRs) jointly detect a target using random unknown communication signals transmitted by a collaborative base station. Unlike traditional active detection, the considered passive detection does not require complete prior knowledge of the transmitted communication signals at each SR. First, we derive a generalized likelihood ratio test detector and conduct an asymptotic analysis of the detection statistic under the large-sample regime. We examine how the signal-to-noise ratios (SNRs) of the target paths and direct paths influence the detection performance. Then, we propose two joint transmit beamforming designs based on the analyses. In the first design, the asymptotic detection probability is maximized while satisfying the signal-to-interference-plus-noise ratio requirement for each communication user under the total transmit power constraint. Given the non-convex nature of the problem, we develop an alternating optimization algorithm based on the quadratic transform and semi-definite relaxation. The second design adopts a heuristic approach that aims to maximize the target energy, subject to a minimum SNR threshold on the direct path, and offers lower computational complexity. Numerical results validate the asymptotic analysis and demonstrate the superiority of the proposed beamforming designs in balancing passive detection performance and communication quality. This work highlights the promise of target detection using unknown communication data signals in multi-static ISAC systems.
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id arxiv_https___arxiv_org_abs_2506_07019
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publishDate 2025
record_format arxiv
spellingShingle Passive Detection in Multi-Static ISAC Systems: Performance Analysis and Joint Beamforming Optimization
He, Renjie
Wang, Yiqiu
Tao, Meixia
Sun, Shu
Information Theory
Signal Processing
This paper investigates the passive detection problem in multi-static integrated sensing and communication (ISAC) systems, where multiple sensing receivers (SRs) jointly detect a target using random unknown communication signals transmitted by a collaborative base station. Unlike traditional active detection, the considered passive detection does not require complete prior knowledge of the transmitted communication signals at each SR. First, we derive a generalized likelihood ratio test detector and conduct an asymptotic analysis of the detection statistic under the large-sample regime. We examine how the signal-to-noise ratios (SNRs) of the target paths and direct paths influence the detection performance. Then, we propose two joint transmit beamforming designs based on the analyses. In the first design, the asymptotic detection probability is maximized while satisfying the signal-to-interference-plus-noise ratio requirement for each communication user under the total transmit power constraint. Given the non-convex nature of the problem, we develop an alternating optimization algorithm based on the quadratic transform and semi-definite relaxation. The second design adopts a heuristic approach that aims to maximize the target energy, subject to a minimum SNR threshold on the direct path, and offers lower computational complexity. Numerical results validate the asymptotic analysis and demonstrate the superiority of the proposed beamforming designs in balancing passive detection performance and communication quality. This work highlights the promise of target detection using unknown communication data signals in multi-static ISAC systems.
title Passive Detection in Multi-Static ISAC Systems: Performance Analysis and Joint Beamforming Optimization
topic Information Theory
Signal Processing
url https://arxiv.org/abs/2506.07019