Exploiting Movable Antennas for Multi-Target Wireless Sensing System in Multipath Environments

2026-07-25

AI Summary

This work addresses the challenge of limited multi-target wireless sensing accuracy in multipath environments by proposing a novel approach that exploits structural correlations between line-of-sight (LoS) and first-order non-line-of-sight (NLoS) propagation paths. The method employs a movable antenna array to adaptively reconfigure the propagation geometry and introduces a cross-sparse Markov mixture prior to jointly model target locations. A two-dimensional ambiguity function is devised for the first time to quantify sensing performance in the angular domain, and antenna placement is efficiently optimized via a Dykstra-projected gradient descent algorithm to suppress sidelobes and narrow the mainlobe. Simulations demonstrate that the proposed method significantly enhances localization accuracy while substantially reducing computational complexity, achieving performance comparable to or better than existing approaches.

Abstract

In this paper, we study the multi-target detection problem in a movable-antenna (MA)-enabled wireless sensing system with linear arrays, in which both the direct line-of-sight (LoS) paths and the first-order non-LoS (NLoS) paths are explicitly considered. Unlike conventional fixed-position antenna arrays, MAs provide additional design degrees of freedom by enabling adaptive antenna positioning to reconfigure the propagation geometry, offering great potential to enhance the sensing performance in complex multi-target multipath scenarios. Under this setup, we first develop a cross sparsity Markov mixture prior to derive the posterior probabilities of target locations, in which the structural correlation between the LoS and NLoS paths is effectively exploited to enhance the location estimation accuracy. Based on the derived posterior probabilities, we further analyze the angular-domain sensing performance for multi-target detection by proposing a new two-dimensional (2D) ambiguity function as the performance metric. Next, we optimize the MA positions to suppress the sidelobe levels and narrow the mainlobe width of the proposed ambiguity function. Although the resulting problem is highly non-convex, we develop a low-complexity Dykstra-based projected gradient descent algorithm to solve it efficiently. Finally, simulation results verify the accuracy of the proposed ambiguity function analysis, demonstrate the substantial performance gains enabled by the proposed prior model and MAs, and show that the proposed algorithm achieves performance comparable to existing methods with significantly lower computational complexity.

Problem

• multi-target detection
• movable antennas
• multipath environments
• wireless sensing
• NLoS paths

Innovation

• movable antennas
• multi-target sensing
• multipath exploitation
• cross sparsity prior
• 2D ambiguity function