AI 中文总结
研究多径环境中可移动天线无线传感系统的多目标检测问题，通过开发交叉稀疏马尔可夫混合先验推导目标位置后验概率，提出二维模糊函数分析角域传感性能，优化MA位置，算法低复杂度且性能与现有方法相当。
AI 中文摘要
本文研究了具有线性阵列的可移动天线（MA）无线传感系统中的多目标检测问题，明确考虑了直接视距（LoS）路径和一阶非视距（NLoS）路径。与传统固定位置天线阵列不同，MA通过自适应天线定位提供额外设计自由度来重新配置传播几何结构。首先开发交叉稀疏马尔可夫混合先验推导目标位置后验概率，利用LoS和NLoS路径结构相关性提高定位精度。基于此进一步分析角域传感性能，提出新二维模糊函数作为性能指标。接着优化MA位置抑制旁瓣电平、缩小主瓣宽度，虽问题高度非凸，但开发低复杂度基于Dykstra的投影梯度下降算法有效求解。最后仿真结果验证了所提模糊函数分析准确性，证明了先验模型和MA带来的性能提升，且所提算法在显著降低计算复杂度下性能与现有方法相当。
英文摘要
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.
Comments14 pages, 11 figures
