Analysis on UAV Sensing with Sparse Array in ISAC System

In integrated sensing and communication (ISAC) systems, the increasing demand for higher sensing resolution necessitates the use of more antenna elements and greater radio resource consumption when employing traditional uniform antenna arrays and uniform pilot placement. This paper addresses these challenges by leveraging sparse arrays in ISAC system design to reduce costs while maintaining sensing performance. Specifically, the application of sparse antenna arrays and sparse pilot placement is investigated in the context of device-free unmanned aerial vehicle (UAV) sensing. To accommodate the distinct characteristics of sparse patterns in the spatial, frequency and time domains, two types of sparse array signal processing algorithms are designed. These algorithms are based on subspace analysis or correlation in difference coarray (DCA) domain, and applied for direction of arrival (DoA) estimation with sparse antenna arrays or for distance/velocity estimation with sparse resource patterns. Additionally, to analyze the sensibility condition of multi-target sensing scenarios, an Uncorrelation Criterion associated with multi-target geometrical characteristics is proposed. Simulation results demonstrate that sparse arrays offer similar performance to full uniform arrays for single-target scenarios but provide only coarse sensing in multi-target scenarios.