Distributed detection of sparse signals in radar networks via locally most powerful test

Abstract

In this chapter, the problem of the detection of sparse stochastic signals with radar sensor networks was studied. The BG distribution was imposed on the sparse signals and accordingly the problem of distributed detection of sparse signals was converted into the problem of close and one-sided hypothesis testing. The original LMPT detector was presented to detect sparse signals from high -precision measurements without any requirement of signal reconstruction. Simulation results demonstrated that to achieve the same detection performance, the original LMPT detector has a much lower computational burden than the DOMP-based detector. We further presented the quantized LMPT detector to solve the problem of the distributed detection of sparse signals with quantized measurements. To ensure the optimal detection performance, a method for the design of the quantizers at the local sensors was presented. Theoretical analysis of the performance of both original and quantized LMPT detectors was consistent with the simulation results. Simulation results also demonstrated that (1) the 1 -bit LMPT detector with 3.3L measurements approximately achieves the same detection performance as the original LMPT detector with L high -precision measurements; and (2) the detection performance of the 3 -bit LMPT detector is very close to that of the original LMPT detector.