Group sparsity techniques for data fusion of a passive MISO radar network

Abstract

Passive radar networks offer several advantages compared to standard single radar. For instance a target is illuminated from different aspect angles and this leads to an increase of the detection performance and higher position estimation accuracy. Beside these both improvements the network is able to cope with an increased number of targets and the range/Doppler resolution is enhanced. As the system does not emit any electromagnetic energy the operator has no hassle with a frequency license. A very promising constellation of such a passive radar network uses digital broadcast communication stations like DAB-T or DVB-T which forms a single frequency network. This paper describes a new approach for evaluating data from a passive Multiple-Input-Single-Output (MISO) radar network based on single frequency network (SFN) by employing group sparsity techniques known from Compressive Sensing. When real targets are huge airplanes, observed during their take-off and touchdown phase, the received echoes are spread out over several range/Doppler cells. The proposed methods takes this into account.