Data Fusion in a Multi-Target Radar Sensor Network

In this paper, we consider the decision fusion of Rayleigh fluctuating targets in multi-radar sensor networks. Decision fusion and data fusion in wireless sensor networks (WSNs) has been widely studied in order to save energy. Radar system as a special sensor network, when implemented for battlefield surveillance, faces bandwidth constraint in real-time applications instead of energy restriction. In this work, we study the decision fusion rules of multiple fluctuating targets in multi-radar (MT-MR) sensor networks. The MT-MR decision fusion problem is modeled as a multi-input multi-output (MIMO) system. We assume that each radar makes binary decision for each target from the observation. We derive our MIMO fusion rules based on the target fluctuation model and compare against the optimal likelihood ratio method (LR), maximum ratio combiner (MRC) and equal gain combiner (EGC). Simulation results show that the MIMO fusion rules approach the optimal-LR and outperforms MRC and EGC at high signal to noise ratio (SNR)