Data Compression and Network Design for Signal Fusion based Distributed Radar
Distributed detection is a hot topic in the radar field.Signal fusion-based detection generally outperforms data fusion-based detection,but the communication cost is often huge.In order to tackle this problem,a data compression algorithm for dis-tributed non-coherent target detection based on signal fusion is presented in this paper.In the proposed algorithm,signal fusion with parallelized computation is employed to realize the decoupling of observations from different radars,and censored detection is used to eliminate locally unpowerful noise from transmitting,and then censored observations are compressed by requantization pro-cessing.Detection performance of the proposed algorithm is capable of approaching a signal fusion-based algorithm,but only needs a low communication cost like a data fusion-based detection.Numerical simulation results with four distributed radars indicate that compared with signal-fusion based detection algorithms,the communication bandwidth of the proposed compression algorithm can be reduced to 0.1%,whereas the signal-to-noise ratio loss is less than 0.7 dB.Accordingly,the radar network structure design problem is then discussed for distributed radar to support different application scenarios.
万方数据
维普
