一种新的超声速弱目标长时间相参积累算法

由于超声速弱目标的高速、小雷达散射截面特性,常规雷达难以对其进行有效检测与跟踪。针对该问题,文中提出了一种新的超声速弱目标长时间相参积累检测与估计算法。首先,给出了超声速弱目标回波信号模型,并分析了距离走动特性,同时通过构造线性频率相位因子,补偿了距离走动的影响;然后,进行匹配滤波和相参积累,来提高目标的检测性能,同时完成目标的参数估计;最后,通过仿真实验分析了该算法的检测和参数估计性能。理论推导和仿真结果表明,该算法的信噪比积累增益为脉压比和相参积累脉冲数的乘积。

A Novel Long Term Coherent Integration Algorithm for Supersonic Weak Targets

Because of the high velocity and small radar cross section(RCS) of the supersonic weak targets, traditional radar has poor detection performance on them. To solve the problem, a novel long term coherent integration algorithm for the supersonic weak targets is proposed. First the echo signal model of supersonic weak targets is presented, and the characteristic of range walk is analyzed. Based on this, the linear frequency phase factor is constructed to compensate the effect of the range walk. Then, the range compression and coherent integration are performed to detect the targets and the parameters are estimated. Finally, the performance of target detection and parameter estimation of the algorithm are analyzed by the simulation experiments, and theoretical derivation and simulation results suggest that the signal-to-noise ratio(SNR) integration gain of the algorithm is the product of pulse compression ratio and pulse number of coherent integration.