Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multi...Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo(particle) technique. Target detection was accomplished through the estimation of target's existence probability, and the estimate of target state was obtained by combining the outputs of modeldependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter.展开更多
文摘研究了EBPSK(extended binary phase shift keying)调制脉冲雷达信号的探测性能.首先分析了该雷达系统信号在几种情况下基于CFAR(constant false alarm rate)的目标检测概率,表明在未知目标距离情况下,EBPSK调制雷达信号的探测性能优于传统LFM雷达信号的探测性能,在短波信道中,EBPSK脉冲雷达信号不存在多普勒频扩.该信号的模糊函数是对称的,所以不存在距离-多普勒耦合效应.其次,EBPSK调制雷达系统的调制器灵活多变,可根据要求设置不同的调制参数以改变信号的测距精度与目标探测性能.最后,通过仿真实验验证了理论分析的合理性,在较大脉冲回波时延估计误差的前提下,EBPSK信号的脉冲压缩性能高于LFM的脉冲压缩性能,且获得相同的探测性能,当时延大于0.05μs时,EBSPK脉冲雷达信号所需SNR比LFM脉冲雷达信号要少;当时延为0.5μs时,EBSPK脉冲雷达信号所需SNR比LFM脉冲雷达信号少25 d B.
基金Projects(61002022,61471370)supported by the National Natural Science Foundation of China
文摘Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo(particle) technique. Target detection was accomplished through the estimation of target's existence probability, and the estimate of target state was obtained by combining the outputs of modeldependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter.
