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针对低信噪比微弱目标的实时检测与跟踪问题,提出了基于复似然比的粒子滤波改进算法。该算法弥补了传统粒子滤波利用幅度似然比计算粒子权重而忽略相位信息的缺陷,推导了基于复似然比的粒子权重表达式,从而更好地利用了目标原始信息。同时,基于幅度似然比的权重计算需要多次进行贝塞尔函数运算,而基于复似然比的权重计算只需进行一次贝塞尔函数运算,可以有效降低计算复杂度。仿真结果表明:改进算法不仅在检测与跟踪性能上优于传统粒子滤波算法,所需计算时间也明显降低。
Aiming at the real-time detection and tracking of weak signal with low signal-to-noise ratio, an improved particle filter based on complex likelihood ratio is proposed. This algorithm makes up for the defect that the traditional particle filter uses the Likelihood Ratio to calculate the particle weight and ignores the phase information, and derives the particle weight expression based on the complex likelihood ratio so as to make better use of the target original information. At the same time, the weight calculation based on the amplitude-to-likelihood ratio needs to carry out Bezier functions several times, while the weight calculation based on the complex likelihood ratio needs only one Bessel function operation, which can effectively reduce the computational complexity. The simulation results show that the improved algorithm not only outperforms the traditional particle filter in detection and tracking performance, but also significantly reduces the computation time.