CAJ | 학술논문

首先针对无源传感器目标跟踪中的非线性问题,将高斯-厄米特求积分规则运用于高斯混合概率假设密度滤波,提出一种求积分卡尔曼概率假设密度滤波. 其次,针对未知时变过程噪声,将基于极大后验估计原理的噪声估计器运用到概率假设密度滤波中, 同时依据目标状态一步预测与状态滤波结果之间的残差, 提出一种对滤波发散情况判断和抑制的算法. 最后通过无源传感器双站跟踪仿真表明:相较于已有的非线性高斯混合概率假设密度滤波,所提算法有更高的精度,并且在未知时变噪声环境中具有较好跟踪效果.
수선침대무원전감기목표근종중적비선성문제,장고사-액미특구적분규칙운용우고사혼합개솔가설밀도려파,제출일충구적분잡이만개솔가설밀도려파. 기차,침대미지시변과정조성,장기우겁대후험고계원리적조성고계기운용도개솔가설밀도려파중, 동시의거목표상태일보예측여상태려파결과지간적잔차, 제출일충대려파발산정황판단화억제적산법. 최후통과무원전감기쌍참근종방진표명:상교우이유적비선성고사혼합개솔가설밀도려파,소제산법유경고적정도,병차재미지시변조성배경중구유교호근종효과.
Firstly, to solve the nonlinear problem in the field of passive tracking, Gauss-Hermite quadrature is used to Gaussian mixture probability hypothesis density filter, and the quadrature Kalman probability hypothesis density filter was proposed. Then under the condition of unknown and time-varying process noise statistic, a noise statistic estimator based on maximum a posterior estimation was used in probability hypothesis density filter. According to the residual between predicted state and estimated state, an algorithm to judge and restrain filter divergence was proposed. Finally, simulations under the condition that two passive sensors tracking multiple targets show that:the proposed algorithm has better accuracy than existing algorithms, and achieve good effect when process noise statistic is unknown and time-varying.