基于稀疏度阶数优化的杂波密度估计算法  

A clutter density estimation algorithm by optimized sparsity order

作　　者：郭云飞[1] 钱恒泽 GUO Yun-fei;QIAN Heng-ze(Automation School,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]杭州电子科技大学自动化学院,杭州310018

出　　处：《控制与决策》2020年第12期2923-2930,共8页Control and Decision

基　　金：浙江省自然科学基金重点项目(LZ20F010002);国家自然科学基金项目(61871166)。

摘　　要：针对杂波分布不均匀且密度未知的多目标跟踪问题,提出一种基于稀疏度阶数优化的杂波密度估计算法.首先,剔除在跟踪门内的潜在目标测量,获取杂波测量集;其次,从杂波测量集中构造"稀疏度阶数-超立方体容积"的样本,并利用支持向量回归机对样本拟合;再次,通过梯度法求得拟合曲线的极值点,实现稀疏度阶数在线优化;最后,将优化后的杂波稀疏度估计器嵌入高斯混合概率假设密度滤波器中,实现复杂杂波环境下目标状态与杂波密度联合估计.仿真结果验证了所提出算法的有效性.In order to address the problem of multi-target tracking by nonuniform clutter spatial distribution and unknown density,a clutter density estimator based on sparsity order optimization is proposed.Firstly,the clutter set is obtained by eliminating the potential target-originated measurements that fall within the validation gate.Then,the samples of"sparsity order-hypercube volume"are constructed from the clutter set and the corresponding fitting function is established by the support vector regression machine.Furthmore,the sparsity order is optimized online by finding the mininum using the gradient method.Finally,the clutter sparsity estimator is combined by the Gaussian mixture probability hypothesis density to estimate the clutter density and target state in complicated backgroud simultaneously.Simulation results show the effectiveness of the proposed algorithm.

关 键 词：杂波密度估计 多目标跟踪 稀疏度阶数优化 概率假设密度 支持向量回归机 梯度法 

分 类 号：TN953[电子电信—信号与信息处理]