The Recursive Form of Error Bound for Joint Detection and Estimation of Groups
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摘要: 在随机有限集框架下给出了当杂波和漏检存在时,群目标联合检 测与估计(Joint detection and estimation, JDE)误差界的递推形式. 首先,将多个群目标运动过程建模为一个多Bernoulli过程, 并采用连续个体目标数假设建模群目标观测似然函数; 其次,采用最优子模式 分配距离定义群目标JDE误差; 最终,利用信息不等式推导获得了建议的误差界. 仿真实验在不同杂波密度和检测概率场景下利用群势概率假设密度 和群势平衡多目标多Bernoulli滤波器对该误差界的有效性进行了验证.Abstract: Within the random finite set framework, this paper derives the recursive form of error bound for joint detection and estimation (JDE) of groups in the presence of clutter and missed detection. First, the dynamic of multiple groups is modeled as a multi-Bernoulli process and the group observation likelihood is modeled based on the concept of continuous individual target number. Then, the optimal sub-pattern assignment distance is used to define the JDE error of the groups. Finally, the proposed bound is derived in terms of information inequality. Given various clutter density and probabilities of detection, the effectiveness of the bound is verified through simulation by indicating the performance limitations of the cardinalized probability hypothesis density and cardinality balanced multi-target multi-Bernoulli filters for the groups.
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Key words:
- Error bounds /
- groups tracking /
- joint detection and estimation /
- random finite set
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