Multi-maneuvering Acoustic Targets Tracking Algorithm Based on Virtual Extension of Single Acoustic Vector Sensor
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摘要: 为解决单声矢量传声器(Acoustic vector sensor, AVS)可跟踪声目标数目少、跟踪性能差的问题, 提出了基于AVS虚拟扩展的多机动声目标跟踪算法. 首先, 引入高阶累积量预处理过程并建立高阶似然函数, 不仅能够抑制高斯噪声、提高估计精度, 还可通过AVS的虚拟扩展增加可跟踪目标数目. 然后, 在边缘化
$\delta$ 广义标签多伯努利(Marginalized$\delta$ -generalized label multi-bernoulli, M$\delta$ -GLMB)滤波框架下, 提出了基于累积量的增广运动模型状态的M$\delta$ -GLMB (Cumulants-based augumented motion model state M$\delta$ -GLMB, Cum-AMMS-GLMB)算法. 算法引入多种运动模型, 并将表征不同模型的索引标号作为目标状态的增广参数, 通过各模型间的加权混合获取优于单一运动模型的跟踪性能. 除此之外, 算法的序贯蒙特卡洛(Sequential Monte Carlo, SMC)实现过程中, 依据高阶预处理获得的归一化空间谱拟合检测概率函数, 抑制了杂波向可用粒子扩展, 进一步增强了高似然区域的粒子. 最后, 推导了AVS目标跟踪的后验克拉美罗下界(Posterior cram$\acute{e}$ r-rao lower bound, PCRLB), 并通过仿真实验验证了算法的量测噪声抑制能力和声目标跟踪性能.-
关键词:
- 声矢量传声器 /
- 高阶累积量 /
- 虚拟扩展 /
- 广义标签多伯努利滤波 /
- 多目标跟踪
Abstract: To solve the problem of poor performance and fewer trackable targets in the multi-targets tracking of acoustic vector sensor (AVS) in multi-target tracking, a multi-maneuvering acoustic targets tracking algorithm based on virtual extension of single AVS is proposed. First, the higher-order cumulants processing method is introduced to establish a higher-order likelihood function, which can not only improve the estimation accuracy by suppressing the Gaussian noise, but also increase the number of estimable targets by virtually extending the AVS. Then, under the marginalized$\delta$ -generalized label multi-bernoulli (M$\delta$ -GLMB) framework, a cumulants-based augmented motion model state M$\delta$ -GLMB (Cum-AMMS-GLMB) algorithm is proposed. The algorithm introduces multiple models, and uses the model index labels that distinguish different motion models as an augmented parameter for the target state, and obtains a better tracking performance than a single motion model through weighted mixing of the updated states of each model. In addition, in the sequential Monte Carlo (SMC) implementation of the algorithm, the detection probability function is fitted based on the normalized spatial spectrum obtained by higher-order cumulants preprocessing can suppress the diffusion of clutter to the available particles, and further enhance the particles in the high-likelihood region. Finally, the posterior cram$\acute{e}$ r-rao lower bound (PCRLB) for targets tracking of single AVS is derived, and the performance of measurement noise suppression and acoustic targets tracking is verified by simulation experiments. -
图 1 不同信噪比下的归一化高阶似然函数示例
Fig. 1 Example of normalized higher-order spatial spectrum under different SNR
图 3 Cum-AMMS-GLMB算法的多声目标跟踪结果
Fig. 3 Multiple acoustic target tracking results of Cum-AMMS-GLMB algorithm
图 4 双声目标情况下不同算法的估计结果
Fig. 4 The estimation results of different algorithms in the case of two acoustic targets
图 5 不同信噪比下PCRLB和各算法的RMSE估计结果
Fig. 5 PCRLB and RMSE estimation results of each algorithm under different SNR
图 6 不同
$\sigma_w$ 下PCRLB和各算法的RMSE估计结果Fig. 6 PCRLB and RMSE estimation results of each algorithm under different
$\sigma_w$ 表 1 各时间段对应的运动模型
Table 1 Movement model corresponding to each time period
时间 1~15 s 16~30 s 31~40 s 41~50 s 运动模型 CV模型 CA模型
$0.1^\circ/{\rm s}^2$CT模型
$ \omega=-2\pi/180$CV模型 
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表 2 声目标的运动状态和幸存时间
Table 2 Motion state and survival time of acoustic targets
声目标 初始状态 存在时间 目标1 $\mathrm{DOA}:\ \{30^\circ,30^\circ\}$, 速度$:\ \{1^\circ/{\rm s},0.05^\circ/{\rm s}\}$ 1~50 s 目标2 $\mathrm{DOA}:\ \{300^\circ,80^\circ\}$, 速度$:\ \{-1^\circ/{\rm s},1^\circ/{\rm s}\}$ 1~50 s 目标3 $\mathrm{DOA}:\ \{200^\circ,100^\circ\}$, 速度$:\ \{0^\circ/{\rm s},0.1^\circ/{\rm s}\}$ 10~20 s 目标4 $\mathrm{DOA}:\ \{150^\circ,40^\circ\}$, 速度$:\ \{1^\circ/{\rm s},0^\circ/{\rm s}\}$ 1~50 s 目标5 $\mathrm{DOA}:\ \{90^\circ,80^\circ\}$, 速度$:\ \{1^\circ/{\rm s},0.5^\circ/{\rm s}\}$ 25~50 s
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