Optimization of Particle CBMeMBer Filters for Hardware Implementation
Optimization of Particle CBMeMBer Filters for Hardware Implementation
复制标题
粒子 CBMeMBer 滤波器硬件实现的优化
DOI：
10.1109/tvt.2018.2853120
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发表时间：
2018-07
影响因子：
6.8
通讯作者：
Qin Zhongya
中科院分区：
文献类型：
--
作者：
Yang Chaoqun;Shi Zhiguo;Han Kuan;Zhang Jun Jason;Gu Yujie;Qin Zhongya
在硬件平台上实现粒子基数平衡多目标多伯努利（CBMeMBer）滤波器是实时多目标跟踪的一种很有前途的解决方案。然而，由于CBMeMBer滤波器中伯努利强度分量的时变数量与硬件平台中粒子数量有限之间存在矛盾，该解决方案难以实现。此外，实时硬件实现需要适合并行处理的重采样过程，而现有的并行重采样算法对该过程过于简化，导致估计性能下降。本文提出了一种粒子分配优化算法来克服上述矛盾，并提出了一种并行重采样算法来提高估计性能。数值实验证明了该算法在多目标跟踪中的有效性。
It is a promising solution for real-time multitarget tracking to implement particle cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filters in hardware platforms. However, this solution is difficult to materialize since there is a contradiction between the time-varying number of Bernoulli intensity components in CBMeMBer filters and the limited number of particles in hardware platforms. Moreover, real-time hardware implementation requires a resampling procedure that is suitable for parallel processing, while the existing parallel resampling algorithms oversimplify this procedure, resulting in estimation performance degradation. In this paper, we propose an optimization algorithm of particle allocation to overcome the above-mentioned contradiction, and a parallel resampling algorithm to improve the estimation performance. Numerical experiments demonstrate the effectiveness of the proposed algorithms in multitarget tracking.
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影响因子：
5.4
作者：
Mahler, Ronald P. S.;Ba-Tuong Vo;Ba-Ngu Vo
通讯作者：
Ba-Ngu Vo
影响因子：
6.8
作者：
Butakov, Vadim A.;Ioannou, Petros
通讯作者：
Ioannou, Petros
影响因子：
--
作者：
R. Mahler
通讯作者：
R. Mahler
DOI：
10.1109/icics.2015.7459929
发表时间：
2015-12
期刊：
2015 10th International Conference on Information, Communications and Signal Processing (ICICS)
影响因子：
--
作者：
Kuan Han;Zhongya Qin;Xiaoding Gao;Mengjun Jin;Zhiguo Shi
通讯作者：
Kuan Han;Zhongya Qin;Xiaoding Gao;Mengjun Jin;Zhiguo Shi
影响因子：
6.8
作者：
Zhou, Chengwei;Gu, Yujie;Shi, Zhiguo
通讯作者：
Shi, Zhiguo