基于随机有限集多目标密度的异构多传感器融合
Heterogeneous Multi-sensor Fusion with Random Finite Set Multi-object Densities
摘要：
研究了基于密度的随机有限集（RFS）型多目标密度（MOD）多传感器协同融合。现有的融合方法使用标量权重来表征局部MOD之间的相对信息置信度，并且以这种方式，每个局部MOD对融合的全局MOD的贡献部分可以通过调整这些权重来调整。我们的分析表明，使用标量系数的融合机制可以被过度简化的实际情况下，由于传感器能力的不完善和监视环境的各种影响，MOD的信息置信度是复杂的，通常是空间变化的。因此，当这些标量权重不能反映实际情况时，可以观察到严重的融合性能下降。我们为解决这一问题作出两项贡献。首先，我们提出了一种新的异构融合方法来执行本地RFS MOD之间的信息平均。通过将每个局部MOD分解为若干个较小的子MOD，将复杂的融合问题转化为一个可并行化的多簇融合问题。其次，由于所提出的融合策略是一个通用的过程，没有任何特定的模型假设，我们进一步推导出详细的异构融合方程，与集中式网络架构，概率假设密度（PHD）过滤器和多伯努利（MB）过滤器。提出的融合算法的高斯混合实现。各种数值实验的目的是证明所提出的融合方法的有效性。
英文摘要：
This paper addresses the density based multi-sensor cooperative fusion using random finite set (RFS) type multi-object densities (MODs). Existing fusion methods use scalar weights to characterize the relative information confidence among the local MODs, and in this way the portion of contribution of each local MOD to the fused global MOD can be tuned via adjusting these weights. Our analysis shows that the fusion mechanism of using a scalar coefficient can be oversimplified for practical scenarios, as the information confidence of an MOD is complex and usually space-varying due to the imperfection of sensor ability and the various impacts from surveillance environment. Consequently, severe fusion performance degradation can be observed when these scalar weights fail to reflect the actual situation. We make two contributions towards addressing this problem. Firstly, we propose a novel heterogeneous fusion method to perform the information averaging among local RFS MODs. By factorizing each local MODs into a number of smaller size sub-MODs, it can transform the original complicated fusion problem into a much easier parallelizable multi-cluster fusion problem. Secondly, as the proposed fusion strategy is a general procedure without any particular model assumptions, we further derive the detailed heterogeneous fusion equations, with centralized network architecture, for both the probability hypothesis density (PHD) filter and the multi-Bernoulli (MB) filter. The Gaussian mixture implementations of the proposed fusion algorithms are also presented. Various numerical experiments are designed to demonstrate the efficacy of the proposed fusion methods.
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