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首页> 外文会议>International Conference on Control, Automation and Information Sciences >Likelihood-based Sensor Fusion in Radar/Infrared System Using Distributed Particle Filter
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Likelihood-based Sensor Fusion in Radar/Infrared System Using Distributed Particle Filter

机译：雷达/红外系统中的基于似然的传感器融合，使用分布式粒子滤波器

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In this paper, the distributed data fusion problem in radar/infrared system which is composed of radar and infrared, is considered. Generally, the different dimensions of local measurements and the strong nonlinearity of infrared measurement equation are two major issues in radar/infrared system. For these issues, a parameterized likelihood-based distributed particle filter (P-L-DPF) algorithm is used, where the local likelihood function (rather than posterior or measurement) is regraded as the filtering results since the likelihood function can preserve the most original measurements information. Meantime, we approximate the likelihood function using polynomial expansion, and transmit polynomial coefficients to the fusion center, which efficiently reduces the transmission requirements. In the simulation, an example that a radar/infrared system tracks a moving target is given, the results show that the tracking performance of the P-L-DPF algorithm outperforms the posterior-based DPF (P-DPF) algorithm and is very close to the measurement-based centralized particle filter (M -CPF) algorithm.
机译：本文认为，考虑了由雷达和红外系统组成的雷达/红外系统中的分布式数据融合问题。通常，局部测量的不同尺寸和红外测量方程的强非线性是雷达/红外系统中的两个主要问题。对于这些问题，使用基于参数化的基于似然的分布式粒子滤波器（PL-DPF）算法，其中当地似然函数（而不是后后或测量）被后悔作为滤波结果，因为似然函数可以保留最原始的测量信息。同时，我们近似使用多项式扩展的似然函数，并将多项式系数传输到融合中心，这有效地降低了传输要求。在模拟中，给出了雷达/红外系统跟踪移动目标的示例，结果表明，PL-DPF算法的跟踪性能优于基于后的DPF（P-DPF）算法，并且非常接近基于测量的集中粒子滤波器（M-CPF）算法。

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