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Shifted Rayleigh filter: a new algorithm for bearings-only tracking

机译：位移瑞利滤波器：一种仅跟踪方位的新算法

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摘要

A new algorithm, the "shifted Rayleigh filter," is introduced for two- or three-dimensional bearings-only tracking problems. In common with other "moment matching" tracking algorithms such as the extended Kalman filter and its modern refinements, it approximates the prior conditional density of the target state by a normal density; the novel feature is that an exact calculation is then performed to update the conditional density in the light of the new measurement. The paper provides the theoretical justification of the algorithm. It also reports on simulations involving variants on two scenarios, which have been the basis of earlier comparative studies. The first is a "benign" scenario where the measurements are comparatively rich in range-related information; here the shifted Rayleigh filter is competitive with standard algorithms. The second is a more "extreme" scenario, involving multiple sensor platforms, high-dimensional models and noisy measurements; here the performance of the shifted Rayleigh filter matches the performance of a high-order bootstrap particle filter, while reducing the computational overhead by an order of magnitude.
机译：针对二维或三维纯方位跟踪问题，引入了一种新算法“移位瑞利滤波器”。与其他“矩匹配”跟踪算法（例如扩展的卡尔曼滤波器及其现代改进）相同，它以正常密度近似目标状态的先验条件密度；其新颖之处在于，根据新的测量结果，可以执行精确的计算来更新条件密度。本文提供了该算法的理论依据。它还报告了在两种情况下涉及变体的模拟，这些模拟已成为早期比较研究的基础。第一种是“良性”方案，其中的测量值相对包含与范围有关的信息。移位后的瑞利滤波器与标准算法相比具有竞争力。第二种是更“极端”的情况，涉及多个传感器平台，高维模型和噪声测量。在此，移位瑞利滤波器的性能与高阶自举粒子滤波器的性能相匹配，同时将计算开销减少了一个数量级。

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