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Tracking of manoeuvring targets using fuzzy information fusion filter

机译：使用模糊信息融合滤波器的机动目标跟踪

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The paper discusses the tracking of a manoeuvring target using fuzzy information fusion filters (FIFFs), based on bearing only measurements received from multiple sensors. While a number of tracking filters including the Kalman filters have been proposed and demonstrated successfully for tracking, the information filter has been reported to be capable of estimating the state of a target even without an initial estimate. However, the estimate is often seen to diverge after a short period. A FIFF has been shown to track small manoeuvres in target following a constant velocity model but is not effective when the target switches to a coordinated turn model. In this paper, the idea of using the FIFF for tracking manoeuvring target is proposed and demonstrated. The filter uses only the bearing measurements in tracking the target and also for detecting the manoeuvre. Using an adaptive turn rate model, the turning rate of the moving target is estimated from the range rate. Here, the range rate is also computed directly from bearing measurements. The detection of the onset of manoeuvre is on the basis of consistent change in the turning rate of the target. A number of different scenarios involving manoeuvring targets are given to demonstrate the efficacy of this approach.
机译：本文讨论了基于模糊信息融合滤波器（FIFF）的机动目标跟踪，该方法仅基于从多个传感器接收的测量结果。尽管已经提出并成功地证明了包括卡尔曼滤波器在内的许多跟踪滤波器以进行跟踪，但是据报道，即使没有初始估计，信息滤波器也能够估计目标的状态。但是，经常会在短时间后看到估计数的差异。 FIFF已显示遵循恒定速度模型跟踪目标中的小动作，但是当目标切换到协调转弯模型时无效。本文提出并证明了使用FIFF跟踪机动目标的想法。过滤器仅使用方位测量来跟踪目标并检测动作。使用自适应转弯速率模型，根据测距速率估算移动目标的转弯速率。在此，测距率也可以直接从轴承测量值中得出。操纵开始的检测是基于目标转动速度的一致变化。给出了涉及机动目标的许多不同场景，以证明这种方法的有效性。

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