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由于海洋的水下环境非常复杂,利用声呐系统对运动的目标进行跟踪和定位,所测得的信息中必然含有误差,漏报和错误数据。此外,对机动的被测量目标,如鱼雷、潜艇、深潜器等,跟踪系统应具有很好的适应能为。为了提高跟踪系统的定位准确度和对机动目标的适应能力,本文将讨论一种自适应卡尔曼滤波。在水声测量中,由于水下环境变化复杂,难以掌握其变化规律,但是卡尔曼滤波要求事先建立滤波模型,这就限制了卡尔曼滤波在水声领域中的应用。为了克服这一困难,本文通过理论推导和模拟实验,提出了一种自适应卡尔曼滤波,并应用于鱼雷声靶定位系统中,取得了很好的效果。
Due to the complexity of the underwater environment of the ocean, the sonar system is used to track and locate the target of the movement. The measured information inevitably contains errors, omissions and erroneous data. In addition, the maneuvering target to be measured, such as torpedo, submarine, submarine, etc., the tracking system should have good adaptability. In order to improve the tracking system’s positioning accuracy and adaptability to maneuvering targets, this paper will discuss an adaptive Kalman filter. In the underwater acoustic measurement, due to the complex changes in the underwater environment, it is difficult to grasp the variation law. However, the Kalman filter requires the prior establishment of a filtering model, which limits the application of Kalman filtering in the field of underwater acoustic. In order to overcome this difficulty, this paper presents an adaptive Kalman filter through theoretical derivation and simulation experiments, and it is applied to the torpedo target positioning system and achieved good results.