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首页> 外文期刊>IFAC PapersOnLine >Pseudomeasurement Kalman filter in underwater target motion analysis & Integration of bearing-only and active-range measurement * * A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, IITP RAS, Moscow.
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Pseudomeasurement Kalman filter in underwater target motion analysis & Integration of bearing-only and active-range measurement * * A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, IITP RAS, Moscow.

机译：水下目标运动分析中的伪测量卡尔曼滤波器以及仅方位角和活动范围测量的集成 * * AA莫斯科IITP RAS俄罗斯科学院Kharkevich信息传输问题研究所。

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Target motion analysis of the underwater target tracking by the UUV (Unmanned underwater vehicle) usually based on the bearing-only observations including azimuth and elevation angles. However, low angular resolution of hydroacoustic sonars does not enough for the good qiality of tracking. Moreover, angular observations lead to nonlinear filtering such as Extended Kalman Filtering (EKF) which usually produce estimations with unknown bias and quadratic errors. Moreover, in bearing-only observations, as it was mentioned long ago, possible unobservability could take place, therefore, some special observer’s motion become necessary. Other filters like the particle or unscented ones need the additional computer resources and also could produce the tracking loss. At the same time the pseudomeasurements Kalman filtering (PKF) method which transforms the estimation problem to the linear one and gives the current coordinates estimation with almost same accuracy could be modified to evaluate the moving target coordinates and velocities without bias. Since PKF gives unbiased estimate for the motion and the quadratic error it provides the good means for integration of various measurements methods such as passive (bearing-only) and active (range) metering. Using this filtering approach the good quality of TMA for randomly moving target may be achieved.
机译：UUV（无人水下航行器）对水下目标进行跟踪的目标运动分析通常基于仅方位观测值，包括方位角和仰角。但是，水声声纳的低角分辨率不足以实现良好的跟踪质量。此外，角度观测会导致非线性滤波，例如扩展卡尔曼滤波（EKF），通常会产生带有未知偏差和二次误差的估计。此外，如前所述，在仅方位观测中，可能会出现不可观察性，因此，必须有一些特殊的观察者动作。其他过滤器（例如粒子过滤器或无气味的过滤器）需要额外的计算机资源，并且还可能产生跟踪损失。同时可以修改伪测量卡尔曼滤波（PKF）方法，将估计问题转化为线性问题，并以几乎相同的精度给出当前坐标估计，以评估运动目标坐标和速度而不会产生偏差。由于PKF给出了运动和二次误差的无偏估计，因此它为集成各种测量方法（如无源（仅轴承）和有源（范围）计量）提供了很好的手段。使用这种滤波方法，可以实现针对随机移动目标的高质量TMA。

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