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Generalized pseudo Bayesian algorithms for tracking of multiple model underwater maneuvering target

机译：用于跟踪多模型水下机动目标的广义伪贝叶斯算法

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摘要

The strength of Generalized Pseudo Bayesian (GPB) algorithms is exploited in the presented study to enhance the target tracking precision, effective model approximation and rapid convergence of multi-model maneuvering object tracking. The GPB methods are considered to be suitable for approximating systems whose dynamics follow discrete-time and fixed state Markov process. Underwater maneuvering target tracking problems are usually solved with nonlinear Bayesian algorithms, in which kinetics of object are associated with passive bearings using state-space modeling. Here accuracy and convergence of GPB methods based on Interacting Multiple Model Extended Kalman Filter (IMMEKF), Interacting Multiple Model Extended Kalman Smoother (IMMEKS), Interacting Multiple Model Unscented Kalman Filter (IMMUKF) and Interacting Multiple Model Unscented Kalman Smoother (IMMUKS) are efficiently analyzed for tracking of multimodel maneuvering target in complex ocean environment. Application of these algorithms is systematically presented for estimating the real-time state of a maneuvering object that follows a coordinated turn trajectory. Performance analysis of IMM Kalman filters and smoothers is done with variations in the standard deviation of white Gaussian measurement noise by following Bearings Only Tracking (BOT) phenomena. Least Mean Square Error (MSE) between approximated and the real position of maneuvering target in rectangular coordinates is calculated for analyzing the performance of filtering and smoothing techniques. Simulation results of the Monte Carlo runs validate the effectiveness of IMMEKS and IMMUKS over IMMEKF and IMMUKF for scenario of given framework. (C) 2020 Elsevier Ltd. All rights reserved.
机译：广义伪贝叶斯（GPB）算法的强度在呈现的研究中被利用，以提高目标跟踪精度，有效模型近似和多模型机动对象跟踪的快速收敛。 GPB方法被认为适用于近似系统，其动力学遵循离散时间和固定状态马尔可夫过程。水下的机动目标跟踪问题通常用非线性贝叶斯算法解决，其中物体动力学与使用状态空间建模的无源轴承相关联。这里有基于交互多模型扩展卡尔曼滤波器（ImmeKF）的GPB方法的准确性和收敛性，交互多模型扩展卡尔曼更顺畅（Immeks），交互多模型Unscented Kalman滤波器（Immukf）并与多种型号无创Kalman更加顺畅（Immuks）有效复杂海洋环境中多模型机动目标的跟踪分析。系统地呈现这些算法的应用，以估计遵循协调转弯轨迹的操纵对象的实时状态。 IMM卡尔曼过滤器和SmoOths的性能分析是通过以下轴承跟踪（机器人）现象来实现白色高斯测量噪声的标准偏差的变化。计算近似和在矩形坐标中操纵目标的实际位置之间的最小均方误差（MSE），用于分析过滤和平滑技术的性能。 Monte Carlo运行的仿真结果验证了Immekf和Immukf的Immeks和Immukf的有效性。 （c）2020 elestvier有限公司保留所有权利。

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