非等声速信道下的循环神经网络机动目标跟踪模型

Tracking model of maneuvering target based on recurrent neural network in non-equal sound speed channel

针对水下非等声速信道声线弯曲导致传统滤波跟踪轨迹偏差的问题,提出一种循环神经网络的目标跟踪模型。该模型在缺乏声速剖面信息的情况下,通过数据驱动迭代训练,学习输入观测值与输出状态值之间的映射关系,实现目标位置和瞬态特征变化的精确获知。蒙特卡洛仿真实验结果表明,本文模型在非等声速信道下复杂机动场景中相较传统单模型滤波算法以及交互式多模型算法,水平距离跟踪精度分别提升4.06%,1.57%,深度估计精度分别提升0.87%,0.85%。本文模型相较于传统滤波方法具有更高的跟踪精度,并且能够在失配声速分布信道下进行迁移学习,提升模型在失配声场环境下的泛化性。

Aiming at the problem that the bending of the sound ray leads to the deviation of traditional filtering result in the non-equal sound speed channel,a tracking model based on recurrent neural network is proposed.In the absence of sound speed profile,the model learns the mapping relationship between input observations and output state values through data-driven iterative training to achieve the precise information of the change of the target position and transient characteristics.The results of the Monte Carlo simulation experiment show that the horizontal distance tracking error is decreased by 4.06%and 1.57%,and the depth estimation error is decreased by 0.87%and 0.85%,compared with the traditional single-model filtering algorithm and interactive multi-model algorithm in the complex maneuvering scene.The proposed model has higher tracking accuracy,and is able to carry out transfer learning under the mismatched sound speed channel,so as to improve the generalization of the model to the mismatched environment.