基于TACKF的海面目标航向航速估计算法

A New Sea Target Course and Speed Estimation Algorithm Based on Truncated Adaptive Cubature Kalman Filter

航向航速是海面目标的重要特征,能准确估计出目标的航向航速对于海面目标的跟踪、识别和打击具有非常重要的意义。由于海面目标跟踪中易出现量测高精度、系统复杂强非线性等情况,导致传统非线性滤波器对海面目标航向航速的估计精度不高。此外,海面运动目标自身速度较慢,滤波器的稳态波动对海面目标的航速估计影响较大。针对上述问题,提出了一种基于截断的自适应容积卡尔曼滤波器(TACKF)的海面目标航向航速估计算法。仿真结果表明,所提出的TACKF算法较传统的非线性滤波算法有显著的性能提升,可以有效提高复杂环境下海面目标航向航速的估计精度。

Course and speed are the important features of the sea targets.Accurately acquiring target course and speed has important significance for target tracking,recognition and combat.However,the sea target course and speed estimation accuracy could get bad by use of the traditional nonlinear filters under the condition of high accuracy measurements and strong nonlinearity systems.In addition,due to the low speed of the sea targets,the steady-state fluctuation of the filter will lead to a great influence on the speed estimation.In order to solve these problems,a new sea target course and speed estimation algorithm based on the truncated adaptive cubature Kalman filter（TACKF）is proposed.Simulation results show that the proposed filtering algorithm has higher estimation accuracy and considerable performance improvement than the traditional nonlinear filtering methods.It can effectively improve the sea target course and speed estimation a ccuracy in complex situations.