Unbiased Least Square Estimation for Bearings-Only Target Tracking Based on Sequential Algorthm
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摘要: 测量方程的非线性是纯方位角跟踪研究的难点。为了解决传统伪线性估计结果存在严重有偏性这一问题, 提出了一种基于伪线性估计的无偏估计方法, 即在伪线性方程中利用约束最小二乘估计, 它不需要对初始值进行假设, 且收敛效果良好。采用序贯滤波方法对估计量作批处理滤波以减少运算量, 提高了运算效率。仿真结果表明, 利用该方法得到的估计量在高斯噪声下能很好地逼近理想状态, 且收敛速度较快, 比扩展卡尔曼滤波具有更好的跟踪精度及收敛速度, 在目标高速运动时仍能保持较好的跟踪效果。Abstract: Bearings-only target tracking attempts to obtain a target trajectory only based on bearings measurements. This problem is difficult because the measurement equations are nonlinearly related to the target parameters. The pseudolin-ear estimation provides a linearized measurements equation, however the estimation result is severely biased. In order to deal with this problem, this paper proposes an unbiased estimation method with good convergence property, in which constraint least squares minimization is applied to the pseudolinear estimation without assuming initial values. The se-quential filtering algorithm is employed for batch processing of the estimator, thus the estimation accuracy is improved and the computation is reduced. Simulation result shows that the proposed algorithm gets higher tracking accuracy and faster convergence speed than the extended Kalman filter, the obtained estimator approaches the perfect state in the condition of Gaussian noise rapidly and keeps steady when the target is moving at a high speed.
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