基于TDOA与GROA的多运动站误差配准算法

Multiple Moving Observers Registration Algorithm Based on TDOA and GROA

针对多运动站无源定位过程中存在系统误差的问题,该文提出一种基于到达时差(TDOA)与到达增益比(GROA)最小二乘配准算法。该算法利用泰勒展开把非线性的量测方程线性化,利用最小二乘算法得到对目标状态和系统误差的联合估计,并考虑了量测噪声和站址误差的影响。同时推导了存在量测噪声、站址误差和系统误差时的克拉美罗下界(CRLB),并分析了系统误差对CRLB的影响。多种条件下的仿真表明,该算法对系统误差和目标状态的估计精度较高,说明了算法的有效性。

This paper proposes a least-squares registration algorithm using Time Differences Of Arrival （TDOA） and Gain Ratios Of Arrival （GROA） measurements to solve the problem of multiple moving observers passive localization under the influence of system error. The proposed algorithm linearizes the nonlinear measurement equation by Taylor expansion, executes least squares algorithm for the joint estimation of target state and system biases, and considers the influence of measurement noise and location errors of observers. Meanwhile Cram^r-Rao Lower Bound （CRLB） under the influence of measurement noise~ location errors of observers and system biases is derived, and the influence of system error on CRLB is analyzed. Simulations under several different conditions indicate the proposed algorithm is valid, which can effectively estimate the system biases and target state.