针对水下目标被动定位中传感器位置误差带来的定位精度不高的问题,提出了一种基于两步最小二乘的到达时间差波达方向(time difference of arrival-direction of arrival,TDOA-DOA)目标定位算法。首先,构建TDOA-DOA理想化无误差模型,并...针对水下目标被动定位中传感器位置误差带来的定位精度不高的问题,提出了一种基于两步最小二乘的到达时间差波达方向(time difference of arrival-direction of arrival,TDOA-DOA)目标定位算法。首先,构建TDOA-DOA理想化无误差模型,并利用最小二乘算法对目标位置进行粗估计。其次,考虑测量误差和传感器位置误差,构建目标定位误差和传感器位置的联合方程,并利用加权最小二乘求解。最后,利用目标定位误差对目标位置粗估计值进行修正,得到更精确的定位结果。仿真实验表明,所提算法可对目标位置和传感器位置进行联合估计,相较于已有算法具有更高的定位精度,更适用于传感器位置存在误差情况下的水下目标定位。展开更多
To improve the estimation accuracy,a novel time delay estimation(TDE)method based on the closed-form offset compensation is proposed.Firstly,we use the generalized cross-correlation with phase transform(GCC-PHAT)metho...To improve the estimation accuracy,a novel time delay estimation(TDE)method based on the closed-form offset compensation is proposed.Firstly,we use the generalized cross-correlation with phase transform(GCC-PHAT)method to obtain the initial TDE.Secondly,a signal model using normalized cross spectrum is established,and the noise subspace is extracted by eigenvalue decomposition(EVD)of covariance matrix.Using the orthogonal relation between the steering vector and the noise subspace,the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE.Finally,the offsets are compensated via simple least squares(LS).Compared to other state-of-the-art methods,the proposed method significantly reduces the computational complexity and achieves better estimation performance.Experiments on both simulation and real-world data verify the efficiency of the proposed approach.展开更多
The source location based on the hybrid time difference of arrival(TDOA)/frequency difference of arrival(FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA position...The source location based on the hybrid time difference of arrival(TDOA)/frequency difference of arrival(FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA positioning will greatly affect the accuracy of positioning. Using unmanned aerial vehicle(UAV) as base stations, by optimizing the trajectory of the UAV swarm, an optimal positioning configuration is formed to improve the accuracy of the target position and velocity estimation. In this paper, a hybrid TDOA/FDOA positioning model is first established, and the positioning accuracy of the hybrid TDOA/FDOA under different positioning configurations and different measurement errors is simulated by the geometric dilution of precision(GDOP) factor. Second, the Cramer-Rao lower bound(CRLB) matrix of hybrid TDOA/FDOA location under different moving states of the target is derived theoretically, the objective function of the track optimization is obtained, and the track of the UAV swarm is optimized in real time. The simulation results show that the track optimization effectively improves the accuracy of the target position and velocity estimation.展开更多
提出了一种基于到达时间(time of arrival,TOA)和到达时间差(time difference of arrival,TDOA)的空中运动平台对目标高精度三维定位的无源定位方法。该方法使用3个辅站信号到空中运动平台的TOA以及辅站位置确定空中运动平台自身的位置...提出了一种基于到达时间(time of arrival,TOA)和到达时间差(time difference of arrival,TDOA)的空中运动平台对目标高精度三维定位的无源定位方法。该方法使用3个辅站信号到空中运动平台的TOA以及辅站位置确定空中运动平台自身的位置,然后依据目标散射回波到达各个辅站与空中运动平台的TDOA确定目标的位置。分析了三维TDOA目标定位模糊产生的原因,提出了一种无模糊的高精度TDOA目标位置求解算法。仿真结果表明,该算法比经典的TDOA定位算法精度高,而且不存在定位模糊,从而验证了该空中运动平台对目标进行无源定位方法的有效性以及正确性。展开更多
本文针对Ho提出的基于TDOA(Time Difference of Arrival)与GROA(Gain Ratio of Arrival)信号源定位的代数闭式解,提出两种偏差消减方法.首先对其闭式解偏差进行了推导,然后给出BiasRed法与BiasSub法两种偏差消减算法,BiasSub法从Ho给出...本文针对Ho提出的基于TDOA(Time Difference of Arrival)与GROA(Gain Ratio of Arrival)信号源定位的代数闭式解,提出两种偏差消减方法.首先对其闭式解偏差进行了推导,然后给出BiasRed法与BiasSub法两种偏差消减算法,BiasSub法从Ho给出的解中直接减去期望偏差,BiasRed法通过分析误差表达方程并引入二次约束来提升定位估计精度;分析表明两种方法均可针对远距离信号源,在较小高斯误差情况下有效消减定位偏差,BiasRed法可将偏差降低到最大似然估计算法的水平;计算机仿真分析验证了所提算法的性能.展开更多
文摘针对水下目标被动定位中传感器位置误差带来的定位精度不高的问题,提出了一种基于两步最小二乘的到达时间差波达方向(time difference of arrival-direction of arrival,TDOA-DOA)目标定位算法。首先,构建TDOA-DOA理想化无误差模型,并利用最小二乘算法对目标位置进行粗估计。其次,考虑测量误差和传感器位置误差,构建目标定位误差和传感器位置的联合方程,并利用加权最小二乘求解。最后,利用目标定位误差对目标位置粗估计值进行修正,得到更精确的定位结果。仿真实验表明,所提算法可对目标位置和传感器位置进行联合估计,相较于已有算法具有更高的定位精度,更适用于传感器位置存在误差情况下的水下目标定位。
基金supported in part by National Key R&D Program of China under Grants 2020YFB1807602 and 2020YFB1807600National Science Foundation of China(61971217,61971218,61631020,61601167)+1 种基金the Fund of Sonar Technology Key Laboratory(Range estimation and location technology of passive target viamultiple array combination),Jiangsu Planned Projects for Postdoctoral Research Funds(2020Z013)China Postdoctoral Science Foundation(2020M681585).
文摘To improve the estimation accuracy,a novel time delay estimation(TDE)method based on the closed-form offset compensation is proposed.Firstly,we use the generalized cross-correlation with phase transform(GCC-PHAT)method to obtain the initial TDE.Secondly,a signal model using normalized cross spectrum is established,and the noise subspace is extracted by eigenvalue decomposition(EVD)of covariance matrix.Using the orthogonal relation between the steering vector and the noise subspace,the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE.Finally,the offsets are compensated via simple least squares(LS).Compared to other state-of-the-art methods,the proposed method significantly reduces the computational complexity and achieves better estimation performance.Experiments on both simulation and real-world data verify the efficiency of the proposed approach.
基金supported by the National Natural Science Foundation of China (61502522)Equipment Pre-Research Field Fund(JZX7Y20190253036101)+1 种基金Equipment Pre-Research Ministry of Education Joint Fund (6141A02033703)Hubei Provincial Natural Scie nce Foundation (2019CFC897)。
文摘The source location based on the hybrid time difference of arrival(TDOA)/frequency difference of arrival(FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA positioning will greatly affect the accuracy of positioning. Using unmanned aerial vehicle(UAV) as base stations, by optimizing the trajectory of the UAV swarm, an optimal positioning configuration is formed to improve the accuracy of the target position and velocity estimation. In this paper, a hybrid TDOA/FDOA positioning model is first established, and the positioning accuracy of the hybrid TDOA/FDOA under different positioning configurations and different measurement errors is simulated by the geometric dilution of precision(GDOP) factor. Second, the Cramer-Rao lower bound(CRLB) matrix of hybrid TDOA/FDOA location under different moving states of the target is derived theoretically, the objective function of the track optimization is obtained, and the track of the UAV swarm is optimized in real time. The simulation results show that the track optimization effectively improves the accuracy of the target position and velocity estimation.
文摘提出了一种基于到达时间(time of arrival,TOA)和到达时间差(time difference of arrival,TDOA)的空中运动平台对目标高精度三维定位的无源定位方法。该方法使用3个辅站信号到空中运动平台的TOA以及辅站位置确定空中运动平台自身的位置,然后依据目标散射回波到达各个辅站与空中运动平台的TDOA确定目标的位置。分析了三维TDOA目标定位模糊产生的原因,提出了一种无模糊的高精度TDOA目标位置求解算法。仿真结果表明,该算法比经典的TDOA定位算法精度高,而且不存在定位模糊,从而验证了该空中运动平台对目标进行无源定位方法的有效性以及正确性。
文摘本文针对Ho提出的基于TDOA(Time Difference of Arrival)与GROA(Gain Ratio of Arrival)信号源定位的代数闭式解,提出两种偏差消减方法.首先对其闭式解偏差进行了推导,然后给出BiasRed法与BiasSub法两种偏差消减算法,BiasSub法从Ho给出的解中直接减去期望偏差,BiasRed法通过分析误差表达方程并引入二次约束来提升定位估计精度;分析表明两种方法均可针对远距离信号源,在较小高斯误差情况下有效消减定位偏差,BiasRed法可将偏差降低到最大似然估计算法的水平;计算机仿真分析验证了所提算法的性能.
