On the use of calibration sensors in source localization using TDOA and FDOA measurements

The accuracy of a source location estimate is very sensitive to the presence of the random noise in the known sensor positions. This paper investigates the use of calibration sensors, each of which is capable of broadcasting calibration signals to other sensors as well as receiving the signals from the source and other calibration sensors, to reduce the loss in the source localization accuracy due to uncertainties in sensor positions. We begin the study with deriving the Cramer–Rao lower bound (CRLB) for source localization using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements when a single calibration sensor is available. The obtained CRLB result is then extended to the more general case with multiple calibration sensors. The performance improvement due to the use of calibration sensors is established analytically. We then propose a closed-form algorithm that can explore efficiently the calibration sensors to improve the source localization accuracy when the sensor positions are subject to random errors. We prove analytically that the newly developed localization method attains the CRLB accuracy under some mild approximations. Simulations verify the theoretical developments.     

A Joint TDOA/FDOA Lo calization Algorithm Using Bi-iterative Method with Optimal Step Length

In order to improve the estimation accuracy of multi-station joint Time difference of arrive/ Frequency difference of arrive (TDOA/FDOA) location with Bi-Iterative method, a solution for the position of target with Gauss-Newton optimal step length is proposed in this paper. First, get the initial estimation of target based on Two-stage weighted least-squares (TSWLS) algorithm, and then alternately solve the position and velocity of the target with Bi-Iterative method. In this paper, Gauss-Newton method is applied to iteratively solve the target position, including the detailed equations of the descending direction and the optimal iterative step length in each iterative process. Simulations are carried out to examine the algorithm's performance by comparing it with TSWLS method and Gauss-Newton method regardless of the step length. The results show that when Gauss noise variance is small, the estimation accuracy is close to Cramer Rao lower bound (CRLB) and the proposed method performs better than the other two methods. In addition, because the model which includes the position and velocity of the observation station and the target is in line with the over-the-horizon reality scene in this paper, our research has certain practical value.     

基于FDOA多星定位精度综合分析

针对多星频差定位系统,建立了频差定位模型。利用矩阵理论和统计信号处理理论,推导了由于频差、卫星自定位和卫星速度测量误差所产生的定位误差几何稀度(GDOP)公式,对其理论分析和仿真表明:地球约束能显著改善定位精度;频差测量误差和卫星位置速度测量误差对系统性能均有影响,但前者影响更大。     

双星时差频差定位体制在频域混叠电磁环境下的应用

现代战场电磁环境复杂,不同类型电磁信号在时频域碰撞混叠,现有常规侦察手段对此类信号的定位应用受到严重制约。双星时差频差定位体制通过互模糊函数求解信号到达时差和频差,分离频域混叠信号并获得高达公里量级的定位精度,非常适合于复杂电磁环境下的应用。通过求解模糊函数的方法分析了时差频差体制对频域混叠信号的分离和定位能力,然后结合美航母编队及其电磁环境构成,探讨了这一能力在监视航母编队,尤其是跟踪识别编队个体成员的具体应用。仿真与分析结果证明了双星时差频差定位体制的优势,对于天基侦察系统的建设具有重要意义。     

基于静止、准静止双星TDOA／FDOA无源定位算法

针对由静止卫星、准静止卫星构成的无源定位系统，该文研究了利用到达时间差／到达频率差（TDOA／FDOA）联合对地球表面固定辐射源的无源定位算法。与迭代搜索法相比，该算法不需要初值，不用迭代运算，仅仅需要解4次多项式方程，因此该算法更有效。文中推导了定位方程的求解算法，分析了多项式方程的多根情况，并提出了相应的解决办法；最后进行了蒙特卡罗仿真，仿真结果表明该算法具有适应能力强、定位精度高的优点。     

一种基于最小二乘准则的方位-多普勒频差联合无源定位新方法

提出了一种方位-多普勒频差(DOA-FDOA)联合定位新算法,该算法多次应用最小二乘准则,借助目标航迹约束条件,并利用多普勒频差信息实现了对运动目标的可靠高精度定位,避免了直接求解非线性方程组。新算法的定位误差逼近CRLB,仿真结果验证了算法的可行性和有效性。     

双星系统对雷达无源定位的可行性分析

双星无源定位系统对地海面雷达辐射进行定位时,存在脉冲信号频差参数提取的模糊问题。提出了一种基于虚拟双星的双时差粗定位方法,可有效解决频差测量的模糊,再结合时差/频差联合定位方法,分析了双星系统对雷达辐射源无源定位的可行性,给出了一些有用的结论。     

基于正则化约束总体最小二乘的TDOA/FDOA无源定位方法

目前在基于到达时间差(TDOA)和到达频率差(FDOA)的多站无源定位模型中,噪声的干扰、接收站和目标位置的不合理分布以及接收站的个数均会对定位模型中的系数矩阵造成影响,因此在实际的求解过程中系数矩阵可能会出现病态的问题,这在很大程度上会对定位结果产生影响.为了进一步在系数矩阵出现病态的情况下确保定位精度,提出了一种基...     

基于四阶累积量的到达频率差估计算法

研究了基于四阶累积量的到达频率差估计算法,并给出了该算法的离散实现方法。该算法利用高阶累积量的特性,在相关的噪声环境下,对到达频率差作出了精确估计。仿真结果验证了该算法的有效性。     

A joint TDOA and FDOA estimation algorithm based on second-order cone programming

ABSTRACT

Considering the low estimation accuracy of the traditional interpolation method, this paper, on the basis of second-order cone programming (SOCP), proposes a novel joint time difference of arrival (TDOA) and frequency difference of arrival (FDOA) estimation interpolation method, which can attain the sub-sample precision. The proposed method uses several discrete samples produced by cross ambiguity function (CAF) to structure the convex optimization models with regard to the interpolation surface. Then, the SOCP is utilized to obtain the interpolation surface which matches the discrete surface of CAF well. Finally, the method achieves the precision superior to the traditional TDOA and FDOA estimation directly through the search for the maximum of the continuous approximate surface. This method decreases the computational load without loss of precision and can efficiently reduce the limitation of finite sampling interval and sampling time in estimation precision. Numerical simulations show that the method in this paper is efficient and outperforms existing interpolation algorithms about estimation precision.