联合时延与多普勒频率的直接定位改进算法
An Improved Direct Position Determination Algorithm with Combined Time Delay and Doppler

针对传统两步定位算法中测量参数不能保证与真实位置匹配,导致信息损失,产生定位误差的问题,提出了一种联合时延与多普勒频率的直接定位改进(IDPD)算法。首先采用高斯最大似然估计器,将从数据中提取目标位置信息的问题转化为求解包含发射源位置信息的厄尔米特矩阵的最大特征值问题;然后利用矩阵转置后特征值相同这一性质进行简化运算;最后通过二维地理网格搜索以获取发射源的位置估计。较之传统的直接定位(DPD)算法,IDPD算法对信号无要求,且利用极大特征值作为网格点的代价函数,抑制了噪声的影响,提高了定位精度。仿真结果表明,IDPD算法在低信噪比条件下对目标位置估计误差在102 m量级,与DPD算法相比,定位精度提高了30％以上,更加逼近克拉美罗下界。
An improved direction position determination (IDPD) algorithm with combined time delay and Doppler is proposed to solve the problem of positioning errors due to the information loss from the mismatch between measured parameters and actual location in traditional two steps localization algorithm. A Gaussian maximum likelihood estimator is employed to transform the problem of extracting target position information into the problem of calculating the maximum eigenvalue of a Hermite matrix which contains the emitter’s location information. Then, the property of eigenvalue keeping unchanged after matrix transpose is used to simplify the calculation. The emitter’s position is finally estimated through the search of two？？dimensional geo？？grid. Compared with the traditional direct position determination (DPD) algorithm, the IDPD does not need any special requirements for signals and restrains the effect of noise by taking the maximum eigenvalue as the cost function, therefore the position accuracy can be improved. Simulation results show that the performance of the proposed algorithm reaches a 102 meter magnitude even under low signal to noise ratio. Comparison with the DPD shows that the positioning accuracy of the IDPD increases by 30％ and is closer to the Cramér？？Rao lower bound