Compared with conventional two-step localization algorithms which are implemented by initially estimating localization parameters and then calculating the target position, the performance of direct position determination (DPD) algorithm is superior in terms of high estimation accuracy and strong resolution capability. DPD algorithms can make full use of the characteristics of signals to improve positioning performance. Using this localization mechanism as basis, this study develops a novel DPD algorithm that profits from the characteristics of orthogonal frequency division multiplexing (OFDM) signals based on the time and angle of arrival. First, the DPD optimization model is constructed based on maximum likelihood criterion and the characteristic of multiple carrier modulation technique of OFDM signals. Then, an extended subspace data fusion-based DPD algorithm is developed by constructing and decomposing the extended covariance matrices. The algorithm fuses the extended subspace data to estimate the target position without calculating the intermediate variables, which is less complex and more efficient. These properties can avoid the limitations of two-step algorithms. The last but important result is a derivation of the closed-form expression of Cramer-Rao lower bound (CRLB) on the position estimation variance for OFDM sources. Simulation results show that, the algorithm proposed in this paper outperforms existing DPD algorithms and conventional two-step localization algorithms. Especially under the condition of low signal-to-noise ratio, its localization accuracy is close to the CRLB.

This paper discusses the localization problem for strictly noncircular sources based on Doppler shifts and angle of arrival (AOA). The conventional location methods, called two-step methods, first extract measurement parameters from the signals, and then estimate the position from these parameters. Unlike the conventional two-step methods, direct position determination (DPD) methods can locate emitter in a single step without estimating intermediate parameters, thereby receiving higher location accuracy. However, existing DPD algorithms focusing on moving receivers remain few, and ignore exploiting the noncircular property of sources to enhance the localization accuracy. To solve these problems, this paper develops a decoupled maximum likelihood (ML) DPD algorithm for strictly noncircular sources based on Doppler shifts and AOA. First, we reconstruct the DPD optimization model by exploiting the noncircular property of sources and using Doppler shifts to meet the moving receivers application. Then, since the prescribed ML DPD algorithm for multiple sources will suffer huge computation load, a decoupled iterative idea is applied to it. Compared with the straightforward implementation of ML estimators, our method substantially reduces the computation complexity via decoupling the position of each emitter from the other emitters in each iteration. In addition, we derive the Cramér-Rao Bound for strictly noncircular sources, and prove it is lower than that of circular sources. Simulation results demonstrate that the proposed algorithm achieves convergence through only a few iterations, and receives superior performance than other location methods in the test scenarios.

针对基于时频差测量的无源跟踪中面临的非线性估计问题, 提出一种正交容积卡尔曼滤波跟踪算法. 该算法在容积卡尔曼滤波算法的基础上, 通过引入特定正交矩阵改进容积采样方法, 在高维状态估计下减小因采样产生的误差, 在没有增加计算量的前提下, 有效提高收敛速度及跟踪精度. 仿真结果表明, 在基于到达时差和到达频差的联合无源跟踪问题中, 与扩展卡尔曼滤波及容积卡尔曼滤波算法相比, 本文所提算法在跟踪性能上有明显提升.

在低信噪比条件下，基于时延和多普勒频移的直接定位算法在解决宽带信号源定位时精度较差.针对此问题，提出了一种基于分段信号相关累加的变速度多站联合直接定位算法，并给出了其克拉美罗下界.该算法利用多个变速度的观测站对信号进行接收，然后将同一观测站接收的目标信号分割成多段不重叠的短时信号，采用最大似然估计器，联合各段信号的时延、多普勒频移信息对目标进行直接定位.算法充分利用了观测信号包含的定位信息，并利用观测站速度的变化增加了目标位置信息，解决了分段信号联合估计带来的定位模糊问题，使定位精度进一步提高，增加了算法的实用性.仿真实验表明，较之传统直接定位算法，本文算法定位精度更高，尤其在低信噪比条件下更能逼近克拉美罗界.

The existing localization algorithms for coherently distributed (CD) sources fail to achieve the best localization performance because of position information loss and error accumulation. To solve this problem, this study proposed a novel direct position determination (DPD) algorithm that profits from the characteristics of noncircular signals. Based on the parameterization assumption of CD sources, the DPD localization model is initially constructed and an extended subspace data fusion‐based DPD algorithm is subsequently proposed by decomposing the extended covariance matrices, which are constructed by combining the characteristics of noncircular sources. The algorithm achieves a low complexity and a high efficiency by avoiding the calculation of the intermediate variables. Specifically, the closed‐form expression of the Cramer‐Rao lower bound for CD noncircular sources is also derived. Simulation results show that, compared with existing DPD algorithms, the proposed algorithm improves the multitarget localization capability and achieves high‐accuracy results.