一种针对恒模信号的运动单站直接定位算法

相比于常规的"测向+位置估计"两步定位模式,以Weiss等提出的目标直接位置确定(DPD)算法具有估计精度高、分辨能力强和无需数据关联等诸多优点。基于该类定位算法的基本理念,提出了一种利用单个运动天线阵列对恒模(即相位调制)信号的DPD算法。首先,依据最大似然(ML)准则以及恒模信号的恒包络特征,建立了相应的直接定位优化模型;接着,根据优化函数的代数特征提出了一种有效的多参量交替迭代算法,用以获得ML估计器的最优数值解;此外,推导了针对恒模信源的位置直接估计方差的克拉美罗界(CRB),从而为新算法的定位精度提供定量的理论下界。仿真实验表明:相比于已有的基于单个运动天线阵列的直接定位算法以及传统的两步定位算法,通过利用恒模信号的恒包络特征可以明显提高目标直接定位的估计精度。     

基于极大似然估计器的GNSS矢量跟踪算法

矢量跟踪是一种将全球导航卫星系统(GNSS)接收机的信号跟踪与导航解算融为一体的跟踪算法。传统的基于矢量延迟/频率锁定环(VDFLL)的跟踪算法普遍采用延迟锁定环(DLL)和锁频环(FLL)鉴别器计算伪距和伪距率偏差观测量,由于锁频环鉴别器存在近似误差和一步延迟效应,在高动态环境下容易造成环路失锁。从直接估计卫星信号特征参数的角度出发,基于中频信号模型构建码相位和载波多普勒的极大似然代价函数,采用非迭代估计算法得到各通道码相位和多普勒频移的估计偏差,转换为卡尔曼滤波器的观测矢量,提出一种基于极大似然估计器(MLE)的矢量跟踪算法。理论分析和仿真结果表明:新算法结合了极大似然估计和矢量跟踪的优点,克服了FLL的延迟效应,与基于VDFLL的矢量环路相比,高动态环境下的跟踪稳定性更好,可以对被遮挡的卫星保持持续的跟踪。     

面向再入目标跟踪的估计与辨识联合优化算法

准确的弹道系数辨识和精确的目标状态估计是再入目标高精度跟踪与高可靠识别的关键。一方面,状态估计的误差会造成模型参数(弹道系数)的辨识风险;另一方面,模型参数的辨识偏差又会导致模型失配从而降低目标状态的估计精度。因此,需要实现再入目标的状态估计和参数辨识的联合优化。针对再入目标弹道系数未知情形,提出了一种基于期望最大化(EM)框架并采用粒子滤波(PF)平滑器实现的PF-EM联合优化算法。在E步基于粒子平滑器得到目标状态的后验平滑估计,M步采用数值优化算法更新上一次迭代的弹道系数,通过E步和M步的不断迭代,以保证状态估计和弹道系数辨识的一致性。算法仿真对比表明:所提算法的状态估计和参数辨识精度均优于传统的状态增广算法。     

一种运动双站多脉冲无源定位算法

针对运动双站对已知高度的地面目标辐射源高精度定位问题,借鉴SAR成像原理和直接定位法思想,提出了一种利用空中两运动侦察站接收到目标多脉冲信号间的到达时间差(TDOA)和到达频率差(FDOA)信息的无源定位算法。采样时将观测时间分为快时间和慢时间,在快时间域估计时差,慢时间域估计频差,具体方法为:首先将两侦察站的采样信号在频域互相关,然后利用参考函数和广义Keystone变换消除距离单元徙动,再通过二维傅里叶变换得到目标位置点的TDOA和FDOA联合估计值,最后将时差频差联合估计值通过几何关系映射到目标的空间位置。仿真结果表明信噪比较高时逼近直接定位法的克拉美罗下限(CRLB),信噪比较低时仍然可以定位。此外,本文算法不仅计算量小,且适用于不可区分多目标辐射源定位问题,具有高精度和超分辨特性。     

基于四线性分解的双基地MIMO雷达的角度和多普勒频率联合估计

研究双基地多输入多输出(Multiple-input Multiple-output, MIMO)雷达中的角度和多普勒频率联合估计问题,提出了一种基于四线性分解(Quadrilinear Decomposition)的离开角(Direction of Departure, DOD)、波达角(Direction of Arrival, DOA)和多普勒频率的联合估计算法。通过对接收端匹配滤波器的输出进行延迟操作,得到符合四线性模型的数据,根据四线性交替最小二乘(Quadrilinear Alternating Least Squares, QALS)进行迭代,得到方向矩阵和多普勒频率矩阵的估计,进而得到角度和频率的估计。该算法无需谱峰搜索,无需知道反射系数,可实现角度和频率的自动配对,且能用于非均匀阵,该算法的角度估计性能优于多维ESPRIT方法和三线性交替最小二乘(Trilinear Alternating Least Squares, TALS)方法。论文分析了所提算法复杂度,并推导了克拉美-罗界(Cramer-Rao bound, CRB)。仿真结果验证了该算法的有效性。     

基于DDS技术的雷达多普勒频率模拟器

介绍了两种采用直接数字合成(DDS)技术的雷达目标多普勒频率模拟器,在26位数字控制字的控制下,模拟器可以相参地产生高分辨率的多普勒频率信号。     

基于3DT的空时自适应单脉冲参数估计算法

空时自适应处理(STAP)是机载预警雷达抑制杂波和干扰的一项关键技术,而多普勒三通道联合自适应处理(3DT)是适合工程实现的降维(RD)STAP方法。STAP目标检测后还需进一步估计目标的角度参数,因此将自适应单脉冲(AM)技术引入3DT,提出了一种高精度联合估计目标速度与方位空间角的空时自适应单脉冲算法。理论分析与仿真实验结果表明,当目标多普勒频率偏离检测多普勒单元中心频率时,该算法能同时减少目标多普勒跨越损失和空时导引矢量失配损失,进而提高输出信杂噪比(SCNR),改善目标测角精度。     

共面单脉冲拦截多目标问题

针对共面轨道的单脉冲拦截多目标问题,提出了一种求解拦截二、三目标的数值方法。对于单脉冲拦截二目标问题,当脉冲时刻或拦截某目标时刻给定时,通过Gibbs三矢量定轨算法,将该问题转化为求解只包含2个自由变量的非线性方程问题,通过Newton-Raphson迭代算法实现求解;进一步,考虑脉冲时刻自由,通过数值优化可得到燃料最优解。对于单脉冲拦截三目标问题,基于Lambert解将其转化为求解只包含2个自由变量的非线性方程问题,并通过数值迭代求解。两个问题求解时的迭代初值均通过Pork-Chop图法搜索得到。数值算例验证了提出方法的正确性及有效性。     

非理想均匀圆阵的波达方向和多普勒频率联合估计

针对均匀圆阵存在一般阵列误差 (如阵元的幅相误差和安装位置误差等 )的情况 ,提出了多个信号的波达方向和多普勒频率估计方法。直接利用均匀圆阵的阵列流形 ,采用波达矩阵法估计各个信号的多普勒频率。由一般阵列误差的统计特性构造加权矩阵 ,采用加权总体最小二乘法估计各个信号的波达方向。此方法具有鲁棒性强等特点。计算机仿真证明了此方法的有效性     

功率约束下低相关旁瓣的通导一体化波形研究

通信导航一体化是未来无线系统的发展趋势,可以更加充分地利用频谱资源。设计了一种新的一体化波形,在实现通信符号传递的同时,利用其进行时延测距进而实现相应的定位导航功能。一种联合的优化目标函数被提出,其中以星座图上的范数误差衡量通信性能,以自相关函数的加权积分旁瓣电平衡量波形的定时性能。结合一体化系统的实际,在问题求解时同时考虑了波形总功率约束和波形恒模约束,并采用变量交替迭代优化的算法进行迭代求解。最后利用数值仿真验证了算法的有效性。     

基于单个长基线干涉仪的运动单站直接定位

针对以往单站无源定位系统中采用的多通道干涉仪或阵列测向系统复杂、易受通道间幅相不一致性影响等缺点,提出了一种基于信号子空间分解的运动单站单个长基线干涉仪(LBI)直接定位(DPD)方法。该方法采用量子粒子群优化(QPSO)方法获得定位初值,再利用Newton迭代方法得到精估计值。仿真结果表明:增加观测器的机动性,可对辐射源实现快速无模糊的定位;定位性能在较高信噪比(SNR)下可接近定位误差的克拉美-罗下限(CRLB),在低信噪比下优于多通道干涉仪仅测角(BO)和长基线相位差变化率定位方法。     

基于单个长基线干涉仪的运动单站直接定位

 针对以往单站无源定位系统中采用的多通道干涉仪或阵列测向系统复杂、易受通道间幅相不一致性影响等缺点,提出了一种基于信号子空间分解的运动单站单个长基线干涉仪(LBI)直接定位(DPD)方法。该方法采用量子粒子群优化(QPSO)方法获得定位初值,再利用Newton迭代方法得到精估计值。仿真结果表明:增加观测器的机动性,可对辐射源实现快速无模糊的定位;定位性能在较高信噪比(SNR)下可接近定位误差的克拉美-罗下限(CRLB),在低信噪比下优于多通道干涉仪仅测角(BO)和长基线相位差变化率定位方法。     

直达与非直达环境中的多目标解耦直接定位方法

针对直达（LOS）与非直达（NLOS）环境中的定位问题,提出了一种波形已知条件下的单阵地多目标直接定位（DPD）算法。该算法针对发射时间已知和未知两种情况,利用多径信号到达角度与时延关于障碍物（或反射体）、观测站与目标位置参数的数学关系,建立了三维目标位置的最大似然（ML）函数,无需估计测量参数,避免了传统两步定位方法所需的非直达径识别与数据关联。为了克服多目标定位中的高维非线性优化问题,该算法利用独立波形信息将多目标定位解耦为对各个目标单独求解。通过对目标函数有效近似,算法在发射时间已知和未知两种情况下均仅需三维网格搜索,比相应的两步定位方法具有更低的计算量。此外,基于多径定位场景,推导了发射时间已知和未知两种情况下的位置估计克拉美罗界（CRB）。仿真结果表明：算法的定位性能能够逼近相应的克拉美罗界,比传统两步定位方法和子空间直接定位算法具有更高的定位精度。     

A direct position determination method with combined TDOA and FDOA based on particle filter

The localization of a stationary transmitter using moving receivers is considered. The original Direct Position Determination (DPD) methods, with combined Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA), do not perform well under low Signal-to-Noise Ratio (SNR), and worse still, the computation cost is difficult to accept when the computational capabilities are limited. To get better positioning performance, we present a new DPD algorithm that proves to be more computationally efficient and more precise for weak signals than the conventional approach. The algorithm partitions the signal received with the same receiver into multiple non-overlapping short-time signal segments, and then uses the TDOA, the FDOA and the coherency among the short-time signals to locate the target. The fast maximum likelihood estimation, one iterative method based on particle filter, is designed to solve the problem of high computation load. A secondary but important result is a derivation of closed-form expressions of the Cramer-Rao Lower Bound (CRLB). The simulation results show that the algorithm proposed in this paper outperforms the traditional DPD algorithms with more accurate results and higher computational efficiency, and especially at low SNR, it is more close to the CRLB.     

修正容积卡尔曼滤波数据域直接定位算法

为解决数据域直接定位（DPD）算法面临的计算压力,提高算法效率,提出一种基于修正的容积卡尔曼滤波（MCKF）的DPD算法。首先,融合各观测信号波达方向信息,利用子空间数据融合方法建立一种基于间接观测量的DPD滤波模型;然后,根据模型特点设计MCKF算法进行求解,解决间接观测量带来的噪声累积问题;最后,对算法计算量进行分析和对比,说明计算效率的提升。仿真结果表明,所提算法与基于最大似然遍历搜索和遗传算法的DPD算法相比,在相同的估计性能下,计算量下降明显,时效性显著提升,增加了算法实用价值。     

A closed-form solution for moving source localization using LBI changing rate of phase difference only

Due to the deficiencies in the conventional multiple-receiver localization systems based on direction of arrival(DOA) such as system complexity of interferometer or array and amplitude/phase unbalance between multiple receiving channels and constraint on antenna configuration,a new radiated source localization method using the changing rate of phase difference(CRPD)measured by a long baseline interferometer(LBI) only is studied. To solve the strictly nonlinear problem, a two-stage closed-form solution is proposed. In the first stage, the DOA and its changing rate are estimated from the CRPD of each observer by the pseudolinear least square(PLS) method,and then in the second stage, the source position and velocity are found by another PLS minimization. The bias of the algorithm caused by the correlation between the measurement matrix and the noise in the second stage is analyzed. To reduce this bias, an instrumental variable(IV) method is derived. A weighted IV estimator is given in order to reduce the estimation variance. The proposed method does not need any initial guess and the computation is small. The Cramer–Rao lower bound(CRLB) and mean square error(MSE) are also analyzed. Simulation results show that the proposed method can be close to the CRLB with moderate Gaussian measurement noise.     

A two-step weighted least-squares method for joint estimation of source and sensor locations: A general framework

It is well known that the Two-step Weighted Least-Squares(TWLS) is a widely used method for source localization and sensor position refinement. For this reason, we propose a unified framework of the TWLS method for joint estimation of multiple disjoint sources and sensor locations in this paper. Unlike some existing works, the presented method is based on more general measurement model, and therefore it can be applied to many different localization scenarios.Besides, it does not have the initialization and local convergence problem. The closed-form expression for the covariance matrix of the proposed TWLS estimator is also derived by exploiting the first-order perturbation analysis. Moreover, the estimation accuracy of the TWLS method is shown analytically to achieve the Cramér-Rao Bound(CRB) before the threshold effect takes place. The theoretical analysis is also performed in a common mathematical framework, rather than aiming at some specific signal metrics. Finally, two numerical experiments are performed to support the theoretical development in this paper.     

Sequential localization of a radiating source by Doppler-shiftedfrequency measurements

For a single omnidirectional sensor in contact with a constant velocity source that radiates a constant frequency tone, the measurements of the Doppler-shifted frequencies over time do not provide sufficient information to localize the source. Instead, only the parameters of source rest frequency, speed, and range relative to a sensor can be determined. However, if over time three or more sensors sequentially come in contact with the source, the source trajectory can then be specified. This is termed sequential localization. A two-dimensional grid search technique to find the Doppler parameters from measurements taken by a single sensor alone is presented. From these parameters, it is next shown how sequential localization is accomplished. Two simulation examples are provided to verify the development. The localization root mean squared errors (RMSEs) are found to be very close to the Cramer-Rao bound     

具有通信约束的分布式SOR多智能体轨迹估计算法

针对传统多智能体轨迹估计算法信息交换量大，计算量随群规模指数增长，可扩展性差等诸多不足，提出了一种基于超松弛迭代（SOR）的分布式多智能体轨迹估计算法，通过将最大似然（ML）准则下的轨迹估计转化为两级线性优化问题，并综合利用分布式超松弛迭代（Distributed SOR）和标记初始化方法，加快求解速度并简化信息交换流程，最终实现了多智能体位姿轨迹优化和协作定位。实验表明，所提的分布式方法能达到集中式算法的精度水平，在49个智能体规模条件下，位置估计误差小于0.15 m，姿态估计误差小于0.03°，且数据交换量仅到现有主流分布式方法DDF-SAM的0.06%，能很好用于大规模集群的场景。