基于最小二乘融合估计的双星时频差定位

针对单参考站条件下具有测量误差和星历误差时定位精度不高的特点,提出了一种基于多次观测数据的最小二乘融合估计定位算法,该算法无需增加观测条件即可有效提高辐射源定位精度.分析了测量误差、星历误差对单参考站单次定位及融合定位精度的影响,推导了测量误差、星历误差对定位误差的传递公式,提出了含星历误差影响的最小二乘融合估计加权算法.通过Monte-Carlo仿真验证了误差分析结果和定位算法,并比较了加权最小二乘估计定位和单次定位的性能.仿真试验表明:在相同观测精度条件下,加权最小二乘融合定位可极大地提高辐射源定位精度,最大提高10倍以上.     

基于约束最小二乘的近空间雷达网定位算法

该文研究了一种基于归一化约束最小二乘的近空间雷达网定位算法。首先将距离与角度信息的非线性方程转换为线性方程,通过一阶Taylor展开分析了噪声对线性方程的影响,然后将定位问题转化为归一化约束总体最小二乘问题,并通过Lagrange函数将其转换为无约束的优化问题,根据定位均方误差最小原则选取加权因子得到定位解,最后进行了定位误差分析,仿真结果表明了该算法的有效性。     

基于DV-Hop算法的无线传感器网络定位精度优化

在无线传感器网络定位领域,DV-Hop算法因其实现简单得以广泛使用.针对DV-Hop算法定位误差较大的问题,提出一种基于DV-Hop多通信半径的加权定位算法.该算法利用多通信半径并通过引入修正因子细化和优化跳数,利用最小均方误差准则和加权方式修正平均跳距,并利用加权最小二乘法估算未知节点坐标.通过仿真得出所提算法在相同实验条件下的定位精度较DV-Hop算法提升约60.5％,相较于双通信半径优化算法和3-DV-Hop算法分别提升约36.4％和13.8％.     

一种基于最小化广义Rayleigh商的无源定位算法研究

 为改善只测角无源定位的性能,提出了一种基于最小化广义Rayleigh商的无源定位算法.该算法利用扰动观测矩阵和扰动观测向量的乘性结构,将约束总体最小二乘问题转化为最小化广义Rayleigh商问题,从而只需对一对矩阵束进行广义特征值分解即可求得全局最优定位解.仿真结果表明所提算法性能稳健且计算量较小,定位收敛精度逼近克拉美罗限(CRLB),远优于最小二乘(LS)算法和总体最小二乘(TLS)算法,实用性强.     

观测站有位置误差的多维标度时频差定位算法

定位精度对观测站位置误差很敏感。论文针对观测站位置和速度有误差的情况,提出了一种加权多维标度时频差定位算法。该算法利用了多维标度定位通过特征结构和维度信息抑制噪声的优势,它将定位残差表示成测量误差和观测站位置误差的线性形式,然后通过加权最小二乘给出了目标位置和速度估计的闭式解。仿真结果表明,在小的测量误差和观测站位置误差时,该算法对目标位置和速度的估计能够达到克拉美劳下界。与两步加权最小二乘和约束总体最小二乘算法相比,该算法在测量噪声和观测站位置误差较大时有更高的定位精度。      

非视距和未知传输参数环境下的RSS-TOA目标定位算法

在未知传输时间和发射功率的情况下,本文研究了非视距环境下基于RSS-TOA混合测量值的目标定位问题。首先为解决非视距误差会显著降低定位性能这一难题,根据最坏情况下的定位性能优化准则和鲁棒最小二乘法准则构建了未知视距和非视距路径分布状态下的目标定位优化问题。然后为了有效解决非凸和高度非线性的原定位问题,采用二阶锥松弛技术将该问题转化为凸优化问题,并通过内点法有效得到最优解。最后仿真结果验证了所提出的定位算法优于已有的定位算法。     

基于接收信号强度的WSN概率定位算法

定位对无线传感器网络的应用、操作和管理发挥着至关重要的作用.针对传感器节点的定位,提出了一种基于接收信号强度的概率定位算法.介绍了算法原理及实现过程,讨论了信标节点分布对该算法性能的影响,最后比较了本概率定位算法和最小二乘定位算法在传感器节点定位性能上的优劣.仿真结果表明,信标节点分布对未知节点的定位误差具有较大的影响,本定位算法的性能要优于最小二乘定位算法.     

两种NLOS误差消除及TOA定位算法

在蜂窝网络定位中,由于NLOS环境造成的附加时延(NLOS误差)是导致定位精度下降的主要原因,本文将NLOS误差与系统测量误差合成的噪声分为均值部分和随机部分,利用卡尔曼滤波算法输出与噪声方差无关的特性,无需得到全部噪声方差的准确值,只利用系统测量噪声的方差,用卡尔曼滤波算法除随机部分,再根据噪声均值部分与移动台到基站距离的关系,提出了一种简单的最小二乘(LS)定位算法,或利用最优化方法进行定位;利用仿真实验得到滤波距离--误差先验信息,基于先验信息提出了第二种NLOS误差消除算法,再利用所提的最小二乘定位算法进行定位.仿真结果表明,本文提出的算法能够有效消除NLOS误差带来的影响,具有更高的定位精度与稳健性.     

基于TLS的主动全向声呐浮标目标定位与跟踪

采用最小二乘( LS)拟和法进行主动全向浮标目标定位时容易受测量误差的影响,为此提出一种基于总体最小二乘法( TLS)的定位算法,首先对主动全向浮标的定位方程和测速方程进行变换形成线性的量测方程,然后利用潜艇测距误差和浮标定位误差的随机性,基于总体最小二乘法对量测方程进行递归解算,解算过程根据潜艇运动的物理限制,取运动模型的状态预测值作为递归过程的初值,最终实现了测量误差平滑化和航迹的整体优化。仿真结果显示算法较之最小二乘法具有更好的抑制噪声能力和机动目标跟踪能力。     

应用预测最小二乘准则的AR模型判阶方法

本文通过多种AR模型的判阶准则的比较提出了应用最小二乘预测误差的滑窗预测最小二乘(Sliding Window Predictive Least Squares, SWPLS)判阶准则.采用这种准则的主要优点除了准确的判阶性能外,对于时变AR模型具有良好的跟踪特性,同时算法容易实现在线实时处理.文中主要对时变模型参数和时变模型阶数的多种情况进行了判阶模拟,验证了文中提出的滑窗最小二乘预测判阶准则的有效性.     

基于SRP和GA的声源定位方法

目前在枪声的定位系统中,大多采用到达时间差（DTOA）的方法对声源方向进行估计,这种方法没有用到阵列整体的优势,缺乏稳健性.而基于可控响应功率（SRP）的波束形成技术,具有较强的稳健性、抗噪性,以及具有一定的空域滤波性能,能够提高信号的信噪比,增加探测距离.在确定声源位置时,一般采用最优化算法,最小二乘法容易陷入局部极值点,而基于自然选择法则的遗传算法（GA）模拟生物进化过程,具有并行计算的特点,是一种全局搜索算法,能够得到全局最优解.将基于可控响应功率的波束形成技术和遗传法算法应用于枪声定位系统中,并通过实际的试验数据对此方法进行了验证,从结果来看具有较高的精度.     

Near-optimal range and depth estimation using a vertical array in acorrelated multipath environment

This paper proposes a near-optimal procedure to localize a single stationary source in a two-path underwater acoustic environment. The investigation is for an M-element vertical array with omnidirectional sensors. The range and depth estimators are developed using a linear least-squares technique when a set of auto- and cross-correlators is used for time difference of arrival (TDOA) estimates. A weighting matrix is derived to achieve the approximate maximum likelihood (ML) performance of the weighted least-squares range and depth estimators. The expressions for error variances and covariances of the range and depth estimates are derived with a small error analysis technique. It is verified analytically that the error covariance matrix of the weighted least-squares solutions reaches the Cramer-Rao lower bound in the small error region. The correlation of the range and depth estimation errors is investigated. Results show that the range and depth estimation errors are highly correlated in a multipath environment. The accuracy properties of the proposed multipath localization procedure are analyzed using different array configurations. The results show that the performances of the range and depth estimators are significantly improved if the linear-dependent TDOA estimates are included for localizing and that the unweighted range and depth estimators, using the entire set of TDOAs, are approximately optimal for most of the applications. The theoretical development of error variance and covariance expressions of the range and depth estimates, which incorporates the correlation in the TDOA estimates, is corroborated with Monte Carlo simulations     

逆向捕获时间差的Voronoi声源定位机制

提出一种逆向捕获时间差的声源定位协议,可以使传感器网络在大部分时间里处于射频休眠状态,因此具有显著的节能效果;在利用时间差数据求解声源位置时,引入Voronoi图理论对搜索空间进行裁剪,以提高算法搜索求解的效率和成功率。理论分析和实验结果表明该声源定位机制具有明显的能量有效性、定位解算的精确性、快速收敛性和顽健性,适用于能量受限无线传感器网络中动态声源的实时精确定位。     

基于信息散度的雷达/红外数据关联算法

针对基于多维分配模型的雷达与红外数据关联算法在构造关联代价函数时未考虑最大似然估计引入的误差的一问题,提出了一种基于信息散度的雷达与红外数据关联算法.该算法首先利用无迹变换获得伪量测的统计信息.然后在构造关联代价函数时,将真实量测数据的极大后验分布和伪量测的概率密度函数的之间的Kullback-Leibler散度(KLD)作为关联代价,继而代入多维分配模型求解关联.最后进行了仿真分析,结果表明该算法具有良好的关联性能,其关联代价可更精准地反映数据关联的可能性程度.     

基于NLS的多机只测角误差配准算法

多机无源融合定位中的误差配准是目前多传感器误差配中的难点之一。当无源传感器获得的观测量存在系统误差却不进行配准时,多机融合定位的效果将受到严重影响。针对这一情况,在多机只测角无源定位问题中提出了一种基于非线性最小二乘（NLS）的误差配准算法。该算法将多机只测角误差配准问题转换为非线性最小二乘估计问题,并采用高斯–牛顿法求解,即先将非线性量测方程线性化并采用加权最小二乘进行估计,然后进行迭代直至收敛到最优估计值。仿真结果表明,与EKF配准算法相比,当观测时间足够长时,本文提出的NLS误差配准算法的定位误差可以接近克拉美罗限（CRLB）,并且对系统误差的估计精度非常高。      

Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks

A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to the existing acoustic energy based source localization methods, this proposed ML method delivers more accurate results and offers the enhanced capability of multiple source localization. A multiresolution search algorithm and an expectation-maximization (EM) like iterative algorithm are proposed to expedite the computation of source locations. The Crame/spl acute/r-Rao Bound (CRB) of the ML source location estimate has been derived. The CRB is used to analyze the impacts of sensor placement to the accuracy of location estimates for single target scenario. Extensive simulations have been conducted. It is observed that the proposed ML method consistently outperforms existing acoustic energy based source localization methods. An example applying this method to track military vehicles using real world experiment data also demonstrates the performance advantage of this proposed method over a previously proposed acoustic energy source localization method.     

Range-Free Mobile Sensor Localization and a Novel Obstacle Detection Technique

Mobile sensor localization is a challenging problem in wireless sensor networks. Due to mobility, it is difficult to find exact position of the sensors at any time instance. The aim of localization is to minimize positioning errors of the mobile sensors. In this paper we propose two range-free distributed localization algorithms for mobile sensors with static anchors. Both the algorithms depend on selection of beacon points. First we assume that mobile sensors move straight during localization which helps us to provide an upper bound on localization error. Certain applications may not allow sensors to move in a straight line. Obstacles may also obstruct path of sensors. Moreover beacon point selection becomes difficult in presence of obstacles. To address these issues, we propose another localization algorithm with an obstacle detection technique which selects correct beacon points for localization in presence of obstacles. Simulation results show improvements in performance over existing algorithms.     

信号波形已知条件下多目标直接定位性能分析及其改进算法

相比传统"测向+位置估计"两步定位体制,以Weiss和Amar提出的目标直接定位技术具有精度高、分辨能力强等优点,并且便于利用信号波形信息.该文首先针对Weiss和Amar提出的信号波形已知条件下的多目标直接定位算法（称为Weiss-Amar算法）进行理论性能分析,证明了其参数估计的渐近一致性,推导了其参数估计的渐近方差,并指出当不同目标信号波形相关时会对算法性能产生较大影响.针对这一缺点,文中提出了信号波形已知条件下的改进型多目标直接定位算法,与Weiss-Amar算法不同的是,改进算法对全部目标的位置参数进行协同估计,并且通过交替投影迭代进行数值优化,能显著提高对相关信号的定位精度.最后文中推导了信号波形已知条件下多目标位置直接估计方差的克拉美罗界.仿真实验验证了文中理论分析的有效性和改进算法的优越性.     

基于线性校正的时差定位算法

在分析传统时差定位算法优缺点的基础上,提出了一种新的线性校正时差定位算法。该算法首先根据方程误差非零均值的特点,对原始线性方程进行了修正,并在无约束条件下得到用户位置和距离的初始估计值,然后在初始位置估计值处对估计量进行了线性近似,并对原始的方程误差代价函数进行了等价替换,最后利用加权最小二乘方法得到了校正后的目标位置估计。仿真实验证明了本算法的有效性。     

A Framework for Improving the Performance of Direct Position Determination by Normal Hedge Algorithm for Emitter Localization in Indoor Applications

Indoor localization is a way to determine the location of transmitter(s) by using wireless network sensors without using GPS. In this paper, it is assumed that a random signal is transmitted by an unknown-position emitter through a noisy and lossy channel and then is received by known-position wireless sensors. Unlike the classical algorithms such as TDOA or AOA, the direct position determination (DPD) approach estimates the position of emitter in one step using the observation signal from all the collector receivers. In this paper a framework of methods is proposed to develop the DPD formulations. Originally, a preprocessing is applied on signals and later, a tree search algorithm and a fine localization algorithm will be proposed in order to achieve a higher resolution and with a less volume of computing. Furthermore, an approach is proposed to increase the performance of this algorithm. Using Normal Hedge algorithm with a proposed loss function, fuses the estimation results of several runs of the fine DPD algorithm. The simulation results are shown that the proposed framework would eventually improve the performance of the DPD algorithm.