基于Bancroft算法的多点定位TOA-LS估计

 由于目标信号的发射时间未知,无源定位技术大多利用TDOA(到达时间差)进行目标定位.本文将求解GPS单点定位的Bancroft算法应用于TOA(到达时间)多点定位方程的求解,提出了基于Bancroft算法的无源多点定位TOA-LS(最小二乘)估计算法.TOA-LS包含三个线性方程,其加权系数与目标位置和目标信号发射时间相关.采用Bancroft算法给出初始值并求解TOA-LS方程,完成加权系数的迭代更新.仿真结果表明对不同几何位置的目标,TOA-LS估计可获得接近理论方差下界的近似最优的性能.     

基于粒子滤波和交互多模型的移动定位方法

为提高非视距情况下移动辐射源的定位精度,提出一种改进的交互粒子滤波算法.该算法对目标运动多模型和信号到达时间差测量噪声分布多模型联合建模.在交互多模型状态更新中利用粒子滤波对目标时变状态以及视距/非视距混合信道参数进行估计,抑制了非视距测量误差对移动定位的影响.仿真结果表明,改进算法的性能要优于现有的视距条件运动多模型和视距/非视距条件单一运动模型的定位算法,并且定位误差接近于推导的后验克拉美劳下界.     

对机动目标单站无源定位的最大似然估计

研究了基于目标的信号到达方向和信号到达时问差信息，利用单个固定的无源侦察站，通过最大似然估计方法对机动目标进行定位的问题。首先建立了机动目标模型与观测方程，然后详细推导了定位算法，并进行了仿真实验。仿真结果表明，该方法具有定位精度高、算法稳定等优点。     

对机动辐射源单站无源定位IMM算法

探讨了一种利用单个观测站对机动辐射源目标进行无源定位与跟踪的交互式多模型（IMM）算法。在该方法中，基于辐射源的信号到达时间（TOA）和信号到达方向（DOA）信息，可以实现单站无源定位，运用交互式多模型算法，可以适应目标的机动性。通过计算机仿真，验证了该方法的正确性和有效性。     

一种基于循环稀疏表示的同频带干扰抑制及发射源定位方法

针对无线通信中的同频信号干扰问题,受信号稀疏表示及循环平稳特性启发,本文通过建立位置坐标的样本集合,构造字典矩阵,采用优化方法在循环频域实现稀疏信号重建,利用接收到信号的循环谱切片直接估计发射源位置.仿真结果表明,该所提算法的定位误差接近于推导的克拉美劳下界,在低信噪比和信干比条件下优于现有的频域直接定位和两步定位算法,具有更高的定位精度.     

基于外辐射源雷达系统的目标定位算法

基于外辐射源的单站无源定位系统实际相当于收发分置的双基雷达,具有反侦察、抗干扰、反隐身和防反辐射导弹等潜在优势,因此利用外辐射源信号对目标进行无源定位和跟踪是目前国际上比较热门的一个研究课题。提出了一种基于外辐射源的单站无源定位系统的目标定位算法,借助信号到达的时间差和方位信息,对空中目标进行定位,并进行算法的推导、精度的分析,通过仿真验证了此定位算法的有效性。     

多校正源联合的TDOA/FDOA无源定位算法

移动观测站和目标之间的位置和速度误差会导致定位性能下降,文中通过克拉美罗下界分析,讨论了存在该类误差时使用校正源对无源时频差定位精度的改善作用,并推导了多个校正源相对于单个校正源所带来的定位性能增益.在此基础上,提出了一种联合校正源测量方程的时频差定位算法,该算法首先利用Taylor级数展开将观测站的位置误差和速度误差线性化,再采用牛顿法迭代实现目标定位.通过蒙特卡罗仿真,比较了该算法和不使用校正源时误差校正算法的定位性能,仿真结果与文中的理论分析一致.     

估计目标速度矢量对运动辐射源的 固定单站无源定位算法

针对无源定位必须实现快速和稳定定位跟踪的要求,本文提出了一种测量目标辐射源脉冲到达时间(TOA)、到达方向(DOA),以及方位角变化率,实现固定单站对三维运动辐射源的无源定位算法.该算法基于运动学原理,充分利用了辐射源信号测量值的时空变化量信息,定位收敛速度快而且非常稳定,具有很强的实用性.文章最后给出了计算机仿真结果.     

短基线时差无源定位方法研究

由于短基线时差无源定位受到辐射源目标到达时间测量精度和多站间定位精度的影响,使得短基线时差无源定位精度比长基线情况要差。本文研究了短基线时差无源定位的滤波算法,推导了时差定位的推广Kalman滤波算法公式。仿真结果表明,该算法可有效提高短基线情况下的时差无源定位精度。     

一种高精度移动多站无源定位方法

提出了一种基于改进遗传算法的远距离固定目标高精度多站无源定位方法,在被测固定目标发射的信号中存在稳定载波情况下,本方法的定位精度可以达到载波波长级别.建立了移动多站观察固定目标的载波相位解算模型,在多移动站同时稳定跟踪目标载波情况下,利用改进遗传算法对多站多历元时刻的载波相位观测量进行迭代递推求解,获得高精度的目标定位信息.理论推导与仿真结果表明:该方法是一种高精度的移动多站无源定位算法.     

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.     

无源定位观测站站址误差自校正算法

无源定位中观测站的站址误差会对定位精度产生不良影响。本文提出了一种基于观测站站址误差自校正的定位算法,推导了高斯噪声模型下定位误差的克拉美罗下界（CRLB）,通过泰勒级数展开建立了关于站址误差的线性方程并得到了误差的线性最小均方误差（LMMSE）估计,改善了观测站的位置,使用Chan算法得到了定位结果,仿真验证了算法的定位精度在高斯噪声模型下能够达到CRLB。      

Theoretical Limits for Time Delay Based Location Estimation in Cooperative Relay Networks

Node localization in wireless networks is crucial for supporting advanced location-based services and improving the performance of network algorithms such as routing schemes. In this paper, we study the fundamental limits for time delay based location estimation in cooperative relay networks. The theoretical limits are investigated by obtaining Cramer–Rao Lower Bound (CRLB) expressions for the unknown source location under different relaying strategies when the location of the destination is known and unknown. More specifically, the effects of amplify-and-forward and decode-and-forward relaying strategies on the location estimation accuracy are studied. Furthermore, the CRLB expressions are derived for the cases where the location of only source as well as both source and destination nodes are unknown considering the relays as reference nodes. In addition, the effects of the node topology on the location estimation accuracy of the source node are investigated. The results reveal that the relaying strategy at relay nodes, the number of relays, and the node topology can have significant impacts on the location accuracy of the source node. Additionally, knowing the location of the destination node is crucial for achieving accurate source localization in cooperative relay networks.     

Distributed location model and algorithm with position uncertainty

The localization of multiple mobile terminals （MTs） is an encouraging paradigm of applications in wireless networks. Peer-to-peer communication between MTs facilitates the cooperative localization of multiple MTs. For sake of low complexity and high robustness, investigations often focus on the distributed algorithm in cooperative localization. However, the impact from position uncertainty of cooperative MT lacks of analysis in related works. A new distributed location model, as well as corresponding algorithm, is devised when considering both the distance measurement error and the position uncertainty of MTs, which is more judicious than the traditional model for distributed cooperative localization scenario. In addition, the performance of proposed algorithm is analyzed through Cramér-Rao lower bound （CRLB）. Simulations indicate that the algorithm outperforms traditional methods in terms of accuracy and robustness.     

On the Use of a Calibration Emitter for Source Localization in the Presence of Sensor Position Uncertainty

Sensor position uncertainty is known to degrade significantly the source localization accuracy. This paper investigates the use of a single calibration emitter, whose position is known to the sensor array, to reduce the loss in localization accuracy due to sensor position errors that are random. Using a Gaussian noise model, we first derive the CramÉr–Rao lower bound (CRLB) for a time difference of arrival (TDOA)-based source location estimate with the use of a calibration source. The differential calibration technique that is commonly used in Global Positioning System through the use of a calibration source to mitigate the inaccuracy in satellite ephemeris data is analyzed. The analysis indicates that differential calibration in most cases cannot reach the CRLB accuracy. The paper then proceeds to propose an algebraic closed-form solution for the source location estimate using both TDOA measurements from the unknown and the calibration source. The proposed algorithm is shown analytically, under high signal-to-noise ratio (SNR) and small sensor position noise, or under moderate level of SNR and sensor position noise together with distant unknown and calibration sources, to reach the CRLB accuracy. Simulations are used to corroborate and support the theoretical development.      

Dual‐mode multiple‐target tracking in wireless sensor networks based on sensor grouping and maximum likelihood estimation

The problem of tracking multiple mobile targets, using a wireless sensor network, is investigated in this paper. We propose a new sensor grouping algorithm, based on the maximum sensor separation distances (G‐MSSD), for estimating the location of multiple indistinguishable targets, either jointly or individually, depending on the distances between the generated groups. The joint tracking algorithm is formulated as a maximum likelihood (ML) estimator and solved through a modified version of the well‐known Gauss‐Newton (MGN) iterative method. We propose two candidate initial guesses for MGN based on G‐MSSD in joint tracking mode, while for the individual mode, the information of each group is used to estimate the location of only the corresponding target. The Cramer‐Rao lower bound (CRLB) for the variance of the proposed ML estimator is derived, and the potential conditions for reducing the CRLB are presented. Since tracking efficiency is affected by poor estimates, we present two criteria to evaluate the quality of estimates and detect the poor ones. An approach is also proposed for correcting the poor estimates, based on additional initial guesses. We demonstrate the effectiveness and accuracy of our proposed dual‐mode algorithm via simulation results and compare our results with the Multi‐Resolution search algorithm.     

利用约束加权最小二乘的单站外辐射源时差定位算法

针对利用单个观测接收站接收数个外辐射源信号进行目标定位过程中,传统解析定位算法估计精度不够理想的问题,提出了一种利用约束加权最小二乘（CWLS）到达时差的定位算法。首先将目标到观测接收站的距离作为冗余参量,实现时差观测方程的线性化,然后根据目标到观测接收站的距离与目标位置之间的函数关系,建立为CWLS定位模型,并采用拉格朗日乘子式(Lagrange multiplier)求解该类带约束条件的CWLS问题。同时,还推导了相应的克拉美罗界(CRLB)和理论误差。通过仿真实验和分析可以表明,在存在测量误差的情况下,本文提出的新算法仍然能够逼近CRLB,且与解析方法相比,具有较好的定位精度。      

利用多普勒的运动单站直接定位算法

针对利用运动单站接收辐射源信号进行目标定位过程中,传统两步定位算法测量参数时会产生信息损失,从而导致估计精度不够理想的问题,提出了一种联合多普勒的运动单站直接定位算法。该算法通过构造代价函数,得到包含辐射源位置信息的共轭矩阵,然后分别针对未知信号和已知信号通过特征值分解求取共轭矩阵的最大特征值,最后进行二维格网型空间索引得到辐射源的位置估计。同时,本文还推导了相应的克拉美罗下界(CRLB),并分析了多普勒测量存在误差下算法的定位性能以及算法的运算量。通过理论分析和仿真实验可以表明,所提出的运动单站直接定位算法对目标位置估计误差能够逼近CRLB,且与两步定位算法相比,具有更好的定位精度。      

Access Point Height Based Location Accuracy Characterization in LOS and OLOS Scenarios

Location estimation in a wireless local area network (WLAN) using received signal strength indication (RSSI) has gained considerable attention in recent years. In a conventional RSSI based indoor WLAN localization, mobile node position is estimated through access point (AP) placed at ceiling height. Researchers have proposed solutions for location estimation in line of sight (LOS) scenarios, by installing the AP at a fixed position. This paper demonstrates the improved location accuracy in LOS and obstructed line of sight (OLOS) scenarios by placing the AP at lower heights. The RSSI variations caused by shadow fading for changing AP heights are used to estimate the location accuracy. The localization performance is computed in terms of Cramer-Rao lower bound (CRLB) of range estimate under dynamic environments which is relatively less complex computation technique and is calibration free. Simulation results reveal that the proposed method has better performance than the multilateration with linearization for access point localization algorithm. The minimum mean localization errors are obtained by deploying the access point at 2 m height. The results also demonstrate that the indoor localization accuracy improves for higher order path loss exponent.     

无线传感器网络节点定位算法研究

研究无线传感器网络节点定位的方法。首先介绍了节点定位的基本原理,在总结节点定位原理的基础上,对节点定位方法的分类依据进行了归纳。在对无线传感器网络节点定位方法的研究中,主要对是否基于测距的节点定位方法进行具体分析,介绍了2种类型的定位方法的基本原理,并对2种类型的定位方法中的典型算法做了具体说明,最后介绍了定位算法的评价标准。