Alleviating Sensor Position Error in Source Localization Using Calibration Emitters at Inaccurate Locations

A previous study shows that the use of a calibration emitter whose position is known exactly can significantly reduce the loss in time differences of arrival (TDOA) based source localization accuracy when the available sensor positions have random errors. This paper extends the previous work to a more practical scenario where the exact position of a calibration emitter is not known. By modeling the calibration position error as additive Gaussian noise, the amount of reduction in localization accuracy due to calibration position error is derived through Cramer-Rao lower bound (CRLB) analysis. In addition, the analysis also affirms the previous studies on Bayesian sensor network localization that it remains possible to improve the localization accuracy even if the calibration position is completely unknown. Next, a performance analysis illustrates that the penalty could be very high if one simply pretends the calibration position is accurate and ignores its error. A closed-form solution is then developed by accounting for the calibration position error and it is proved analytically to reach the CRLB accuracy when the sensor and calibration position errors are small relative to the distance between the calibration emitter and the sensor. Finally, the results are generalized to the case where multiple calibration emitters are available. When deploying multiple calibration emitters, although their positions may not be known exactly, we show that it is possible to completely eliminate the sensor position error and recover the best localization accuracy that is limited by the measurement noise in TDOAs only. All the theoretical developments are corroborated by simulations.     

Distributed Source Localization under Anchor Position Uncertainty

In this work, a distributed source position- ing approach is developed based on Alternating direction method of multipliers （ADMM）. First, a centralized posi- tioning method is developed under case of the anchor un- certainty. And then, the method is realized in a distributed way using ADMM. Simulation results show that the cen- tralized one is robust to the anchor errors and distributed one has similar performance as the centralized one.     

阵列天线互耦、阵元位置误差和通道不一致性的校正

研究了阵列天线中由各阵元互耦、位置误差及通道不一致性引起的阵列流形误差的校正问题.在计算得到互阻抗矩阵对互耦实行补偿的基础上,利用单信号源可以实现各阵元位置、通道幅度和相位的较精确的估计,从而估计出综合误差存在情况下的阵列流形.这种方法解决了一般条件和复杂情况下代价函数难于收敛的问题.仿真结果表明了这种校正方法的有效性和稳健性.     

传感器位置误差下外辐射源雷达三维定位代数解算法

机载外辐射源雷达系统中,部署在飞机上的观测站传感器位置无法精确获知,观测站位置误差将严重影响目标定位精度。对此,该文提出一种观测站位置误差下多基外辐射源雷达3维定位代数解算法。该算法首先利用辅助变量将非线性双基距离和差(BRD)观测方程进行线性化,构造伪线性目标估计模型。然后将观测站位置量测噪声的统计特性融入定位算法,提出一种改进两步加权最小二乘(TS-WLS)算法实现观测站位置误差下外辐射源雷达目标定位。最后推导了克拉美罗下界(CRLB)和算法的理论误差。仿真结果显示,在适中的BRD量测误差和观测站位置误差下,所提算法的目标定位性能能够达到CRLB。

     

一种阵列天线阵元位置,幅度及相位误差的有源校正方法

本文针对等距线阵提出了一种对同时存在阵元位置、幅度及相位误差的阵列进行校正的方法，该方法适用于任意阵列形式，同时，本文也提出了一种估计等距线阵误差量级的方法。计算机模拟及对实际阵的实验表明了本文方法的有效性。     

基于能量的传感器网络目标定位方法

针对运动车辆的震动信号提出了一种基于能量的模糊逻辑定位方法。拟合了运动目标能量衰减模型,制定了模糊规则,结合几何定位方法实现定位。最后根据外场数据给出了仿真结果。     

Acoustic Source Localization in Wireless Sensor Network

In this paper, we consider the problem of acoustic source localization in a wireless sensor network based on different measured signal quantities, such as the received signal strength (RSS), the angle of arrival (AOA) and the time of arrival (TOA). For each of these quantities, an appropriate weighted least squares criterion function is developed to be used for sound source localization. The weights of each criterion function take into account the decrease in the signal-to-noise ratio (SNR) with distance from the source. In addition, RSS localization algorithm proposed in this paper provides improvement of the localization accuracy for low SNR. Finally, separate criterion functions for RSS, TOA and AOA are used together to obtain minimal localization error and maximal reliability of the acoustic source localization. Simulation analysis confirms improved performance of the proposed localization algorithm.     

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。      

非均匀噪声环境下宽带近场源定位的两种改进方法

研究了宽带近场信号源基于最大似然方法和相关信号子空间方法在非均匀噪声下的被动定位算法，并进行了比较。这两种算法均可在传感器任意分布的情况下有效地进行信号源定位。最大似然法采用了迭代的方法来估计噪声的协方差矩。而信号子空间法给出了聚焦阵构造的新方法。仿真试验证明了方法的有效性和稳健性。     

抑制校正源方位估计偏差的阵元位置误差鲁棒估计算法

针对校正源方位估计偏差会影响阵元位置误差校正精度这一问题,该文在假设校正源方位估计偏差的概率分布先验已知的条件下,根据信号子空间拟合理论和贝叶斯估计理论框架,提出一种能够抑制校正源方位估计偏差的阵元位置误差鲁棒估计算法.该算法以Newton迭代的形式给出,并不需要对校正源方位进行数值优化.理论分析和仿真实验均表明:在一定条件下,文中的新算法对校正源方位估计偏差具有较好的鲁棒性,并且其渐近性能可逼近相应的克拉美罗界.     

A Decoupled Approach for Near-Field Source Localization Using a Single Acoustic Vector Sensor

This paper considers the problem of three-dimensional (3-D, azimuth, elevation, and range) localization of a single source in the near-field using a single acoustic vector sensor (AVS). The existing multiple signal classification (MUSIC) or maximum likelihood estimation (MLE) methods, which require a 3-D search over the location parameter space, are computationally very expensive. A computationally simple method previously developed by Wu and Wong (IEEE Trans. Aerosp. Electron. Syst. 48(1):159–169, 2012), which we refer to as Eigen-value decomposition and Received Signal strength Indicator-based method (Eigen-RSSI), was able to estimate 3-D location parameters of a single source efficiently. However, it can only be applied to an extended AVS which consists of a pressure sensor separated from the velocity sensors by a certain distance. In this paper, we propose a uni-AVS MUSIC (U-MUSIC) approach for 3-D location parameter estimation based on a compact AVS structure. We decouple the 3-D localization problem into step-by-step estimation of azimuth, elevation, and range and derive closed-form solutions for these parameter estimates by which a complex 3-D search for the parameters can be avoided. We show that the proposed approach outperforms the existing Eigen-RSSI method when the sensor system is required to be mounted in a confined space.     

无线传感器网络中一种协同源定位算法

针对无线传感器网络传统目标定位过程中,数据在大量传感器节点间传输使得节点能量迅速耗尽,同时远距离低信噪比节点对定位精度的影响等问题。基于分区域协同工作的思想,提出了一种协同源定位算法。对该算法进行仿真试验,结果显示,该算法明显降低了网络能耗,且具有较高的定位精度和稳健性。     

共形阵列天线振动条件下稳健的DOA估计及位置误差校正

共形阵列天线比传统的线阵和面阵具有更优良的空气动力学性能、更宽的波束扫描范围、更小的雷达散射截面积和更大的阵列天线孔径等优势。然而共形天线载体受到飞行惯性和空气动力负载的相互作用很容易发生共形表面的变形和单元位置的扰动。更为重要的是平台的机械振动通常还会导致单元位置扰动的时变性,严重影响了共形天线的分辨测向性能。因而针对此类载体共形天线设计稳健的DOA估计算法和位置误差校正算法十分必要和有意义。本文提出了一种共形天线振动条件下的稳健DOA估计和位置误差校正算法。在给出共形线阵振动的数学模型基础上,得到修正的时变阵列导向矢量。采用时变的阵列导向矢量在一个振动周期内计算MUSIC谱,搜索空时二维的MUSIC谱的谱峰,得到本次采样快拍数据时阵列振动位于振动周期中的时刻和信源方位的联合估计。根据阵列振动的数学模型就可以进一步预测下一次采样快拍数据时刻阵列各阵元的位置误差,从而实现振动条件下的阵元位置误差校正和稳健的DOA估计。计算机仿真结果表明了提出算法的有效性。      

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.     

同步时钟偏差存在下的时差定位性能分析及改进的定位方法

同步时钟偏差会显著增加时差（TDOA）定位误差,该文针对这一问题进行了理论性能分析,并提出了改进方法.首先,分析了时钟偏差存在下参数估计方差的克拉美罗界（CRB）,给出了关于目标位置估计方差更为闭式的CRB表达式,随后基于最大似然（ML）估计准则和泰勒级数（TS）定位方法,定量推导了时钟偏差对于TDOA定位精度的影响.接着,提出了可抑制时钟偏差的降维TS定位方法,并且给出了时钟偏差的ML闭式解.最后,数值实验验证了文中理论分析的有效性,并且新方法可以有效抑制同步时钟偏差的影响.     

Sensor Allocation for Source Localization With Decoupled Range and Bearing Estimation

The source localization accuracy is known to be affected by the placement of sensors. This paper addresses the problem of optimum sensor allocation for decoupled range and bearing estimation, as well as position estimation of an emitting source, under the constraint that all sensors must be confined within a certain area. The source is assumed to be distant and has a curved wavefront when arriving at the sensors. The optimization criterion is the minimum estimation variance defined by the Cramer–Rao lower bound that is derived under Gaussian noise model. A geometric approach is employed to arrive at the optimum geometries and no complicated optimization technique is required. The optimum sensor allocations for time difference of arrival (TDOA), angle of arrival (AOA), and time of arrival (TOA) positionings are derived and their estimation accuracies are contrasted. The optimum geometries belong to the generic structure of a ring array plus some sensors at the center. TDOA and TOA are found to have identical optimum geometries for bearing estimation and yield the same accuracy, although the position localization accuracy of TDOA is known to be worse than that of TOA. Simulations are included to confirm the theoretical development and illustrate the improvement of using an optimum array compared to a random sensor placement array.      

考虑锚节点位置不确定的水下目标定位算法研究

考虑时钟异步和声波分层效应的影响,该文研究了当测量过程受到未知噪声干扰,且锚节点位置不确定时水下目标节点的定位问题。首先构造了水下节点间飞行时间模型,设计了一种交互式异步通信协议,建立了最小化定位误差的优化目标函数。然后提出了一种基于深度强化学习的水下目标定位算法,并采用层归一化来改进深度神经网络,进一步提高模型的泛化能力。最后,仿真和实验结果验证所提方法的有效性。     

阵列天线阵元位置误差的一种有源校正方法

针对等间距直线阵,在子空间基本原理的基础上结合遗传算法,提出了天线阵元位置误差的一种有源校正方法。其优点在于只需要一个校正信号源,而信号源的方向也不必准确已知,所以更加易于工程应用。由于充分利用了遗传算法的全局寻优特性,该方法收敛速度更快,而且适用于任意阵列形式。计算机模拟结果表明,采用该方法完成误差校正后,用MUSIC算法能实现对信号DOA较为准确的估计。     

考虑信源方位误差的阵元位置误差校正

针对信源方位信息存在偏差时阵元位置误差进行讨论,提出了一种基于单信源的误差校正方法。该方法中假设接收阵列可精密旋转,达到多个信源分时校正的效果,从而获得大量样本,先用最小二乘拟合法对校正源的方位信息进行估计,再采用剔野的方式剔除某些阵元位置误差较大的数据,达到校正信源方位信息的目的,然后再次采用最小二乘法校正阵元位置误差,最后仿真验证了该方法的有效性,并对比分析了阵元位置误差校正前后的MUSIC谱测角。