Localization of multiple disjoint sources with prior knowledge on source locations in the presence of sensor location errors

Sensor location errors are known to be able to degrade the source localization accuracy significantly. This paper considers the problem of localizing multiple disjoint sources where prior knowledge on the source locations is available to mitigate the effect of sensor location uncertainty. The error in the priorly known source location is assumed to follow a zero-mean Gaussian distribution. When a source location is completely unknown, the covariance matrix of its prior location would go to infinity. The localization of multiple disjoint sources is achieved through exploring the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) measurements. In this work, we derive the Cramér–Rao lower bound (CRLB) of the source location estimates. The CRLB is shown analytically to be able to unify several CRLBs introduced in literature. We next compare the localization performance when multiple source locations are determined jointly and individually. In the presence of sensor location errors, the superiority of joint localization of multiple sources in terms of greatly improved localization accuracy is established. Two methods for localizing multiple disjoint sources are proposed, one for the case where only some sources have prior location information and the other for the scenario where all sources have prior location information. Both algorithms can reach the CRLB accuracy when sensor location errors are small. Simulations corroborate the theoretical developments.     

Energy-efficient cognitive access approach to convergence communications

This article studies the cognitive access in convergence communications.Convergence communications provide upper-layer applications with uniform communication service,converging different lower-layer networks into a uniform access pattern such as all-IP communications.As an import access in convergence communications,the cognitive access provides users with a flexible and dynamic access to networks.In this article,we do not only take into account the spectrum usage of convergence communication networks,but also consider theirs energy efficiency.An energy-efficient access algorithm is proposed to improve network performance and efficiency.Different from the existing cognitive access,we regard energy efficiency as the optimal objective to turn the energy-efficient cognitive access into an optimal problem.The collision avoidance and sleeping mechanisms are used to reduce energy consumption and raise network throughput.The utility function is proposed to maximize networks’energy efficiency and then achieve the energy-efficient cognitive access.Simulation results show that the proposed approach is effective and feasible,which can significantly improve networks’energy efficiency.     

无线传感器网络中基于TDOA/FDOA的增强半正定松弛定位算法研究

本文在无线传感器网络定位问题中,考虑了基于到达时间差（Time-Difference-of-Arrival,TDOA）和到达频率差（Frequency-Difference-of-Arrival,FDOA）的移动未知目标定位问题,TDOA/FDOA联合定位可以有效利用传感器的位置和速度信息,提高了定位精度。本文在现有的半正定松弛（Semidefinite Relaxation, SDR）方法的基础上,提出了一种增强半正定松弛方法。通过挖掘现有半正定规划问题中优化变量之间的内在联系并将这些联系转化为凸约束,有效提高了现有半正定松弛方法的紧度,从而使估计的未知目标的位置和速度精度达到了克拉美-罗下界 (Cramer Rao lower bound,CRLB)。仿真结果表明,该方法的性能在大噪声时优于现有方法。     

Iterative constrained weighted least squares estimator for TDOA and FDOA positioning of multiple disjoint sources in the presence of sensor position and velocity uncertainties

Sensor position and velocity uncertainties are known to be able to degrade the source localization accuracy significantly. This paper focuses on the problem of locating multiple disjoint sources using time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs) in the presence of sensor position and velocity errors. First, the explicit Cramér–Rao bound (CRB) expression for joint estimation of source and sensor positions and velocities is derived under the Gaussian noise assumption. Subsequently, we compare the localization accuracy when multiple-source positions and velocities are determined jointly and individually based on the obtained CRB results. The performance gain resulted from multiple-target cooperative positioning is also quantified using the orthogonal projection matrix. Next, the paper proposes a new estimator that formulates the localization problem as a quadratic programming with some indefinite quadratic equality constraints. Due to the non-convex nature of the optimization problem, an iterative constrained weighted least squares (ICWLS) method is developed based on matrix QR decomposition, which can be achieved through some simple and efficient numerical algorithms. The newly proposed iterative method uses a set of linear equality constraints instead of the quadratic constraints to produce a closed-form solution in each iteration. Theoretical analysis demonstrates that the proposed method, if converges, can provide the optimal solution of the formulated non-convex minimization problem. Moreover, its estimation mean-square-error (MSE) is able to reach the corresponding CRB under moderate noise level. Simulations are included to corroborate and support the theoretical development in this paper.     

Source localization and calibration using TDOA and FDOA measurements in the presence of sensor location uncertainty

Source localization accuracy is very sensitive to sensor location error.This paper performs analysis and develops a solution for locating a moving source using time difference of arrival(TDOA)and frequency difference of arrival(FDOA)measurements with the use of a calibration emitter.Using a Gaussian random signal model,we first derive the Cram′er-Rao lower bound(CRLB)for source location estimate in this scenario.Then we analyze the differential calibration technique which is commonly used in Global Positioning System.It is indicated that the differential calibration cannot attain the CRLB accuracy in most cases.A closed-form solution is then proposed which takes a calibration emitter into account to reduce sensor location error.It is shown analytically that under some mild approximations,our approach is able to reach the CRLB accuracy.Numerical simulations are included to corroborate the theoretical developments.     

Coherent summation of multiple short-time signals for direct positioning of a wideband source based on delay and Doppler

We consider identifying the source position directly from the received source signals. This direct position determination (DPD) approach has been shown to be superior in terms of better estimation accuracy and improved robustness to low signal-to-noise ratios (SNRs) to the conventional two-step localization technique, where signal measurements are extracted first and the source position is then estimated from them. The localization of a wideband source such as a communication transmitter or a radar whose signal should be considered deterministic is investigated in this paper. Both passive and active localization scenarios, which correspond to the source signal waveform being unknown and being known respectively, are studied. In both cases, the source signal received at each receiver is partitioned into multiple non-overlapping short-time signal segments for the DPD task. This paper proposes the use of coherent summation that takes into account the coherency among the short-time signals received at the same receiver. The study begins with deriving the Cramér–Rao lower bounds (CRLBs) of the source position under coherent summation-based and non-coherent summation-based DPDs. Interestingly, we show analytically that with coherent summation, the localization accuracy of the DPD improves as the time interval between two short-time signals increases. This paper also develops approximate maximum likelihood (ML) estimators for DPDs with coherent and non-coherent summations. The CRLB results and the performance of the proposed source position estimators are illustrated via simulations.     

Efficient semidefinite solutions for TDOA-based source localization under unknown PS

Two efficient solutions via Semi-Definite Programming (SDP) are proposed for source localization problems using time difference of arrival (TDOA)-based ranging measurements when the propagation speed (PS) is unavailable and considered as a variable. For this problem, we propose a relaxed SDP (RSDP) solution, the performance of which is suboptimal. Accordingly, we propose a two-stage SDP method to improve the performance by applying the rank-reduction method. Besides, we also propose a penalty function-based SDP (PF-SDP) by introducing the penalty term. By doing so, the cost function becomes tighter so that the solution performs better. The simulated results show that the performance of two-stage SDP is sufficiently close to the Cramér-Rao Lower Bound (CRLB) accuracy at high noise levels. The PF-SDP outperforms the two-stage SDP in the presence of low noise levels.     

基于移动锚节点的无线传感器网络三边质心定位

探讨了无线传感器网络(WSN)定位技术的意义,研究基于移动锚节点的测距定位技术;设计了移动锚节点运动轨迹,在利用无线电与超声波到达时间差(TDOA)测得锚节点到待定位节点距离的情况下,给出了一种新的定位算法——三边质心定位算法,该算法通过求解待定位节点的定位近点所构成几何图形的质心来完成定位;仿真结果表明,该定位技术能够明显减小定位误差与锚节点数量。     

基于AOA的无线传感器网络节点定位算法

在无线传感器网络节点SDP定位算法基础上,提出了一种改进的基于信号到达角(AOA)信息的无线传感器网络节点自身定位算法。本算法在所有节点的坐标轴方向都是未知的假定下,将AOA测量值用于计算信号到达差分角信息,将所有节点之间的角度关系表述为凸规划约束条件,从而将定位问题转化为一个凸集优化问题。在理想情况下,将定位问题转化为一个线性规划问题。实际情况中,考虑到测量误差,通过引入辅助变量,将定位问题转化为一个二次规划问题。仿真结果表明:改进算法与原算法相比,对AOA测量误差在10°以下时,定位精度提高约为5%~20%。     

基于AUPF的TDOA几何定位跟踪算法研究

使用无源时差（TDOA）定位技术确定无人机等小型辐射源目标的位置是当前研究的热点,针对时差定位算法较为复杂的实际情况,推导了时差双曲线的几何解,并提出了一种基于自适应无迹粒子滤波（AUPF）技术的移动目标定位跟踪方法。通过仿真对该方法在不同场景的应用效果进行了验证,进一步比较分析了算法的定位精度。结果表明,基于自适应无迹粒子滤波的时差几何定位跟踪算法可以在多种情况下较好地拟合出目标真实运动轨迹,实现对运动目标的定位跟踪,同时拥有更低的定位误差和更高的轨迹包容度,使用该方法可以显著提高对非合作移动辐射源目标的位置估计性能。     

时间偏移下的多源目标精确定位方法

采用到达时间( TOA)测距实现源目标定位的方法比较简单,因此在无线传感器网络定位领域得到了广泛的应用。将TOA定位模型转化为线性优化问题,提出了一种存在时间偏移下的多源目标精确定位方法。该方法将定位计算过程分成两步,包括多源目标位置初始值估计和优化计算过程。两步计算方法将定位结果用代数解表示,避免了数值计算过程中的局部最优问题。仿真分析了时间偏移量及噪声大小对定位误差的影响。结果表明即使存在较大的时间偏移量,优化计算后的定位误差也能非常接近于克拉美罗( CRLB)下界值。     

两种基于自适应相位补偿的FDOA估计算法

将约束的自适应相位差估计补偿算法引入到频偏估计当中,实现信号间相位对齐。然后,利用自适应相位补偿因子,根据估计方式的不同,给出了两种频偏估计算法:基于时间平均的算法与基于线性拟合的算法。基于时间平均的频偏估计算法是一种渐进无偏的估计算法,具有可控的误差和非常小的均方差,以及计算简单的特点。仿真结果表明当信号信噪比大于-3dB时,基于线性拟合的频偏估计算法性能与CRLB非常接近。     

基于圆形麦克风阵列的声源定位改进算法

针对波达方向估计中传统互功率谱法声源方位估计准确性差、方位模糊的问题,提出了一种基于圆形麦克风阵列的声源定位改进算法,并进行了实验验证。在该改进算法中,先设计了十二元圆形麦克风阵列,由麦克风对接收语音信号的时延与相位得到相位旋转因子,再将其引入到语音信号的互功率谱中,新定义了圆形集成互功率谱,由该功率谱进行声源方位估计。仿真与实测实验结果表明,本文的圆形集成互功率谱法对声源方位进行估计,估计的准确度高于传统互功率谱法。     

基于TDOA定位机制的无线传感器网络节点设计

介绍了一种具有到达时间差(TDOA)定位机制的基于数字信号处理器(DSP)的无线传感器网络节点。它包括TMS320F2812DSP,512 kB的SRAM,2.4 GHz波段的无线收发模块、音频信号收发模块及电源管理模块等。节点通过测量RF同步信号与音频信号的TDOA来测量节点间的间隔距离,并采用互相关时延估计算法来实现节点定位。实验数据表明:该节点最远测距距离超过25m,角度误差小于3.7%。     

基于飞行理论萤火虫算法的TDOA与GROA定位机制

节点定位是无线传感器网络中最为关键的一项技术。针对无源定位的问题,提出一种到达时间差(TDOA)和到达信号增益比(GROA)联合定位算法,并且采用飞行机制的萤火虫算法(GSO)来求得最终结果。结合TDOA和GROA定位模型,引入辅助变量将方程伪线性化,然后采用修正两步加权最小二乘算法(TSWLS)来进行求解。并且在不影响收敛速度和精度的前提下,采用带有飞行机制的GSO算法来寻求目标定位的最优解,克服粒子群算法易陷入局部最优的缺点。仿真结果表明,该算法相比较TDOA算法而言,定位精度提高了23 dB,并且具有相对较高和较稳定的定位精度。     

基于麦克风阵列的声源定位研究

以基于声达时间差(TDOA)的定位技术为基础,在噪声和混响同时存在的环境下,对基于麦克风阵列的声源定位方法进行了系统研究。在传统LMS自适应算法的基础上,提出了一种基于语音激励信息的LMS自适应时延估计新方法,再结合平面四元几何法定位。经过模拟房间环境的实验验证,该方法抗噪声、抗混响能力强,是一种定位精度高,运算量小的声源定位方法,可用于实时定位。     

Improving the localization accuracy of targets by using their spatial–temporal relationships in wireless sensor networks

Due to the low cost and capabilities of sensors, wireless sensor networks (WSNs) are promising for military and civilian surveillance of people and vehicles. One important aspect of surveillance is target localization. A location can be estimated by collecting and analyzing sensing data on signal strength, time of arrival, time difference of arrival, or angle of arrival. However, this data is subject to measurement noise and is sensitive to environmental conditions, so its location estimates can be inaccurate. In this paper, we add a novel process to further improve the localization accuracy after the initial location estimates are obtained from some existing algorithm. Our idea is to exploit the consistency of the spatial–temporal relationships of the targets we track. Spatial relationships are the relative target locations in a group and temporal relationships are the locations of a target at different times. We first develop algorithms that improve location estimates using spatial and temporal relationships of targets separately, and then together. We prove mathematically that our methods improve the localization accuracy. Furthermore, we relax the condition that targets should strictly keep their relative positions in the group and also show that perfect time synchronization is not required. Simulations were also conducted to test the algorithms. They used initial target location estimates from existing signal-strength and time-of-arrival algorithms and implemented our own algorithms. The results confirmed improved localization accuracy, especially in the combined algorithms. Since our algorithms use the features of targets and not the underlying WSNs, they can be built on any localization algorithm whose results are not satisfactory.     

基于QPSO的震动传感器片上相位补偿器设计方法

震动传感器的系统相位非一致性会对地震波到时时差提取产生很大的误差,严重影响震源定位精度;针对这一问题,提出了一种基于量子粒子群优化算法（QPSO）的震动传感器片上相位补偿器设计方法。首先对震动传感器进行相位标定,获得传感器与参考传感器的相位差;其次设计基于QPSO算法的相位补偿滤波器对相位差进行修正,使其无限趋近于0;最后,将相位补偿滤波器封装成FPGA软核部署于FPGA上,完成对震动传感器的相位片上实时补偿。为了验证该方法的性能,将相位补偿滤波器部署于自研的多通道震动信号采集系统上,对8个相同型号震动传感器进行相位一致性校准。试验结果表明,在震动传感器频响范围内,该方法可以将2.5°内的传感器相位差实时修正至0.0044°以下,实现了震动传感器阵列的相位一致性实时校准。该成果在地下浅层震源定位领域具有较强的应用价值。     

基于非同步采集方式小型声源定位系统设计

利用非同步采集设备,通过广义互相关(GCC)时延估计算法,估算出通道间的相对时延;对非同步采集方式产生的时延误差进行软件补偿;利用基于到达时间差(TDOA)声源定位算法的双步定位特性,估算出声源的位置.分别设计了四元均匀线阵系统和四元平面十字阵列系统对以上方法进行验证,系统能够较准确地实现对声源方位的估计.