联合角度和时差的单站无源相干定位加权最小二乘算法

针对利用单个观测站接收多个外辐射源信号从而实现对目标定位的单站无源相干定位问题, 该文提出了一种联合角度和时差的加权最小二乘定位算法。首先, 将角度和时差的观测方程线性化处理, 考虑方程中的各项误差, 将定位问题建立为加权最小二乘模型。然后利用迭代方法对模型求解。最后, 对算法的定位性能进行了理论分析。仿真结果表明, 不同于仅时差定位方法至少需要3个外辐射源才能定位, 联合角度和时差定位方法仅需一个外辐射源即可定位, 且在同样数量外辐射源条件下估计精度高于仅时差定位;算法的均方误差低于最小二乘算法, 在时差测量噪声较大时定位精度仍然能逼近克拉美罗界。此外, 对系统几何精度因子图的分析表明, 目标及辐射源的位置对定位精度也有重要影响。      

基于1比特量化的大规模MIMO雷达系统直接定位算法

1比特量化技术在大规模MIMO雷达系统中的应用使得系统成本、功耗及传输带宽显著降低。但这同时也对如何从1比特量化后的数据中提取目标高精度信息提出了严峻挑战。针对基于1比特量化的二次定位算法在低信噪比下定位精度低、鲁棒性差的问题,该文提出了一种基于1比特量化的大规模MIMO雷达系统目标直接定位算法。首先,通过将接收信号进行1比特量化,并推导基于1比特信号的概率分布,建立了关于目标位置的代价函数;其次,通过证明代价函数的凸性,利用梯度下降算法求解了回波中未知的信号参数;最后,根据最大似然估计实现了目标直接定位。仿真实验分析了所提算法的定位性能,结果表明,所提算法仅需传输相较于高精度采样(16比特为例)直接定位算法6.25%的通信带宽,同时其功耗仅为前者的0.1%。此外,与基于1比特量化的二次定位算法相比,所提算法在低信噪比下便可实现对目标位置的有效估计,并且其定位性能在低信噪比和低MIMO天线数量下均明显优于前者。同时,其性能会随着过采样技术的应用进一步提升。      

基于WGS-84地球模型的三站时差定位

时差定位根据目标到达多个站之间的时间差进行定位。三站对地面目标的定位属于三站定位问题。借助四站时差定位原理,提出先用四站时差Chan算法求解定位初值,然后用牛顿迭代法求解精确解的算法。时差定位方程加入地球方程才能构成完整的方程组,但非线性方程组的求解是一个复杂的过程。仿真试验发现,粗解精解联合求解算法以比较良好的效果解决了三站三维定位问题。     

目标的位置信息场定位技术

对一种新的定位理念——目标位置信息场分析定位进行了阐述。新的定位理念认为,无论有源还是无源定位问题,均是研究怎样充分利用与目标位置相关的观测数据或观测参数所获得的信息,以及关于目标位置的先验信息,以获取目标的位置。这些信息构成了关于目标位置的信息场。从这个理念出发,可以建立更一般的定位概念模型,用以解决现在定位理论无法解决的目标数目未知、观测信息与目标关系不明确的定位问题。详细阐述了这种定位理念,并就空间二维测向多次观测定位、多站测向观测定位、空间多平台多目标测时差定位3种典型的无源观测情况,介绍了处理技术和处理效果,说明了这种处理技术的突出优势:可同时获得目标数目和多目标的精确位置。     

多机器人系统的可观测性及协同定位精度分析

多机器人协同系统的编队队形影响系统的可观测性,应用中发现系统绝对定位能力也是影响协同定位精度的主要因素。针对系统不可观测情况下无法实现协同定位问题,提出了通过增强系统绝对定位能力实现协同定位的方法,重点分析了不同绝对定位能力的多机器人协同定位系统的可观测性对定位精度的影响,确定了系统可观测性的最佳条件。以3个机器人组成的集群为例进行仿真验证了同一观测度情况下,系统的绝对定位能力越强,协同定位精度越高。     

基于连通度和加权校正的移动节点定位算法

移动节点定位问题是无线传感器网络中的研究重点。针对移动节点定位误差大的问题,提出一种基于连通度和加权校正的移动节点定位算法。在未知节点移动过程中,根据节点间连通度大小选取参与定位的信标节点,利用加权校正方法修正RSSI测距信息,然后用最小二乘法对未知节点进行位置估计。仿真分析表明,节点通信半径和信标密度在一定范围内,该算法表现出良好的定位性能,定位精度明显提升。     

基于分数阶傅里叶变换的多辐射源被动直接定位算法

直接定位(DPD)算法能充分利用观测回波信息,在低信噪比条件下其定位精度一般要高于传统的两步定位算法。为解决多基站无源雷达系统中多个未知线性调频(LFM)信号辐射源的定位问题,该文提出一种基于DPD算法和分数傅里叶变换(FRFT)相结合的多目标定位算法。首先,根据建立的信号模型推导了理论上最优的高维最大似然估计器;其次,由于高维信号参数和目标位置联合估计的计算复杂度限制,利用基于FRFT和基本分类算法的降维策略将多目标定位问题转化为多个单目标定位问题;最后,目标的位置及相应信号参数可通过4维网格搜索得到有效估计。仿真结果表明,相比于已存在的忽略发射信号的DPD算法,该文提出算法定位性能更优。      

利用多照射源的单站多目标无源定位

提出一种针对多目标的单站无源定位算法,综合利用多个外部照射信号,基于对信号多径时延的测量,在目标位置空间进行概率融合,将多目标定位问题转化为对一个目标函数的多峰寻优问题。该算法通过空间关联性实现参数自动配对,能依据单次观测数据对较大数量的散射体目标同时定位。     

单星多波束天线下基于压缩感知的多目标干扰定位

针对卫星干扰处理中的多目标定位问题,该文提出基于压缩感知的定位方法。该方法利用目标的空间稀疏性,以及多波束天线在不同信号源方向上的增益不同,仅需要测量接收信号强度便可实现多个干扰的位置识别。研究结果表明,定位性能与节点分布、目标个数、波束覆盖半径、判决门限有关。在给定参数及原对偶内点算法下,该方法可实现1～4个干扰源的空域定位,在信噪比为20 dB时定位精度达到7.7 km,优于经典的旋转干涉仪和空间谱估计测向方法。     

单星多波束天线下基于压缩感知的多目标干扰定位

针对卫星干扰处理中的多目标定位问题,该文提出基于压缩感知的定位方法.该方法利用目标的空间稀疏性,以及多波束天线在不同信号源方向上的增益不同,仅需要测量接收信号强度便可实现多个干扰的位置识别.研究结果表明,定位性能与节点分布、目标个数、波束覆盖半径、判决门限有关.在给定参数及原对偶内点算法下,该方法可实现1～4个干扰源的空域定位,在信噪比为20 dB时定位精度达到7.7 km,优于经典的旋转干涉仪和空间谱估计测向方法.     

Multi-objective Binary Reinforced Cuckoo Search Algorithm for Solving Connected Coverage target based WSN with Critical Targets

In the stream of WSN, covering the targets using sensors and communication among the sensors to forward the data packets is a prime challenge due to the sparse target locations. Dedicated sensors lead more installation cost and significant amount of maintenance needs to be charged. Coverage of multiple targets by few sensors leads to network failure in case if any sensor runs out of power. Targets in sparse region also should be considered into account while sensing the environment. Hence in this paper, an effective multi-objective connected coverage target based WSN algorithm is proposed namely Multi-Objective Binary Cuckoo Search algorithm. The proposed model also handles the critical targets in the given sensing region. The algorithms hold the potentiality to handle minimized sensor deployment, maximized coverage and connectivity cost simultaneously. The proposed model is compared with the state of art algorithms to prove its significance. Two dedicated simulation region is developed in a large scale to examine the efficiency of the proposed algorithm. The results shows the significance of the proposed model over existing algorithms.

     

Multi-target localization algorithm based on adaptive grid in wireless sensor network

The RSS-based multi-target localization has the natural property of the sparsity in wireless sensor networks.A multi-target localization algorithm based on adaptive grid in wireless sensor networks was proposed,which divided the multi-target localization problem into two phases:large-scale grid-based localization and adaptive grid-based localization.In the large-scale grid-based localization phase,the optimal number of measurements was determined due to the sequential compressed sensing theory,and then the locations of the initial candidate grids were reconstructed by applying l_p (0＆lt; p＆lt;1) optimization.In the adaptive grid-based localization phase,the initial candidate grids were adaptively partitioned according to the compressed sensing theory,and then the locations of the targets were precisely estimated by applying l_poptimization once again.Compared with the traditional multi-target localization algorithm based on compressed sensing,the simulation results show that the proposed algorithm has higher localization accuracy and lower localization delay without foreknowing the number of targets.Therefore,it is more appropriate for the multi-target localization problem in the large-scale wireless sensor networks.     

Seamless Routing and Cooperative Localization of Multiple Mobile Robots for Search and Rescue Application

In particular, for a practical mobile robot team to perform such a task as that of carrying out a search and rescue mission in a disaster area, the network connectivity and localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a Global Positioning System is unavailable. This paper proposes the new collective intelligence network management architecture of multiple mobile robots supporting seamless network connectivity and cooperative localization. The proposed architecture includes a resource manager that makes the robots move around and not disconnect from the network link by considering the strength of the network signal and link quality. The location manager in the architecture supports localizing robots seamlessly by finding the relative locations of the robots as they move from a global outdoor environment to a local indoor position. The proposed schemes assuring network connectivity and localization were validated through numerical simulations and applied to a search and rescue robot team.     

基于模糊空时线索的多目标在线跟踪算法

多目标在线跟踪是视频监控中的关键问题之一.针对日益增长的智能化视频监控的需求,提出了一种基于模糊空时线索的多目标在线跟踪算法.在该算法中,引入模糊空时多属性特征定义距离函数,利用模糊C均值聚类优化得到交叉隶属度矩阵,实现目标与观测间的数据关联.为了减少错误的轨迹起始,利用空时线索定义了遮挡度函数,判别出新目标并起始相应的目标轨迹.实验结果表明,本文算法能够准确地估计出目标的运动轨迹.本文算法可应用于视频监控、安防以及自动驾驶等领域.     

基于Chan算法的水下测控设备组网数据融合技术研究

依据水下测控设备组网系统的工作特点,为了有效挖掘现有测控设备的使用效能,构建高精度的综合性水下测控网络,该文提出基于Chan算法的水下测控设备组网集中式数据融合定位算法。该算法首先利用基于波达时间的加权最小二乘算法粗测目标位置,然后依据该目标位置和测量时延信息的关系,构造新的误差矢量,利用该误差矢量再次加权最小二乘估计解算目标位置。研究结果表明,该算法可实现多套水下测控设备的数据融合,可有效提高全域范围的定位精度,且精度高于纯基于波达时间的融合定位算法。     

无线传感器网络中基于压缩感知的动态目标定位算法

传统的动态目标定位算法需要采集、存储和处理大量数据,并不适用于能量受限的无线传感器网络。针对该缺陷,该文提出一种基于压缩感知的动态目标定位算法。该算法利用目标的运动规律设计稀疏表示基,从而将动态目标定位问题转化为稀疏信号恢复问题。针对传统观测矩阵难以实现的缺陷,该算法设计可实现且与稀疏表示基相关性低的稀疏观测矩阵,从而保证了算法的重构性能。该算法的特点是可利用较少的数据采集实现动态目标定位,从而大大延长无线传感器网络的寿命。仿真结果表明,该文所提出的基于压缩感知的动态目标定位算法具有较好的定位性能。     

一种多基地雷达抗距离欺骗干扰技术

针对距离多假目标欺骗干扰下多基地雷达系统,提出一种基于同源定位检验的抗欺骗干扰技术。首先,各接收站对获取到的目标角度量测进行预处理,划分出同一方位向上目标并进行数据压缩,以此提高同一方向上目标角度量测精度;同时,利用发射站和接收站组成的双基地雷达系统进行目标定位;最后,将各站定位结果进行同源定位检验,并融合检验后结果。仿真结果表明:该方法能有效抑制距离多假目标欺骗干扰,并能对多目标进行精确定位。     

一种自适应分裂与合并的运动目标聚类分割算法

针对智能监控系统中多个运动目标进行图像分割这一问题,该文提出一种自适应分裂与合并的多运动目标聚类分割算法。该算法首先利用视频图像的时域信息,通过样本方差进行背景建模,分割出包含多个运动目标的前景图像。然后定义了像素点的空间连通率,并设计一种利用中垂线分割法,对初始聚类进行自适应分裂与合并。在无需事先设定聚类分割数目的条件下,自组织迭代聚类算法能完成多运动目标的分割。实验结果证明该算法对多运动目标分割效果好,分割结果与人眼视觉的判断一致。利用空间连通信息使得算法迭代收敛速度快,具有良好的实时性。     

Multi‐target localization in wireless sensor networks: a compressive sampling‐based approach

This paper considers the problem of localizing a group of targets whose number is unknown by wireless sensor networks. At each time slot, to save energy and bandwidth resources, only part of sensor nodes are scheduled to activate to remain continuous monitoring of all the targets. The localization problem is formulated as a sparse vector recovery problem by utilizing the spatial sparsity of targets’ location. Specifically, each activated sensor records the RSS values of the signals received from the targets and sends the measurements to the sink node where a compressive sampling‐based localization algorithm is conducted to recover the number and locations of targets. We decompose the problem into two sub‐problems, namely, which sensor nodes to activate, and how to utilize the measurements. For the first subproblem, to reduce the effect of measurement noise, we propose an iterative activation algorithm to re‐assign the activation probability of each sensor by exploiting the previous estimate. For the second subproblem, to further improve the localization accuracy, a sequential recovery algorithm is proposed, which conducts compressive sampling on the least squares residual of the previous estimate such that all the previous estimate can be utilized. Under some mild assumptions, we provide the analytical performance bound of our algorithm, and the running time of proposed algorithm is given subsequently. Simulation results demonstrate the effectiveness of our algorithms.Copyright © 2013 John Wiley & Sons, Ltd.     

CRWSNP: cooperative range-free wireless sensor network positioning algorithm

Sensing events occur in an area without knowing the events locations, is meaningless. Since there is no priorly knowledge about the locations of most of the sensors which scattered randomly in an area, wireless sensor network localization methods try to find out where sensors are located. A new cooperative and distributed range-free localization algorithm, based on only connectivity information is proposed in this paper. The method first uses convex optimization techniques to find primitive target nodes locations estimation, then nodes cooperate with each other in several iterations to improve the whole network location estimation. CRWSNP converges after a finite number of iterations because of applying two novel heuristic location correction techniques. As well as, results of the algorithm have been compared with six range-free based methods like CPE, DV-hop, APIT; and CRWSNP algorithm provides more accurate results over 50 random topologies for the network, in mean error and maximum error metrics.