基于TDOAs与FDOAs的多信号源及感知节点联合定位算法

感知节点存在位置误差与速度误差的情况下,本文采用信号到达时间差(TDOAs)与到达频率差(FDOAs)信息,针对多信号源被动定位与感知节点进行位置及速度的同步优化问题进行了研究.在Sun和Ho前期工作中,只对多个不相关信号源进行了定位,并没有同时给出感知节点位置及速度的优化解,在很多实际应用中,对多个信号源进行被动定位的同时需要对感知节点的位置及速度信息进行优化;本文所提出的算法对前期算法进行了提升,被定位信号源与感知节点的位置及速度可同时较好地达到克拉美罗下界(CRLB);计算机仿真对本文理论推导进行了验证.     

An Approximately Efficient TDOA Localization Algorithm in Closed-Form for Locating Multiple Disjoint Sources With Erroneous Sensor Positions

This paper considers the problem of time difference-of-arrival (TDOA) source localization when the TDOA measurements from multiple disjoint sources are subject to the same sensor position displacements from the available sensor positions. This is a challenging problem and closed-form solution with good localization accuracy has yet to be found. This paper proposes an estimator that can achieve this purpose. The proposed algorithm jointly estimates the unknown source and sensor positions to take the advantage that the TDOAs from different sources have the same sensor position displacements. The joint estimation is a highly nonlinear problem due to the coupling of source and sensor positions in the measurement equations. We introduce the novel idea of hypothesized source locations in the algorithm development to enable the formulation of psuedolinear equations, thereby leading to the establishment of closed-form solution for source location estimates. Besides the advantage of closed-form, the newly developed algorithm is shown analytically, under the condition that the TDOA measurement noise and the sensor position errors are sufficiently small, to reach the CRLB accuracy. For clarity, the localization of two disjoint sources is used in the algorithm development. The developed algorithm is then examined under the special case of a single source and extended to the more general case of more than two unknown sources. The theoretical developments are supported by simulations.      

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。      

A Bayesian approach to auto-calibration for parametric array signalprocessing

A number of techniques for parametric (high-resolution) array signal processing have been proposed in the last few decades. With few exceptions, these algorithms require an exact characterization of the array, including knowledge of the sensor positions, sensor gain/phase response, mutual coupling, and receiver equipment effects. Unless all sensors are identical, this information must typically be obtained by experimental measurements (calibration). In practice, of course, all such information is inevitably subject to errors. Several different methods have been proposed for alleviating the inherent sensitivity of parametric methods to such modelling errors. The technique proposed in the present paper is related to the class of so-called auto-calibration procedures, but it is assumed that certain prior knowledge of the array response errors is available. This is a reasonable assumption in most applications, and it allows for more general perturbation models than does pure auto-calibration. The optimal maximum a posteriori (MAP) estimator for the problem at hand is formulated, and a computationally more attractive large-sample approximation is derived. The proposed technique is shown to be statistically efficient, and the achievable performance is illustrated by numerical evaluation and computer simulation     

传感器位置误差情况下基于多维标度分析的时差定位算法

传统时差定位方法一般是在假设传感器位置信息准确已知的前提下进行的.然而在实际情形中,传感器位置信息往往含有随机误差,这些误差会严重影响对目标的定位精度.针对这一问题,提出了一种传感器位置误差情况下的多维标度时差定位算法.首先利用传感器位置和时差构造对称标量积矩阵,然后利用子空间理论建立关于目标位置的伪线性方程,最后通过设计加权矩阵来减少传感器位置误差对目标定位精度的影响.采用一阶小噪声扰动理论求出了目标位置估计的偏差及协方差矩阵,并通过仿真实验验证了该算法的有效性.     

Probabilistic data association techniques for target tracking in clutter

In tracking targets with less-than-unity probability of detection in the presence of false alarms (FAs), data association-deciding which of the received multiple measurements to use to update each track-is crucial. Most algorithms that make a hard decision on the origin of the true measurement begin to fail as the FA rate increases or with low observable (low probability of target detection) maneuvering targets. Instead of using only one measurement among the received ones and discarding the others, an alternative approach is to use all of the validated measurements with different weights (probabilities), known as probabilistic data association (PDA). This paper presents an overview of the PDA technique and its application for different target tracking scenarios. First, it describes the use of the PDA technique for tracking low observable targets with passive sonar measurements. This target motion analysis is an application of the PDA technique, in conjunction with the maximum-likelihood approach, for target motion parameter estimation via a batch procedure. Then, the PDA technique for tracking highly maneuvering targets and for radar resource management is illustrated with recursive state estimation using the interacting multiple model estimator combined with PDA. Finally, a sliding window (which can also expand and contract) parameter estimator using the PDA approach for tracking the state of a maneuvering target using measurements from an electrooptical sensor is presented.     

Application of maximum likelihood estimation to radar imaging

An efficient maximum likelihood (ML) estimator to obtain the scattering center locations of a target and the relative scattering level of these scattering centers from the scattered field data is described. In the proposed method, ML estimation is carried out in the image domain rather than in the frequency-aspect domain. A two-dimensional (2-D) inverse Fourier transform is used to transfer the scattered field data from frequency-aspect domain to the image domain (down-range/cross-range). As expected, the scattered field data in the image domain has some regions with high energy. The samples in the high-energy regions are used to obtain the initial guess for the ML estimator as well as for ML estimation. The ML estimator in the image domain is applied to both simulated and experimental scattered fields of some targets     

多光电跟踪设备异步序贯分布式目标跟踪算法

针对多光电跟踪设备组网后出现的异步测量问题，提出了一种异步分布式序贯目标跟踪算法。该算法由局部滤波器和融合滤波器构成，先利用状态转换方法，将多光电跟踪设备节点及其邻节点的异步测量对齐到融合时刻，得到拟测量方程。随后，利用射影原理对拟测量方程和目标运动状态方程构成的目标跟踪系统，提出异步序贯局部滤波器来计算较为精确的局部滤波值。再以协方差交叉算法为基础，提出基于扩散策略的融合滤波器，对局部估计值进行融合计算，来提高目标跟踪精度，并降低组网后各光电跟踪设备节点融合估计值的差异程度。最后对所提出的算法进行了仿真实验，以验证其有效性。     

Estimation of sensor gain and phase

The problem of estimating the direction-independent gain and phase characteristics of an array of sensors, using knowledge of the true field covariance at the sensor locations, is considered. A concise expression for the log-likelihood function is derived and several mathematical properties of this objective function are given. The Cramer-Rao (C-R) lower bounds on the variances of gain and phase estimates are derived, with the plane wave in isotropic noise considered as a special case. The maximum-likelihood estimates are shown to be consistent, asymptotically efficient and asymptotically normal. A simple estimator is proposed which is consistent and which gives good initial estimates for a Newton algorithm for finding the maximum-likelihood solution. Comparison of the maximum-likelihood estimates and the C-R bounds is given for the plane-wave-in-isotropic-noise example     

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.     

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.     

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.     

Maximum-likelihood source localization and unknown sensor locationestimation for wideband signals in the near-field

In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator     

观测最优分配的GM-PHD多目标跟踪算法

概率假设密度滤波器将目标的状态空间及观测空间描述为随机有限集合的形式,有效避免了多目标跟踪中复杂的数据关联问题。但对于不同类型的目标使用同样的全部观测数据集进行目标状态更新,未对观测数据进行合理分配,导致估计性能下降。该文提出一种观测最优分配的高斯混合概率假设密度多目标跟踪算法(MOA-GM-PHD),将目标分为已有目标和新生目标两类,推导极大似然门限来获得两类目标对应的最优观测数据,再分别进行目标状态更新。实验结果表明,该文方法目标跟踪效果优于传统GM-PHD滤波器。      

Practical adaptive notch filter for missile bending mode rejection

A practical adaptive notch filter based on the /spl Hscr//sub /spl infin// bending frequency estimator is proposed to remove the time-varying structural mode of a missile from the rate sensor measurements. Simulation results using flight test data show the reliable bending frequency estimation performance with this new technique.     

Data fusion for ballistic targets tracking using least squares

The paper deals with the problem of state estimation of continuous-time nonlinear system using discrete-time measurements from multiple sensors. In particular, the problem of multi-radar tracking of artillery ballistic objects is considered. A batch estimator based on the iterative least squares approach is developed using simplified and accurate models of ballistic flight. The estimator is applied to process the sequences of measurements from radars tracking the same ballistic target. Estimates of the target state over time are computed and their accuracy is compared to the estimates yielded by the extended Kalman filter. Partial estimates from multiple radars are combined using track fusion approach and propagated using the 3 degree of freedom model of ballistic flight. Accuracy of target's firing point estimation is also analysed with respect to the data rates and locations of the radars with respect to the target. Practical aspects of the proposed method are also discussed.     

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.

     

基于概率假设密度滤波法的3D目标位置跟踪

为了实现3D目标位置跟踪,提出了一种基于概率假设密度(PHD)滤波的跟踪方案。方案由2个阶段构成：单视图跟踪阶段和多摄像机融合阶段。单视图跟踪阶段,在时刻k每个摄像机上得到颜色观测值,采用高斯混合概率假设密度(GMPHD)滤波器估计出2D目标位置;多摄像机融合阶段,将得到的目标的2D估计值集合作为数据融合阶段的观测值,并通过GMPHD滤波器估计出目标的3D位置,从而避免观测值与目标状态之间的数据关联。仿真实验结果表明,提出的跟踪方案不但能够可靠地跟踪3D目标位置,而且能够解决在每个摄像机处目标的遮挡问题。     

A Cooperative Localization Algorithm for UWB Indoor Sensor Networks

This paper deals with range-based localization in ultra wideband sensor networks, allowing for the possibility of large range measurement errors because of a failure to detect the direct paths between some nodes. A novel algorithm is proposed that uses only partial knowledge of the service area topology, particularly of the positions of objects which are capable of causing undetected direct path (UDP) propagation conditions. Although the spirit of the proposed approach, because of the lack of information on the range error statistics, is to remove measurements performed under UDP conditions from the computation of the location estimate, these measurements are used implicitly by the algorithm to contribute to the erroneous trial locations being discarded. A cooperative stage is included that allows the probability of localization of a target with an insufficient initial number of accurate range measurements to increase. The proposed algorithm outperforms a variety of alternative positioning techniques, and thus illustrates the capability of this topology knowledge to mitigate the UDP problem, even in the absence of any knowledge about the range error statistics.     

Efficient closed-form solution for target localization using TDOA measurements in the presence of sensor position errors

An efficient solution for locating a target was proposed, which by using time difference of arrival (TDOA) measurements in the presence of random sensor position errors to increase the accuracy of estimation. The cause of position estimation errors in two-stage weighted least squares (TSWLS) method is analyzed to develop a simple and effective method for improving the localization performance. Specifically, the reference sensor is selected again and the coordinate system is rotated according to preliminary estimated target position by using TSWLS method, and the final position estimation of the target is obtained by using weighted least squares (WLS). The proposed approach exhibits a closed-form and is as efficient as TSWLS method. Simulation results show that the proposed approach yields low estimation bias and improved robustness with increasing sensor position errors and thus can easily achieve the Cramer-Rao lower bound (CRLB) easily and effectively improve the localization accuracy.