基于质心迭代估计的无线传感器网络节点定位算法

针对无线传感器网络非测距定位方法的应用,提出了基于质心迭代估计的节点定位算法.该算法首先计算当前连通信标节点所围成的平面质心的坐标及其与未知节点间的接收信号强度,然后用计算所得质心节点替代距离未知节点最远的连通信标节点,缩小连通信标节点所围成的平面,并通过多次迭代的方法提高节点定位精度.仿真实验结果表明,该算法的各项指标均为良好,适用于无线传感器网络的节点定位.     

基于EMD的单通道信源数估计方法

为了获取单通道接收信号的信源数目,针对普通信源数估计方法不能直接用于单通道接收信号的问题,提出了基于经验模态分解(empirical mode decomposition, EMD)的信源数估计方法。将单通道信号通过EMD处理,得到多个固有模态函数(intrinsic-mode function, IMF),据此构造数据协方差矩阵。对所构造的协方差矩阵进行特征值分解,采用基于信息论的AIC和MDL准则估计信源数。为进一步提高算法估计性能,引入对角加载技术对矩阵特征值进行平滑处理。仿真实验结果表面,本文提出的方法能够适用于单通道信源数估计,对角加载技术能够显著提高算法检测性能。     

基于RSSI的无线传感器网络定位系统设计与实现

基于窄带无线信号的路径损耗和阴影衰落,直接建立多个锚节点接收信号强度指示(Received Signal Strength Indication, RSSI)值与待定位节点未知坐标估计量的解析关系,避免传统RSSI定位方法中常用的对两节点距离量的直接求解,减少信息丢失,提高定位精度。仿真分析了锚节点数量、遮挡因子、路径损耗指数等对定位精度的影响。采用CC2530无线传感芯片实现基于RSSI的无线传感器网络(Wireless Sensor Network, WSN)定位系统。系统采用8个锚节点分别在边长4米和10米的两个正方形区域内展开定位实际测试,结果显示其平均定位误差可分别降到0.175米和0.824米。     

深海声影区时频谱干涉结构与声源定位

被动声呐探测位于深海声影区的水面舰船辐射噪声时,接收信噪比通常较低,导致声源被动定位方法的性能较差。针对这一问题,提出一种利用接收信号时频谱干涉结构的声源距离和径向速度联合估计方法。首先根据射线声学理论,建立时频谱沿频率轴和时间轴的干涉条纹周期与声源距离和径向速度的关系,然后对接收信号时频谱进行二维傅里叶变换和多频带处理以估计上述干涉条纹周期,最后解算声源距离和径向速度。仿真和海试数据处理结果表明,相比于现有利用接收信号自相关的声源距离估计方法,该文利用时频谱二维傅里叶变换的声源定位方法具有较好的稳健性,比较适用于低信噪比条件下的声源被动定位。     

分布式矢量传感器对宽带源被动定位的方法研究

本文给出了两种基于矢量传感器的被动定位方法。一般来说，对目标进行被动定位可以通过方位信息或时延信息来实现。单个矢量传感器就可以实现目标方位估计，而对多个矢量传感器接收的信号进行互相关运算即可得到目标对各矢量传感器的时延信息。本文先给出了利用方位信息进行被动定位的方法，然后给出了利用分布式矢量传感器进行方位一时延信息联合被动定位的新方法，并通过仿真分析对这两种方法的定位性能进行了比较。     

基于RSSI测距的传感器节点质心定位修正算法

针对传感器网络节点定位精度问题,研究基于RSSI测距的定位算法,提出多信标节点质心定位修正算法,通过该算法计算得到多组未知节点估计坐标,并在此基础上利用质心定位修正算法计算节点坐标修正值;利用仿真实验,证明基于RSSI测距的传感器节点质心定位算法定位精度比传统质心定位算法定位精度提高13.8%,比RSSI加权质心定位算法提高6.3%。     

滞后估计算法在手机网络失效检测中的应用

对手机网络失效检测是一个新的研究课题。手机网络的失效往往没有明显特征,内部体现为消息的滞后性;根据这一特性,提出一种契比雪夫滞后估计算法并应用到手机网络失效检测中,给出了算法的原理,并根据手机网络传递消息滞后时间,带入算法进而估计下一次信号消息到达的滞后时间估计值,根据手机网络中消息到达的时间间隔,代入算法估计下一个数据消息可能到达的滞后间隔;设置下一个信号消息到达时刻计时器的超时值,将低于该超时值的滞后时间间隔所对应的手机网络消息节点看成失效节点,实现了对手机网络失效的准确检测;实验及分析说明该算法的估计结果同实际手机运行情况匹配度超过85%,具有较高的鲁棒性。     

基于广义似然比判决的混沌信号重构方法

由于传感器网络的自身特征和节点的资源受限,使得对观测信号的处理必须考虑量化和能耗等因素,而引入的量化噪声同时增加了系统整体噪声的复杂性.针对传感器网络中整体噪声统计特性难以准确数学建模的特点,提出了一种基于代价参考粒子滤波的混沌信号重构算法.算法采用容积点变换以获得相对准确的更新粒子,并将局部重构信号的代价增量构建为广义似然比函数,用来选择传感器网络中的有效工作节点.仿真结果表明:所提算法可实现混沌信号的有效重构,且在噪声统计特性未知时性能要优于容积卡尔曼粒子滤波算法;算法同时能够通过选择不同的广义似然比阈值,实现网络能耗和重构精度的折中.     

基于抛物方程的海陆环境信号时延与到达角估计

针对复杂海陆环境中的无线信号传播预测问题,研究了适用于抛物方程的信号时延与到达角估计方法。将自由空间中抛物方程轴向波前信号视为本地副本信号,然后利用信号的自相关特性,将接收信号与副本信号进行互相关运算,最后通过相关函数的峰值检索,得到脉冲信号在复杂环境中传播的附加时延。采用数值算例,验证了该方法的正确性和有效性。此外,采用多重信号分类算法,由抛物方程构建接收阵列的协方差矩阵,并对其进行特征值分解,然后利用信号子空间和噪声子空间的正交性,实现复杂环境中的信号到达角估计。仿真结果表明,相比于传统的平面波谱方法,该方法具有更高的多径分辨率。基于上述方法,并结合数字地图,在典型的海陆环境中进行了仿真实验,分析了蒸发波导对脉冲信号传播时延和到达角的影响。     

深海脉冲信号簇到达结构特征及其在水下声源定位中的应用

为了实现对深海水下声源的定位,对典型深海环境中的脉冲信号到达结构特征进行了理论分析,给出了声源和接收位置位于近海面时到达信号的簇信号形式近似表达式。当声源和接收位置处于近海面深度时,接收到的信号呈簇状结构形式。在提取到达信号中各簇信号到达时间、幅值等特征参数的基础上,提出了一种通过对到达信号中簇信号特征参数匹配搜索进行水下脉冲信号定位的方法,仿真分析了不同距离和不同深度处的簇信号特征,并利用一次南海海域爆炸声源的声传播实验数据进行了实验验证。结果表明:各簇信号对应的到达时间、幅值等特征参数可用于对声源位置的匹配定位;海上实验中利用单水听器得到的水下声源的距离估计结果与实测距离结果较为一致,对实验中2.0 km至90.4 km内声源的距离估计误差不大于8%。      

Turboprop and rotary-wing aircraft flight parameter estimation using both narrow-band and broadband passive acoustic signal-processing methods

Flight parameter estimation methods for an airborne acoustic source can be divided into two categories, depending on whether the narrow-band lines or the broadband component of the received signal spectrum is processed to estimate the flight parameters. This paper provides a common framework for the formulation and test of two flight parameter estimation methods: one narrow band, the other broadband. The performances of the two methods are evaluated by applying them to the same acoustic data set, which is recorded by a planar array of passive acoustic sensors during multiple transits of a turboprop fixed-wing aircraft and two types of rotary-wing aircraft. The narrow-band method, which is based on a kinematic model that assumes the source travels in a straight line at constant speed and altitude, requires time-frequency analysis of the acoustic signal received by a single sensor during each aircraft transit. The broadband method is based on the same kinematic model, but requires observing the temporal variation of the differential time of arrival of the acoustic signal at each pair of sensors that comprises the planar array. Generalized cross correlation of each pair of sensor outputs using a cross-spectral phase transform prefilter provides instantaneous estimates of the differential times of arrival of the signal as the acoustic wavefront traverses the array.     

Regularized matched-mode processing for source localization

This paper develops a new approach to matched-mode processing (MMP) for ocean acoustic source localization. MMP consists of decomposing far-field acoustic data measured at an array of sensors to obtain the excitations of the propagating modes, then matching these with modeled replica excitations computed for a grid of possible source locations. However, modal decomposition can be ill-posed and unstable if the sensor array does not provide an adequate spatial sampling of the acoustic field (i.e., the problem is underdetermined). For such cases, standard decomposition methods yield minimum-norm solutions that are biased towards zero. Although these methods provide a mathematical solution (i.e., a stable solution that fits the data), they may not represent the most physically meaningful solution. The new approach of regularized matched-mode processing (RMMP) carries out an independent modal decomposition prior to comparison with the replica excitations for each grid point, using the replica itself as the a priori estimate in a regularized inversion. For grid points at or near the source location, this should provide a more physically meaningful decomposition; at other points, the procedure provides a stable inversion. In this paper, RMMP is compared to standard MMP and matched-field processing for a series of realistic synthetic test cases, including a variety of noise levels and sensor array configurations, as well as the effects of environmental mismatch.     

The two-dimensional source location problem for time differences of arrival at minimal element monitoring arrays

The time difference of arrival (TDOA) source localization inverse problem is analyzed for two-dimensional signal propagation detected by a small number of sensor elements in a monitoring array. Nonlinear least-squares solutions are found based on the assumptions of geodesic rays propagating at constant speed. The two-dimensional (2D) TDOA source location problem is shown in the case of three sensors to have dual possible solutions for some combinations of arrival time differences. In the case of four non-collinear sensors, there are unique solutions for all physically possible combinations of time differences. Dual solutions to the three-sensor problem are associated with a small range of arrival time differences but large regions in physical space. The locations of the dual solutions are separated by a wide variety of distances, which in some cases prevent the use of alternative reasoning to remove the ambiguity. Three-sensor TDOA cannot be reliably used for 2D source location unless the source is a priori known to be within either the spatial region spanned by the sensor array or the external zones of unique solution. Determining the minimum number of sensors necessary to unambiguously solve the source location problem assists in cost-effective design of sensor arrays.     

基于误差修正的传感器阵列水声定位算法

本文分析了海洋环境中定位精度下降的根源,提出了一种基于误差修正的传感器阵列自主定位算法,并进行了计算机仿真和数据分析,结果表明该算法对于提高传感器阵列自主定位精度是有效的。     

Active noise control with linear control source and sensor arrays for a noise barrier

A study is conducted on minimizing the sum of the squared acoustic pressures with a linear array of control sources and a perpendicular linear array of error sensors, placed above the top of a noise barrier. Particular angular orientations, with respect to the center of the barrier top, and spacings of the linear arrays of control sources and error sensors result in moderate to significant additional reduction of the acoustic pressure in the shadow zone. Visual inspection of the sound pressure field, with and without active noise control, found that uniform and significant additional insertion loss can be generated near the barrier. Numerical simulations were conducted to test the proposed method. For separations between control sources and error sensors much less than a quarter wavelength of the primary noise disturbance, results show that the angular orientation, of the combined linear control source and sensor arrays, is a weak factor for acoustic pressure reduction in the shadow zone. Weak angle dependence serves as an advantage to the proposed method, which yields uniform performance for any angular orientation. An angular orientation involving the alignment of the furthest error sensor with the first diffracting edge of the barrier and the primary source was observed to perform well for a variety of frequencies, since the spacing between error sensors and between control sources is of the order of a quarter-wavelength. Improved noise control in the shadow zone of a barrier is achieved by the use of two control sources and angular orientation as mentioned above. Further spatial extension of the area of reduced acoustic pressure is possible by utilizing an increased number of control sources.     

Eigenspace-based source number estimation for direction-of-arrival estimation

A eigenspace-based source number estimation was presented.It projects the estimated covariance matrix of array signal into signal eigen-subspace and noise eigen-subspace, respectively.Using the orthogonality between signal eigen-subspace and noise eigen-subspace, it is easy to differentiate the contribution of signal and noise by using the criterion value,or the magnitude of projection.Like the Direction-of-Arrival(DOA) estimation algorithm,the estimator uses the eigenvalue decomposition of covariance matrix with order M×M,where M is the number of elements,and hence can save much computational burden.To reduce more computational burden,the estimation can be implemented by the decomposition in the real-value space.Computer simulation demonstrates the distribution of criterion value and the performance of the estimation method.The estimation method was also tested with the sonar data,which shows good performances.     

Fast implementation of sparse iterative covariance-based estimation for source localization

Fast implementations of the sparse iterative covariance-based estimation (SPICE) algorithm are presented for source localization with a uniform linear array (ULA). SPICE is a robust, user parameter-free, high-resolution, iterative, and globally convergent estimation algorithm for array processing. SPICE offers superior resolution and lower sidelobe levels for source localization compared to the conventional delay-and-sum beamforming method; however, a traditional SPICE implementation has a higher computational complexity (which is exacerbated in higher dimensional data). It is shown that the computational complexity of the SPICE algorithm can be mitigated by exploiting the Toeplitz structure of the array output covariance matrix using Gohberg-Semencul factorization. The SPICE algorithm is also extended to the acoustic vector-sensor ULA scenario with a specific nonuniform white noise assumption, and the fast implementation is developed based on the block Toeplitz properties of the array output covariance matrix. Finally, numerical simulations illustrate the computational gains of the proposed methods.     

A target group tracking algorithm for wireless sensor networks using azimuthal angle of arrival information

<正>In this paper,we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network.Since a group of targets moves collectively and is restricted within a limited region,it is not worth consuming scarce resources of sensors in computing the trajectory of each single target.Hence,in this paper,the problem is modeled as tracking a geographical continuous region covered by all targets.A tracking algorithm is proposed to estimate the region covered by the target group in each sampling period.Based on the locations of sensors and the azimuthal angle of arrival(AOA) information,the estimated region covering all the group members is obtained.Algorithm analysis provides the fundamental limits to the accuracy of localizing a target group.Simulation results show that the proposed algorithm is superior to the existing hull algorithm due to the reduction in estimation error,which is between 10%and 40%of the hull algorithm,with a similar density of sensors.And when the density of sensors increases,the localization accuracy of the proposed algorithm improves dramatically.