无线传感器网络中基于多比特量化数据的滚动时域状态估计

该文基于多比特的量化策略,提出了无线传感器网络中多比特分布式滚动时域状态估计算法。每个传感器节点预先设定一个包含多个阈值的阈值簿,利用这个阈值簿将观测值量化成多比特,融合中心接收这些比特信息运用滚动时域的思想得到系统的状态估计值,与预期相同。仿真结果表明阈值簿中阈值个数越多则估计的结果会越精确。与单比特滚动时域状态估计方法相比,该方法避免了每一时刻传感器节点接收融合中心的反馈状态估计值用来设计阈值,并且在多比特信息下状态估计值的精度更高。     

基于概率稀疏随机矩阵的压缩数据收集方法

测量矩阵设计是应用压缩感知理论解决实际问题的关键。该文针对无线传感器网络压缩数据收集问题设计了一种概率稀疏随机矩阵。该矩阵可在减少参与投影值计算节点个数的同时,让参与投影值计算的节点分布集中化,从而降低数据收集的通信能耗。在此基础上,为提高网络数据重构精度,又提出一种适用于概率稀疏随机矩阵优化的测量矩阵优化算法。仿真实验结果表明,与稀疏随机矩阵和稀疏Toeplitz测量矩阵相比,采用优化的概率稀疏随机矩阵作为压缩数据收集的测量矩阵可显著降低通信能耗,且重构误差更小。     

基于格拉布斯准则和改进粒子滤波算法的水下传感网目标跟踪

水下无线传感网络(UWSN)执行目标跟踪时,因为各个传感器节点测量值对目标状态估计的贡献不一样以及节点能量有限,所以探索一种好的节点融合权重方法和节点规划机制能够获得更好的跟踪性能。针对上述问题,该文提出一种基于Grubbs准则和互信息熵加权融合的分布式粒子滤波(PF)目标跟踪算法(GMIEW)。首先利用Grubbs准则对传感器节点所获得的信息进行分析检验,去除干扰信息和错误信息。其次,在粒子滤波的重要性权值计算的过程中,引入动态加权因子,采用传感器节点的测量值与目标状态之间的互信息熵,来反映传感器节点提供的目标信息量,从而获得各个节点相应的加权因子。最后,采用3维场景下的簇-树型网络拓扑结构,跟踪监测区域内的目标。实验结果显示,该算法可有效提高水下传感器网络测量数据对目标跟踪预测的准确度,降低跟踪误差。     

传感器网络的粒子群优化定位算法

无线传感器网络定位问题是一个基于不同距离或路径测量值的优化问题。由于传统的节点定位算法采用最小二乘法求解非线性方程组时很容易受到测距误差的影响,为了提高节点的定位精度,将粒子群优化算法引入到传感器网络定位中,提出了一种传感器网络的粒子群优化定位算法。该算法利用未知节点接收到的锚节点的距离信息,通过迭代方法搜索未知节点位置。仿真结果表明,该算法有效地抑制了测距误差累积对定位精度的影响,提高了节点的定位精度。     

基于稀疏重建的穿墙雷达墙体参数估计方法

未知墙体参数的估计对穿墙雷达(TWR)的应用具有重要意义。时延估计(TDOE)方法能够有效获取被测均匀墙体参数。对于TDOE 方法,墙面反射回波时延估计的精度直接影响墙体参数估计的准确性。文中基于墙体回波的稀疏性提出了一种基于稀疏重建的墙体参数估计方法,通过利用正交匹配追踪(OMP)稀疏重建算法对每个双基地天线间隔下墙体回波信号进行时延估计,然后基于双基地和单基地混合测量模式采用TDOE 方法可以获得墙体的相对介电常数、厚度和电导率。仿真和实验结果表明所提出的估计方法能够提供更高精度的墙体参数估计。     

无线Ad Hoc网络基于泰勒级数展开的节点定位技术

无线自组织网络（Ad Hoc）节点的位置信息提供路由选择及节点查询等功能。节点信号的到达时间（TOA）、到达时间差（TDOA）、信号到达入射角（AOA）和信号强度的测量值已被用作移动节点相对于参考信标的定位。对Ad Hoc网络节点定位进行了研究,基于所有节点均能提供TOA/TDOA测量值的假定,提出一种仅通过部分有定位能力节点就可计算所有节点在Ad-Hoc网络中位置的方法。在此基础上,使用泰勒级数展开算法和二次WLS估计求解非线性定位方程组,以获得更高的节点定位精度。     

一种基于El Gamal加密算法的压缩数据收集方法

压缩感知技术在信号处理、图像处理、数据收集与分析等方面有很大优势,是近年来的研究热点。研究了如何安全高效地运用压缩感知技术来收集无线传感器网络中的数据。传统的基于压缩感知技术的数据收集方法并不考虑数据收集的安全性,而且网络内的所有节点都会参与每个测量值的收集。将El Gamal加密算法和基于稀疏随机矩阵的压缩感知技术相结合,提出了一种基于El Gamal加密算法的稀疏压缩数据收集方法（El Gamal based sparse compressive data gathering,ESCDG）。理论分析和数值实验表明,ESCDG不仅能降低网络资源的消耗而且能抵御多项式算力的内部攻击和外部攻击。     

基于矢量传感器阵列的信号多参数估计方法

本文提出了一种基于非均匀L形矢量传感器（Vector-Sensor,VS）阵列的横平面电磁波信号频率、二维波达方向（2-D DOA）和极化参数估计的方法.为降低系统实现复杂度,文中采用了对信号欠Nyquist采样.为了降低不同频率信号在传感器间的互耦,阵列中各VS稀疏排布,同时,空间参数的估计精度也得到相应的提高.最后通过数值仿真验证了本文算法的有效性.     

认知WSN中基于能量有效性自适应观测的梯度投影稀疏重构方法

针对认知无线传感器网络中传感器节点侧的模拟信息转换器对本地感知数据进行稀疏表示与压缩测量,该文提出一种基于能量有效性观测的梯度投影稀疏重构(GPSR)方法。该方法根据事件区域内认知节点对实际感知到的非平稳信号空时相关性结构,映射到小波正交基级联字典进行稀疏变换,通过加权能量子集函数进行自适应观测,以能量有效的方式获取合适的观测值,同时对所选观测向量进行正交化构造测量矩阵。汇聚节点采用GPSR算法进行自适应压缩重构。仿真比较了GPSR自适应重构与正交匹配追踪(OMP)重构算法。仿真结果表明,在压缩比小于0.2的区域内,基于能量有效性观测的GPSR自适应重构效果优于传统随机高斯测量信号重构。在相同节点数情况下,GPSR自适应压缩重构方法在低信噪比区域内具有较小的重构均方误差,且该方法所需观测数明显低于随机高斯观测,同时有效保障了感知节点的能耗均衡。     

基于RSSI测距的无线传感器网络定位算法

提出了一种采用最小二乘法对环境参数进行拟合的方法,获得损耗模型,同时对所测得的RSSI数据进行高斯处理并优选信标节点,最后对目标节点采用改进后的三边测量定位算法进行节点定位.实验结果表明,本算法定位受环境因素影响减小,比传统RSSI定位算法精度更高,可应用于无线传感器网络中.     

Energy Planning for Progressive Estimation in Multihop Sensor Networks

Multihop sensor networks where transmissions are conducted between neighboring sensors can be more efficient in energy and spectrum than single-hop sensor networks where transmissions are conducted directly between each sensor and a fusion center. With the knowledge of a routing tree from all sensors to a destination node, we present a digital transmission energy planning algorithm as well as an analog transmission energy planning algorithm for progressive estimation in multihop sensor networks. Unlike many iterative consensus-type algorithms, the proposed progressive estimation algorithms along with their transmission energy planning further reduce the network transmission energy while guaranteeing any pre-specified estimation performance at the destination node within a finite time. We also show that digital transmission is more efficient in transmission energy than analog transmission if the available transmission time-bandwidth product for each link and each observation sample is not too limited.     

Distributed Estimation in Energy-Constrained Wireless Sensor Networks

In this paper, we consider distributed estimation of a noise-corrupted deterministic parameter in energy-constrained wireless sensor networks from energy-distortion perspective. Given a total energy budget allowable to be used by all sensors, there exists a tradeoff between the subset of active sensors and the energy used by each active sensor in order to minimize the estimation MSE. To determine the optimal quantization bit rate and transmission energy of each sensor, a concept of equivalent unit-energy MSE function is introduced. Based on this concept, an optimal energy-constrained distributed estimation algorithm for homogeneous sensor networks and a quasi-optimal energy-constrained distributed estimation algorithm for heterogeneous sensor networks are proposed. Moreover, the theoretical energy-distortion performance bound for distributed estimation is addressed and it is shown that the proposed algorithm is quasi-optimal within a factor 2 of the theoretical lower bound. Simulation results also show that the proposed method can achieve a significant reduction in the estimation MSE when compared with other uniform schemes. Finally, the proposed algorithm is easy to implement in a distributed manner and it adapts well to the dynamic sensor environments.     

基于APF的二元WSN运动目标跟踪算法

针对传感节点计算、通信及资源受限的特点,引入二元WSN模型,提出了一种基于辅助粒子滤波(APF)的集中式算法,以实现运动目标的实时跟踪。由于每个二元传感器只对目标是否进入其感知区域做出反应(向数据融合中心报告0或1),粒子滤波算法的复杂运算集中在融合中心完成,因此节点结构简单、通信代价低廉,有助于延长监测网络的生存周期。仿真实验结果表明,该算法对随机部署和规则部署的两种方案,均具有良好的跟踪性能,能满足一般机动目标实时跟踪的应用要求。     

一种基于平行稀疏阵列虚拟孔洞填充的二维DOA估计算法

采用稀疏阵列进行波达方向（Direction of Arrival,DOA）估计时往往会产生虚拟孔洞，它严重限制了阵列孔径的扩展与阵元自由度的提升。由于孔洞位置与初始阵列阵元数目、排布方式有关，故较难对其进行预填充。为此，提出了一种基于平行稀疏阵列虚拟孔洞填充的二维DOA估计算法，利用双稀疏线阵扩展生成两个不同的虚拟阵列，并利用其中一阵的信息去填充另一阵的孔洞。为尽可能减少总阵元数目，采用提前计算的孔洞位置去设计另一阵列的排布规则，并通过求根多重信号分类(Root-Mutiple Signal Classification，Root-MUSIC）算法替代传统的二维谱峰搜索算法完成对入射角度的估计与自动匹配。实验仿真结果验证了所提算法相比传统算法能以更少的阵元获得更高的估计精度。     

稀疏贝叶斯字典学习空时机动目标参数估计算法

针对基于稀疏恢复的空时自适应处理(STAP)目标参数估计方法中字典失配导致估计性能下降的问题,该文提出一种基于稀疏贝叶斯字典学习的高精度目标参数估计方法。该方法首先通过目标方位信息补偿多个阵元数据构建联合稀疏恢复数据,然后对补偿后的每个阵元数据利用双线性变换进行加速度和速度项分离。最后构建速度参数和加速度参数的泰勒级数动态字典,对机动目标参数进行高精度贝叶斯字典学习稀疏恢复。仿真实验证明,该方法能有效提高字典失配情况下目标参数估计精度,估计性能优于已有字典固定离散化的稀疏恢复空时目标参数估计方法。     

One-bit compressed sensing recovery algorithm robust to perturbation

One-bit compressed sensing(CS) technology reconstructs the sparse signal when the available measurements are reduced to only their sign-bit. It is well known that CS reconstruction should know the measurement matrix exactly to obtain a correct result. However, the measurement matrix is probably perturbed in many practical scenarios. An iterative algorithm called perturbed binary iterative hard thresholding (PBIHT) is proposed to reconstruct the sparse signal from the binary measurements (sign measurements) where the measurement matrix experiences a general perturbation. The proposed algorithm can reconstruct the original data without any prior knowledge about the perturbation. Specifically, using the ideas of the gradient descent, PBIHT iteratively estimates signal and perturbation until the estimation converges. Simulation results demonstrate that, under certain conditions, PBIHT improves the performance of signal reconstruction in the perturbation scenario.     

Coverage in Hybrid Mobile Sensor Networks

This paper considers the coverage problem for hybrid networks which comprise both static and mobile sensors. The mobile sensors in our network only have limited mobility, i.e., they can move only once over a short distance. In random static sensor networks, sensor density should increase as O(log L + k log log L) to provide k-coverage in a network with a size of L. As an alternative, an all-mobile network can provide k-coverage with a constant density of O(k), independent of network size L. We show that the maximum distance for mobile sensors is O( 1/sqrt(k) log^(4/3)(kL)). We then propose a hybrid network structure, comprising static sensors and a small fraction of O( 1/sqrt(k)) of mobile sensors. For this network structure, we prove that k-coverage is also achievable with a constant sensor density of O(k). Furthermore, for this hybrid structure, we prove that the maximum distance which any mobile sensor has to move is bounded as O(log^(3/4)L). We then propose a distributed relocation algorithm, where each mobile sensor only requires local information in order to optimally relocate itself. We verify our analysis via extensive numerical evaluations and show an implementation of the mobility algorithm on real mobile sensor platforms.     

WSN中基于稀疏观测和异步传输的分布式实时定位算法

针对无线传感器网络（WSN）中现有集中式多维标度（MDS-MAP）节点定位算法在定位精度和分布式方面的不足,提出一种基于稀疏观测和异步传输的分布式实时定位算法。首先在传统MDS-MAP算法中融入稀疏观测机制,使其能够更好地符合实际观测场景;然后提出一种异步传输序列,使节点能够分布式计算距离观测,并通过分布式计算结果给出位置估计;最后通过提出的位置估计精化操作减小估计误差,最终实现节点的精确定位。实验结果表明,该算法具有较高的定位精度。     

A distributed minimum variance estimator for sensor networks

A distributed estimation algorithm for sensor networks is proposed. A noisy time-varying signal is jointly tracked by a network of sensor nodes, in which each node computes its estimate as a weighted sum of its own and its neighbors' measurements and estimates. The weights are adaptively updated to minimize the variance of the estimation error. Both estimation and the parameter optimization is distributed; no central coordination of the nodes is required. An upper bound of the error variance in each node is derived. This bound decreases with the number of neighboring nodes. The estimation properties of the algorithm are illustrated via computer simulations, which are intended to compare our estimator performance with distributed schemes that were proposed previously in the literature. The results of the paper allow to trading-off communication constraints, computing efforts and estimation quality for a class of distributed filtering problems.     

Three-Dimension Localization of Wideband Sources Using Sensor Network

Usually source localization using sensor networks requires many sensors to localize a few number of sources, and it is still very troublesome to deal with coherent sources. When the three-dimensional (3-D) space are considered, the localization will become more difficult. A new approach is proposed to localize 3-D wideband coherent sources based on distributed sensor network, which consists of two nodes and each node contains only two sensors. Direction-of-arrival (DOA) estimation is performed at each node by employing a new noise subspace proposed. Combining the pattern matching idea and the prior geometrical information of sources, a cost function is constructed to estimate the rough positions. A rotational projection algorithm is proposed to estimate the heights of sources and correct the rough positions, and consequently the localization of 3-D sources could be achieved. Numerical examples are provided to demonstrate the effectiveness of this approach.