Position‐based adaptive quantization for target location estimation in wireless sensor networks using one‐bit data

The problem of target location estimation in a wireless sensor network is considered, where due to the bandwidth and power constraints, each sensor only transmits one‐bit information to its fusion center. To improve the performance of estimation, a position‐based adaptive quantization scheme for target location estimation in wireless sensor networks is proposed to make a good choice of quantizer' thresholds. By the proposed scheme, each sensor node dynamically adjusts its quantization threshold according to a kind of position‐based information sequences and then sends its one‐bit quantized version of the original observation to a fusion center. The signal intensity received at local sensors is modeled as an isotropic signal intensity attenuation model. The position‐based maximum likelihood estimator as well as its corresponding position‐based Cramér–Rao lower bound are derived. Numerical results show that the position‐based maximum likelihood estimator is more accurate than the classical fixed‐quantization maximum likelihood estimator and the position‐based Cramér–Rao lower bound is less than its fixed‐quantization Cramér‐Rao lower bound. Copyright © 2015 John Wiley & Sons, Ltd.     

Genetic Algorithm-based Adaptive Optimization for Target Tracking in Wireless Sensor Networks

In this paper, we address the problem of genetic algorithm optimization for jointly selecting the best group of candidate sensors and optimizing the quantization for target tracking in wireless sensor networks. We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering best group of candidate sensors selection problem. The main objective of this paper is twofold. Firstly, the quantization level and the group of candidate sensors selection are to be optimized in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor network. Secondly, the target position is to be estimated using quantized variational filtering (QVF) algorithm. The optimization of quantization and sensor selection are based on the Fast and Elitist Multi-objective Genetic Algorithm (NSGA-II). The proposed multi-objective (MO) function defines the main parameters that may influence the relevance of the participation in cooperation for target tracking and the transmitting power between one sensor and the cluster head (CH). The proposed algorithm is designed to: i) avoid the problem lot of computing times and operation counts, and ii) reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The computation of these criteria is based on the predictive information provided by the QVF algorithm. The simulation results show that the NSGA-II -based QVF algorithm outperforms the standard quantized variational filtering algorithm and the centralized quantized particle filter.     

机动目标跟踪时组网机会阵雷达功率分配算法

针对机动目标的高动态属性导致雷达系统不能精确地分配系统资源问题,本文提出了一种基于改进的当前统计模型的组网机会阵雷达功率分配算法。该算法通过改进的当前统计模型预测机动目标运动状态,采用预测的条件克拉美罗界作为功率分配时目标跟踪性能的衡量基准。针对目标信息的不确定性,引入随机变量表征目标RCS,建立基于机会约束规划的功率资源分配模型,并设计混合智能优化算法求解满足机会约束的最优功率分配。仿真结果表明,预测的条件克拉美罗界能够提供一个更加精确的跟踪性能衡量边界,该算法能够有效提高雷达系统资源利用率。     

Power‐aware range‐free wireless sensor network localization using neighbor distance distribution

In large‐scale wireless sensor networks, cost‐effective and energy‐efficient localization of sensor nodes is an important research topic. In spite of their coarse accuracy, range‐free (connectivity‐based) localization methods are considered as cost‐effective alternatives to the range‐based localization schemes with specialized hardware requirements.In this paper, we derive closed‐form expressions for the average minimum transmit powers required for the localization of sensor nodes, under deterministic path loss, log‐normal shadowing, and Rayleigh fading channel models. The impacts of propagation environment and spatial density of anchor nodes on the minimum transmit power for node localization are evaluated analytically as well as through simulations. Knowledge of the minimum transmit power requirements for localizability of a sensor node enables improving energy efficiency and prolonging lifetime of the network. We also propose a novel distance metric for range‐free localization in large‐scale sensor networks. The target and anchor nodes are assumed to be positioned according to two statistically independent two‐dimensional homogeneous Poisson point processes. Analytical expression for the average distance from a target node to its kth nearest neighbor anchor node is derived and is used for estimating the target‐to‐anchor node distances for localization. The Cramér–Rao lower bound on the localization accuracy for the new distance estimator is derived. Simulation results show the accuracy of the proposed distance estimate compared with some existing ones for range‐free localization. The results of our investigation are significant for low‐cost, energy‐efficient localization of wireless sensor nodes. Copyright © 2011 John Wiley & Sons, Ltd.     

无线传感器网络中基于多比特量化的极大似然分布式估计方法

在无线传感器网络背景下的分布式估计中,由于传输网络对发送功率和传输带宽的限制,压缩信源冗余、降低通信数据量便成为一个重要的课题.为此,本文提出了一种基于多比特量化观测的分布式估计方法（MQS）,利用渐进性能作为优化准则构造量化阈值优化问题,运用粒子群算法对其进行求解得到最优量化阈值,给出了克拉美罗下界的解析表达式,并与均匀量化方法（UQS）和未量化方法（NQS）进行对比.理论分析和仿真实验表明,MQS的性能优于UQS.当量化深度增大到3时,MQS的估计性能十分接近NQS的估计性能.     

An Efficient Direct Position Determination Algorithm Combined with Time Delay and Doppler

The conventional passive location methods such as Taylor series and two-step weighted least square are usually implemented by first estimating related parameters and then solving equations to get the target position. However, the parameters used for location estimation are only estimates and represent an unnecessary intermediate step in the process, which also cannot guarantee to match the real location information. This separation between the parameter estimation algorithm and the location estimation algorithm may lead to information loss. By utilizing a combination of time delay and Doppler, this paper proposes an improved direct position determination algorithm to improve the estimation accuracy. A novel maximum likelihood estimator is used to transform the problem into one of searching for the largest eigenvalue of a Hermitian matrix of position information. Calculation is simplified since the part of nonzero eigenvalues remains unchanged after the matrix is transposed. The target’s position estimation is then determined by searching the space of two-dimensional geographic grids. Simulation results show that the performance of proposed algorithm is closer to the Cramér–Rao lower bound than the original direct position determination algorithm and traditional two-step method based on time delay and Doppler.     

Underwater acoustic sensor networks: Target size detection and performance analysis

Underwater acoustic sensor network consists of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area. Scalability concern suggests a hierarchical organization of underwater sensor networks with the lowest level in the hierarchy being a cluster. In this paper, we show that an ultra-wide band (UWB) channel can be used for underwater channel modeling and propose a maximum-likelihood (ML) estimation algorithm for underwater target size detection using collaborative signal processing within a cluster in underwater acoustic sensor networks. Theoretical analysis demonstrates that our underwater sensor network can tremendously reduce the variance of target size estimation. We show that our ML estimator is unbiased and the variance of parameter estimation matches the Cramer–Rao lower bound. Simulations further validate these theoretical results.     

Optimization-Based Dynamic Sensor Management for Distributed Multitarget Tracking

In this paper, the general problem of dynamic assignment of sensors to local fusion centers (LFCs) in a distributed tracking framework is considered. With recent technological advances, a large number of sensors can be deployed for multitarget tracking purposes. However, due to physical limitations such as frequency, power, bandwidth, and fusion center capacity, only a limited number of them can be used by each LFC. The transmission power of future sensors is anticipated to be software controllable within certain lower and upper limits. Thus, the frequency reusability and the sensor reachability can be improved by controlling transmission powers. Then, the problem is to select the sensor subsets that should be used by each LFC and to find their transmission frequencies and powers in order to maximize the tracking accuracies and minimize the total power consumption. The frequency channel limitation and the advantage of variable transmitting power have not been discussed in the literature. In this paper, the optimal formulation for the aforementioned sensor management problem is provided based on the posterior CramÉr–Rao lower bound. Finding the optimal solution to the aforementioned NP-hard multiobjective mixed-integer optimization problem in real time is difficult in large-scale scenarios. An algorithm is presented to find a suboptimal solution in real time by decomposing the original problem into subproblems, which are easier to solve, without using simplistic clustering algorithms that are typically used. Simulation results illustrating the performance of sensor array manager are also presented.      

Estimating distances via connectivity in wireless sensor networks

Distance estimation is vital for localization and many other applications in wireless sensor networks. In this paper, we develop a method that employs a maximum‐likelihood estimator to estimate distances between a pair of neighboring nodes in a static wireless sensor network using their local connectivity information, namely the numbers of their common and non‐common one‐hop neighbors. We present the distance estimation method under a generic channel model, including the unit disk (communication) model and the more realistic log‐normal (shadowing) model as special cases. Under the log‐normal model, we investigate the impact of the log‐normal model uncertainty; we numerically evaluate the bias and standard deviation associated with our method, which show that for long distances our method outperforms the method based on received signal strength; and we provide a Cramér–Rao lower bound analysis for the problem of estimating distances via connectivity and derive helpful guidelines for implementing our method. Finally, on implementing the proposed method on the basis of measurement data from a realistic environment and applying it in connectivity‐based sensor localization, the advantages of the proposed method are confirmed. Copyright © 2012 John Wiley & Sons, Ltd.     

Iterative receiver for amplify‐and‐forward relay networks with unknown noise correlation

For amplify‐and‐forward relay networks, we propose an iterative scheme to estimate channel and detect information symbols for the multi‐antenna destination in spatially correlated noise. The equivalent channel coefficients and noise covariance are estimated by expectation–maximization algorithm. In addition, we discuss the initialization of iteration and analyze the modified Cramér–Rao bound to show the performance of the proposed iterative estimation. Moreover, on the basis of the structure of the proposed iterative estimator, a joint channel estimation and detection receiver is also provided. Finally, simulation results show that the proposed channel estimator and receiver can achieve the optimal performances in amplify‐and‐forward relay networks with unknown noise correlation. Copyright © 2012 John Wiley & Sons, Ltd.     

The novel communication algorithm and the throughput analysis for wireless sensor networks

Aiming at the significance of the energy controls of wireless sensor networks, an economical energy consumption algorithm for wireless communicating in Wireless Sensor Networks (WSN) is presented. Based on the algorithm, the maximal system throughput of WSN is analyzed, and the upper bound of throughput of WSN is proposed and proved. Some numerical simulations are conducted and analyzed. The conclusions include that the transmitting radius of sensor node and the parameters of the energy cost function have significant influence upon the throughput, but the monitoring region radius has little influence. For the same transmitting distance, the more the hopping of information trans- mitting, the better the throughput of WSN. On the other hand, for the energy optimization of the whole WSN, the trade-off problem between the throughput capacity and the relay nodes is proposed, and the specific expression of relay hops that minimized the energy consumptions and the maximal throughput of WSN under the specific situation is derived.     

Bayesian Surface and Underwater Navigation

A common framework for maritime surface and underwater (UW) map-aided navigation is proposed as a supplement to satellite navigation based on the global positioning system (GPS). The proposed Bayesian navigation method is based on information from a distance measuring equipment (DME) which is compared with the information obtained from various databases. As a solution to the recursive Bayesian navigation problem, the particle filter is proposed. For the described system, the fundamental navigation performance expressed as the CramÉr–Rao lower bound (CRLB) is analyzed and an analytic solution as a function of the position is derived. Two detailed examples of different navigation applications are discussed: surface navigation using a radar sensor and a digital sea chart and UW navigation using a sonar sensor and a depth database. In extensive Monte Carlo simulations, the performance is shown to be close to the CRLB. The estimation performance for the surface navigation application is in comparison with usual GPS performance. Experimental data are also successfully applied to the UW application.     

Optimal sensor and path selection for target tracking in wireless sensor networks

This paper addresses target tracking in wireless sensor networks where the nonlinear observed system is assumed to progress according to a probabilistic state space model. Thus, we propose to improve the use of the quantized variational filtering by jointly selecting the optimal candidate sensor that participates in target localization and its best communication path to the cluster head. In the current work, firstly, we select the optimal sensor in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor networks. This selection is also based on the local cluster node density and their transmission power. Secondly, we select the best communication path that achieves the highest signal‐to‐noise ratio at the cluster head; then, we estimate the target position using quantized variational filtering algorithm. The best communication path is designed to reduce the communication cost, which leads to a significant reduction of energy consumption and an accurate target tracking. The optimal sensor selection is based on mutual information maximization under energy constraints, which is computed by using the target position predictive distribution provided by the quantized variational filtering algorithm. The simulation results show that the proposed method outperforms the quantized variational filtering under sensing range constraint, binary variational filtering, and the centralized quantized particle filtering. Copyright © 2012 John Wiley & Sons, Ltd.     

A Massive Transmission Scheme in Contention-Based MAC for Wireless Multimedia Sensor Networks

For transmitting the data which is composed of sensed information in sensors, wireless sensor networks have been developed and researched for the improvement of energy efficiency, hence, many MAC protocols in WSN employ the duty cycle mechanism. Since the progressed development of the low power transceiver and processor let the high energy efficiency come true, the delivery of the multimedia data which occurs in the area of sensor work should be needed to provide supplemental information. In this paper, we design a new scheme for massive transmission of large multimedia data where the duty cycle is used in contention based MAC protocol, for WMSN. The proposed scheme can be applied into the previous duty cycle mechanism because it provides two operations between normal operation and massive transmission operation. Measuring the status of the buffer in a sender and the condition of current radio channel can be criteria for the decision of the above two operations. This paper shows the results of the experiment by performing the simulation. The target protocol of the experiment is X-MAC which is contention-based MAC protocol for WSN. Two approaches, both X-MAC which operates only duty cycle mechanism and X-MAC(MTS) which operates combined massive transmission scheme, are used for the comparative.     

Decentralized Detection With Censoring Sensors

In the censoring approach to decentralized detection, sensors transmit real-valued functions of their observations when "informative" and save energy by not transmitting otherwise. We address several practical issues in the design of censoring sensor networks including the joint dependence of sensor decision rules, randomization of decision strategies, and partially known distributions. In canonical decentralized detection problems involving quantization of sensor observations, joint optimization of the sensor quantizers is necessary. We show that under a send/no-send constraint on each sensor and when the fusion center has its own observations, the sensor decision rules can be determined independently. In terms of design, and particularly for adaptive systems, the independence of sensor decision rules implies that minimal communication is required. We address the uncertainty in the distribution of the observations typically encountered in practice by determining the optimal sensor decision rules and fusion rule for three formulations: a robust formulation, generalized likelihood ratio tests, and a locally optimum formulation. Examples are provided to illustrate the independence of sensor decision rules, and to evaluate the partially known formulations.     

Optimum processing for delay-vector estimation in passive signal arrays

For the purpose of localizing a distant noisy target, or, conversely, calibrating a receiving array, the time delays defined by the propagation across the array of the target-generated signal wavefronts are estimated in the presence of sensor-to-sensor-independent array self-noise. The Cramér-Rao matrix bound for the vector delay estimate is derived, and used to show that either properly filtered beamformers or properly filtered systems of multiplier-correlators can be used to provide efficient estimates. The effect of suboptimally filtering the array outputs is discussed.     

An Improved Hybrid RSS/TDOA Wireless Sensors Localization Technique Utilizing Wi-Fi Networks

This paper presents a hybrid localization algorithm for wireless sensor networks (WSNs) that simultaneously exploits received signal strength (RSS) and time difference of arrival (TDOA) measurements. The accuracy and convergence reliability of the proposed hybrid scheme are also enhanced by incorporating RSS measurements from Wi-Fi networks via cooperative communications between Wi-Fi and sensor networks. To this end, two different types of estimators based on Taylor-series (TS) expansion and maximum-likelihood (ML) estimation are first proposed to solve the set of nonlinear RSS/TDOA equations taking into account measurement errors. The corresponding Cramér-Rao lower bound (CRLB) for the established scheme is then derived and utilized as a performance measure for the two estimators. Simulation results show that the proposed hybrid positioning approach significantly outperforms the previously considered localization solutions in WSNs, thanks to the joint process of the received signals’ power and time difference of arrival. The advantages of the proposed scheme in providing high location accuracy, fast convergence, low complexity implementation, and low power consumption make it an attractive localization solution via WSNs.     

Maximum Likelihood-Based Direction-of-Arrival Estimator for Discrete Sources

This paper addresses the problem of direction-of-arrival (DOA) parameter estimation in array processing when the signals are inherently discrete, which is the case mainly in the digital communication context. Based on the particular structure of the signal space in the data model, a maximum likelihood-based approach is introduced. The strategy consists in transforming the parameter estimation problem into a decision task. It is shown through numerical simulations that the proposed solution closely follows the performance limit given by the Cramér–Rao bound. Some important features of the technique are as follows: (i) it is capable of handling any number of sources, provided that the number of sensors is greater than or equal to two and the number of snapshots is sufficiently greater than the cardinality of the signal space; (ii) the estimation quality is not affected by the angle and phase separation; and (iii) it offers the possibility to deal with uncalibrated arrays.     

一种三角形网格空洞修复算法

无线传感器网络由大量传感器节点组成,在网络初始化时节点随机部署在目标区域中,导致某一区域未被覆盖而形成覆盖空洞.针对目标区域中存在覆盖空洞问题,设计了一种基于三角形网格的无需地理信息的空洞探测算法ATN和空洞修复算法TNR.利用ATN算法检测节点与其邻居形成的三角形网格是否被完全覆盖,TNR算法以ATN算法理论为基础,向三角形网格中添加节点使目标区域完全覆盖.理论与仿真实验分析表明,ANR算法能够探测出目标区域中所有空洞,TNR算法在部署密集的传感网络中能够快速完成空洞修复.