Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering

Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wireless sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies Kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.     

Distributed estimation for adaptive sensor selection in wireless sensor networks

Wireless sensor networks (WSNs) are usually deployed for monitoring systems with the distributed detection and estimation of sensors. Sensor selection in WSNs is considered for target tracking. A distributed estimation scenario is considered based on the extended information filter. A cost function using the geometrical dilution of precision measure is derived for active sensor selection. A consensus-based estimation method is proposed in this paper for heterogeneous WSNs with two types of sensors. The convergence properties of the proposed estimators are analyzed under time-varying inputs. Accordingly, a new adaptive sensor selection (ASS) algorithm is presented in which the number of active sensors is adaptively determined based on the absolute local innovations vector. Simulation results show that the tracking accuracy of the ASS is comparable to that of the other algorithms.     

Trajectroy prediction for target tracking using acoustic and image hybrid wireless multimedia sensors networks

Wireless multimedia sensor networks (WMSN), with self-organizing and high fault tolerant characteristics, have achieved great advantages in target tracking region. However, the capabilities of these tiny devices are limited by their battery power, storage capacity, computational ability and communication bandwidth. In this paper, hybrid wireless multimedia sensors networks composed of acoustic and image sensors are proposed for target tracking. When the target appears in the detection area, it may change the environment parameters nearby, so acoustic sensors are used to gather target signal firstly. Then, a target location method is executed based on the strength of the received acoustic signal. Furthermore, to achieve energy-efficient target tracking with high reliability and robust, image sensors are used as supplements to the acoustic sensors. This approach also reduces the power consumption communication burden of the whole networks. In order to decrease the number of active nodes, Gauss Markov mobility model is also adopted to predict the target trajectory and minimize the tracking region with considering of vehicular kinematics. Simulation results verify that, compared with other algorithms, our scheme can reduce the energy consumption and improve tracking accuracy.     

Building a sonar map in a specular environment using a singlemobile sensor

The physical properties of acoustic sensors are exploited to obtain information about the environment for sonar map building. A theoretical formulation for interpreting the sensor databases on the physical principles of acoustic propagation and reflection is presented. A characterization of the sonar scan that allows the differentiation of planes, corners, and edges in a specular environment is described. A single sensor mounted on an autonomous vehicle in a laboratory verifies the technique. The implications for sonar map building and the limitations of differentiating elements with one sensor are discussed     

Narrowband signal detection techniques in shallow ocean by acoustic vector sensor array

This paper presents the formulation and performance analysis of four techniques for detection of a narrowband acoustic source in a shallow range-independent ocean using an acoustic vector sensor (AVS) array. The array signal vector is not known due to the unknown location of the source. Hence all detectors are based on a generalized likelihood ratio test (GLRT) which involves estimation of the array signal vector. One non-parametric and three parametric (model-based) signal estimators are presented. It is shown that there is a strong correlation between the detector performance and the mean-square signal estimation error. Theoretical expressions for probability of false alarm and probability of detection are derived for all the detectors, and the theoretical predictions are compared with simulation results. It is shown that the detection performance of an AVS array with a certain number of sensors is equal to or slightly better than that of a conventional acoustic pressure sensor array with thrice as many sensors.     

Optimization of a thermal flow sensor for acoustic particle velocity measurements

In this paper, a thermal flow sensor consisting of two or three heated wires, the Microflown, is treated for application to acoustic measurements. It is sensitive to flow ("particle velocity"), contrary to conventional microphones that measure acoustic pressures. A numerical analysis, allowing for detailed parametric studies, is presented. The results are experimentally verified. Consequently, improved devices were fabricated, and also sensors with a new geometry consisting of three wires, instead of the usual two, of which the central wire is relatively most heated. These devices are the best performing Microflowns to date with a frequency range extending from 0 to over 5 kHz and a minimum detectable particle velocity level of about 70 nm/s at 2 to 5 kHz (i.e., 3 dB PVL or SPL, corresponding to a pressure of 3.1/spl middot/10/sup -5/ Pa at a free field specific acoustic impedance).     

基于正四棱锥形六元声阵列被动定位的研究

声音无线传感器网络节点利用到达时间差(TDOA)原理进行声音目标定位,提出了一种正四棱锥形六元声音阵列定位模型。推导出了目标空间定位计算公式,应用广义互相关算法对时延进行估计。同时对模型误差进行了分析。最后通过仿真实验,可以对目标进行有效定位,定位误差小。     

Preliminary results on a silicon gyrometer based on acoustic modecoupling in small cavities

A silicon vibratory gas angular rate sensor has been developed by means of micromachining techniques. It is a shockproof sensor because it has no movable part. This device is a miniaturized form of an instrument, which has been previously fabricated in a conventional technology format and called the acoustic gyrometer. The working principle of this sensor is based on acoustic coupling between two orthogonal modes of a closed cavity, due to Coriolis forces effect on vibrating gas particles. The gyrometer, which is presented in this paper, was fabricated by a silicon process. It is constituted by an acoustic cavity and four microphones: one to generate an acoustic wave, one to slave the cavity at its first resonance frequency, and two for the measurement of the angular rate effect. Finite element modeling (FEM) modal analyses were performed on two cavity shapes: cylindrical and trapezoidal, corresponding to the fabricated devices. The results are compared with available analytic solutions and with measurements     

Energy-efficient adaptive sensor scheduling for target tracking in wireless sensor networks

Sensor scheduling is essential to collaborative target tracking in wireless sensor networks (WSNs). In the existing works for target tracking in WSNs, such as the information-driven sensor query (IDSQ), the tasking sensors are scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energyefficient adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter (EKF)-based estimation technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly without degrading the tracking accuracy.     

Square-root unscented Kalman filtering-based localization and tracking in the Internet of Things

The Internet of Things (IoT), which is usually established over architectures of wireless sensor networks, provides an actual platform for various applications of personal and ubiquitous computing. Recently, moving target localization and tracking in an IoT environment have been paid more and more attention. This paper proposes a square-root unscented Kalman filtering (SR-UKF)-based algorithm to discover real-time location of a moving target in an IoT environment where there exist quantities of sensors. The data generated from wireless sensor nodes of the IoT make contributions to localization and tracking of the moving target. First, a least-square (LS) criterion-based mathematical model is proposed for localization initialization in an IoT scenario. Next, we employ an SR-UKF idea for the further localization and tracking. By using the data coming from sensor nodes near the target, real-time location of the moving target can be estimated by implementation of SR-UKF in an iterative fashion so as to achieve target status tracking. Simulation results show that the proposed algorithm achieves good performance in estimation of both position and velocity of the target with either uniform linear motion or variable-speed curve motion. Compared with some existing conventional extended Kalman filtering (EKF) or UKF-based methods, the proposed algorithm shows lower location/velocity estimation error under the same computational complexity, which demonstrates its potential significance in ubiquitous computing applications for an IoT environment.     

Multi-Sensor Estimation for Unreliable Wireless Networks with Contention-Based Protocols

The state estimation plays an irreplaceable role in many real applications since it lays the foundation for decision-making and control. This paper studies the multi-sensor estimation problem for a contention-based unreliable wireless network. At each time step, no more than one sensor can communicate with the base station due to the potential contention and collision. In addition, data packets may be lost during transmission since wireless channels are unreliable. A novel packet arrival model is proposed which simultaneously takes into account the above two issues. Two scenarios of wireless sensor networks (WSNs) are considered: the sensors transmit the raw measurements directly and the sensors send the local estimation instead. Based on the obtained packet arrival model, necessary and sufficient stability conditions of the estimation at the base station side are provided for both network scenarios. In particular, all offered stability conditions are expressed by simple inequalities in terms of the packet arrival rates and the spectral radius of the system matrix. Their relationships with existing related results are also discussed. Finally, the proposed results are demonstrated by simulation examples and an environment monitoring prototype system.     

基于光学测量的触觉硬度估计研究综述

对基于光学测量的触觉传感器在柔性目标的硬度估计中的应用和发展方向进行梳理。首先以Shore硬度为例对柔性目标硬度测量的基本原理进行介绍。进一步地,对基于光学测量的触觉传感的基本原理进行分析,并对其在硬度估计中的可行性进行探讨。然后,对基于该类型传感器的硬度估计在不同领域中的应用进行了总结。最后,讨论了基于光学测量的触觉传感器在硬度估计中的发展方向。目前的研究表明,基于光学测量的触觉传感器因其分辨率高、延迟低、感知信息丰富等方面的优势,在柔性目标的硬度估计方面展现出巨大的潜力,然而,在灵巧手操作、医疗诊断等应用层面的研究仍存在较大的发展空间。     

Surface and Bulk-Silicon-Micromachined Optical Displacement Sensor Fabricated With the SwIFT-Lite™ Process

For many micromachined sensors such as microphones and accelerometers, optical displacement sensing may have advantages over capacitive sensing—offering potentially high-displacement sensitivity independent of sensor area and gap height. A particular diffraction-based optical displacement sensor design consisting of a sensing diaphragm suspended over a rigid grated electrode has demonstrated advantages in terms of sensitivity, integration and stability. When illuminated from the backside, this structure generates a zeroth and complementary higher order diffracted beams whose intensities are modulated by the diaphragm displacement with the sensitivity of a regular Michelson interferometer. In previous work, acoustic devices surface micromachined on quartz substrates using aluminum for the diaphragm and grating were presented and characterized. In this work, we present the fabrication and characterization of a surface- and bulk-silicon micromachined diffraction-based optical microphone structure fabricated with Sandia National Laboratories' SwIFT-Lite™ process, which uses silicon nitride and polysilicon structural materials that have been employed extensively in MEMS and form a more thermally matched material set robust against corrosion and fatigue. The process introduces a new design space to microscale optical displacement sensing, enabling large, soft structures with perforated back-plates ideal for microphone and inertial-sensor designs.1599     

水声信号处理中的多传感器数据融合

水下环境的复杂性往往使得单个传感器的可靠性降低,而多个水下传感器的共同使用则成为一个趋势,这就涉及到对于来自多个传感器的数据进行多方面、多层次的融合处理。概括和分析了当前多传感器数据融合技术在水声信号处理领域中的应用现状,并将诸多的融合方法按照具体应用范畴进行了分类,主要包括水下目标探测、跟踪和识别,以及水下自制机车导航等方面,对每种应用情况下的各种数据融合方法进行了比较。     

强度调制型光纤网络温度传感器多路检测技术

本文结合时分复用形式的网络用频率域的方法处理 ,设计了一个具有两个强度调制型传感器的系统 ,并对实际光纤网络温度传感器系统响应进行了测量和数据处理 ,并又分析和计算出强度受调制的两路光的通光率 ,从而完成了对温度的测量     

Cross-spectral visual simultaneous localization and mapping (SLAM) with sensor handover

In this work, we examine the classic problem of robot navigation via visual simultaneous localization and mapping (SLAM), but introducing the concept of dual optical and thermal (cross-spectral) sensing with the addition of sensor handover from one to the other. In our approach we use a novel combination of two primary sensors: co-registered optical and thermal cameras. Mobile robot navigation is driven by two simultaneous camera images from the environment over which feature points are extracted and matched between successive frames. A bearing-only visual SLAM approach is then implemented using successive feature point observations to identify and track environment landmarks using an extended Kalman filter (EKF). Six-degree-of-freedom mobile robot and environment landmark positions are managed by the EKF approach illustrated using optical, thermal and combined optical/thermal features in addition to handover from one sensor to another. Sensor handover is primarily targeted at a continuous SLAM operation during varying illumination conditions (e.g., changing from night to day). The final methodology is tested in outdoor environments with variation in the light conditions and robot trajectories producing results that illustrate that the additional use of a thermal sensor improves the accuracy of landmark detection and that the sensor handover is viable for solving the SLAM problem using this sensor combination.     

MEMS矢量水听器阵列信号处理研究

MEMS矢量水听器是一种新型的水声传感器,对这种传感器的原理进行了简要介绍。为了验证该MEMS矢量水听器阵列的目标估计性能,进行了矢量阵的深海实验研究,选取了MUSIC算法应用于该MEMS矢量阵。实验结果表明:在复杂海洋环境中,该MEMS矢量阵能够实现对目标的方位估计和水下运动目标的航迹跟踪,从而验证了该MEMS矢量水听器成阵的可行性,为工程化应用奠定了基础。     

WSN基于声音能量量化数据的目标定位研究

无线传感器网络的通信带宽和能量是有限的,只传输二进制或几个比特量化数据来完成目标定位任务可以减少网络开销。对无线传感器网络目标定位方法进行了研究,采用声音能量衰减模型,推导了基于量化信号的似然函数,给出了基于声音能量量化数据的最大似然定位方法。为了对估计结果进行评定,推导了最大似然估计的克拉美罗下限。仿真结果表明,该方法对目标定位的准确度基本接近于克拉美罗下限。因此,在满足定位精度的前提下,可通过减少传递的比特位数来节约网络的能量。     

A Dual-Microphone Speech Enhancement Algorithm Based on the Coherence Function

A novel dual-microphone speech enhancement technique is proposed in the present paper. The technique utilizes the coherence between the target and noise signals as a criterion for noise reduction and can be generally applied to arrays with closely-spaced microphones, where noise captured by the sensors is highly correlated. The proposed algorithm is simple to implement and requires no estimation of noise statistics. In addition, it offers the capability of coping with multiple interfering sources that might be located at different azimuths. The proposed algorithm was evaluated with normal hearing listeners using intelligibility listening tests and compared against a well-established beamforming algorithm. Results indicated large gains in speech intelligibility relative to the baseline (front microphone) algorithm in both single and multiple-noise source scenarios. The proposed algorithm was found to yield substantially higher intelligibility than that obtained by the beamforming algorithm, particularly when multiple noise sources or competing talker(s) were present. Objective quality evaluation of the proposed algorithm also indicated significant quality improvement over that obtained by the beamforming algorithm. The intelligibility and quality benefits observed with the proposed coherence-based algorithm make it a viable candidate for hearing aid and cochlear implant devices.