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.     

A cluster-based barrier construction algorithm in mobile Wireless Sensor Networks

It is important to maximize network lifetime while meeting coverage requirements in Wireless Sensor Networks (WSN). This paper focuses on maximizing the network lifetime of barrier coverage in WSN with mobile sensors. For mobile sensors, movement energy consumption can be much higher than during sensing and communication. Since the battery capacity of the sensor is fixed, more energy can be used for sensing and communication if the movement distance required to build the barrier can be reduced. Therefore, we focus on reducing the movement distance required by the mobile sensors to build the barriers. Also, we can build as many barriers as possible if we use as few mobile sensors as possible to build a barrier. By rotating multiple barriers, we can further extend the lifetime of the network. Based on the above concept, an energy-efficient algorithm is proposed for building barriers in WSNs with mobile sensors. The contribution of this paper are: (1) We find the minimum number of sensors required to construct a barrier for a rectangular region. (2) We explain how to cluster the sensors in the region, and then calculate the linear equations of the barriers based on the results of the clustering. (3) We explain how to set the sojourn points for these linear equations of the barriers, and then assign the mobile sensors to every one of these sojourn points. In short, the proposed algorithm can construct multiple barriers depending on the location of the sensor, with a minimum number of sensors connecting the left and right boundaries of the target field. Finally, the simulation results show that the proposed algorithm has better performance in terms of network lifetime than the previous result.     

无源声呐场景中使用辅助粒子滤波的邻近目标检测前跟踪方法

针对无源声呐多目标方位跟踪问题,研究了一种基于粒子滤波的检测前跟踪方法,关注于改善邻近目标和机动目标的跟踪性能。首先,提出了一种考虑了邻近目标影响的似然函数;其次,采用辅助变量利用量测信息优化粒子采样,当算法运动模型与目标实际运动状态失配时,这种策略具有很大优势。结合以上两点,提出了一种检测前跟踪算法,该算法将邻近目标划分为一组,使用邻近目标的预测状态计算目标的似然,计算效率较高。利用仿真生成的数据和海上采集的实际数据分别验证了该算法的性能,并与其他多目标粒子滤波检测前跟踪算法进行比较,证明了该算法具有良好的跟踪性能。在目标邻近和目标机动的情况下,该算法的优势更加明显。     

A Recurrent Quantum Neural Network Model to Describe Eye Tracking of Moving Targets

A theoretical quantum neural network model is proposed using a nonlinear Schrödinger wave equation. The model proposes that there exists a nonlinear Schrödinger wave equation that mediates the collective response of a neural lattice. The model is used to explain eye movements when tracking moving targets. Using a recurrent quantum neural network(RQNN) while simulating the eye tracking model, two very interesting phenomena are observed. First, as eye sensor data is processed in a classical neural network, a wave packet is triggered in the quantum neural network.This wave packet moves like a particle. Second, when the eye tracks a fixed target, this wave packet moves not in a continuous but rather in a discrete mode. This result reminds one of the saccadic movements of the eye consisting of ‘jumps’ and ‘rests’. However, such a saccadic movement is intertwined with smooth pursuit movements when the eye has to track a dynamic trajectory. In a sense, this is the first theoretical model explaining the experimental observation reported concerning eye movements in a static scene situation. The resulting prediction is found to be very precise and efficient in comparison to classical objective modeling schemes such as the Kalman filter.     

基于虚拟仪器和可调谐激光技术的光纤光栅传感系统

提出了一种新颖的基于虚拟仪器(VI)和可调谐激光技术的光纤光栅(FBG)传感系统,利用可调谐激光对由光纤光栅组成的传感器阵列进行波长扫描,实现了多根光栅的复用准静态解调,并结合抖动技术和反馈环结构,使得探测信号在每一根传感光栅中心波长处过零,以提高系统在测定波长偏移时的分辨力。当反馈环工作在闭环状态下时,该系统还可对单根光栅实现动态跟踪锁定,实现单根光栅的动态解调。该传感系统的数据采集采用虚拟仪器技术,通过多通道同时输入输出实现了在线实时解调。实验采用了4根光栅组成传感阵列,获得了静态多根光栅小于1με和单根光栅动态频率10 Hz时3.3 n/εHz的解调分辨力,动态应变范围在850με。     

Genetic algorithm-support vector regression for high reliability SHM system based on FBG sensor network

Structural Health Monitoring (SHM) based on fiber Bragg grating (FBG) sensor network has attracted considerable attention in recent years. However, FBG sensor network is embedded or glued in the structure simply with series or parallel. In this case, if optic fiber sensors or fiber nodes fail, the fiber sensors cannot be sensed behind the failure point. Therefore, for improving the survivability of the FBG-based sensor system in the SHM, it is necessary to build high reliability FBG sensor network for the SHM engineering application. In this study, a model reconstruction soft computing recognition algorithm based on genetic algorithm-support vector regression (GA-SVR) is proposed to achieve the reliability of the FBG-based sensor system. Furthermore, an 8-point FBG sensor system is experimented in an aircraft wing box. The external loading damage position prediction is an important subject for SHM system; as an example, different failure modes are selected to demonstrate the SHM system's survivability of the FBG-based sensor network. Simultaneously, the results are compared with the non-reconstruct model based on GA-SVR in each failure mode. Results show that the proposed model reconstruction algorithm based on GA-SVR can still keep the predicting precision when partial sensors failure in the SHM system; thus a highly reliable sensor network for the SHM system is facilitated without introducing extra component and noise.     

Demodulation algorithm for spatial-frequency-division-multiplexed fiber-optic Fizeau strain sensor networks

A demodulation algorithm for spatial-frequency-division-multiplexed fiber-optic Fizeau strain sensor networks with a large number of sensors is proposed to effectively reduce the cross talk between any two adjacent sensors and hence substantially enhance the multiplexing capability of the network, which is based on the Pisarenko algorithm. The cross talk between two fiber-optic Fizeau sensors is investigated experimentally. The experimental results show that a strain accuracy of better than +/-10 micro permittivity can be achieved even when the cavity length difference is approximately 100 microm. It was demonstrated that the multiplexing capability of the spatial-frequency-division-multiplexed fiber-optic Fizeau sensor network can be approximately 5 times greater than that of the conventional fast-Fourier-transform algorithm, and this can lead to achievement of a sensing network with a multiplexing capability of as many as 1000 Fizeau sensors.     

一种改进相似度量的红外目标跟踪算法

针对红外目标成像特点,提出了一种简单易行、鲁棒性强的目标跟踪算法.在对原始算法中的核心部分相似度量函数进行理论分析的基础上,新算法给出了两点改进:一是利用动态聚类思想对相似度量函数中匹配样本增加动态权值,提高了模型图像和目标图像样本间匹配准确度,二是增加了模型图像和目标图像像素点邻域信息,进一步增强跟踪算法鲁棒性.实验仿真表明,新建算法能对复杂场景下的红外目标进行较好跟踪,证实了跟踪算法的可行性和有效性.     

A unified algorithm for target detection and tracing based on data of array sensors

A unified method for target detection and tracing based on data from sensors of array is presented in order to improve detection and tracking abilities of the weak targets with low signal-to-noise ratio. Assuming that the multiple targets are uncorrelated each other and the number of the targets is known a priori, the status of the targets can be estimated with the maximum a-posteriori （MAP） method directly through the sensors data. The proposed method is different from the classical method, by which it can detect and track targets simultaneously by adding the target＇s signal energy information besides its direction of arrivM（DOA） information. Simulated and sea trial data results show that the detection and tracing capabilities of weak targets can be improved and wrong tracing and missing tracing problems, which exist in the classical tracing method when it is faced with the crossing targets, can be resolved by the proposed method.     

Training Data Selection and Optimal Sensor Placement for Deep-Learning-Based Sparse Inertial Sensor Human Posture Reconstruction

Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a few wearable sensors have been actively studied in recent years. In this paper, we propose a deep-learning-based sparse inertial sensor human posture reconstruction method. This method uses bidirectional recurrent neural network (Bi-RNN) to build an a priori model from a large motion dataset to build human motion, thereby the low-dimensional motion measurements are mapped to whole-body posture. To improve the motion reconstruction performance for specific application scenarios, two fundamental problems in the model construction are investigated: training data selection and sparse sensor placement. The problem of deep-learning training data selection is to select independent and identically distributed (IID) data for a certain scenario from the accumulated imbalanced motion dataset with sufficient information. We formulate the data selection into an optimization problem to obtain continuous and IID data segments, which comply with a small reference dataset collected from the target scenario. A two-step heuristic algorithm is proposed to solve the data selection problem. On the other hand, the optimal sensor placement problem is studied to exploit most information from partial observation of human movement. A method for evaluating the motion information amount of any group of wearable inertial sensors based on mutual information is proposed, and a greedy searching method is adopted to obtain the approximate optimal sensor placement of a given sensor number, so that the maximum motion information and minimum redundancy is achieved. Finally, the human posture reconstruction performance is evaluated with different training data and sensor placement selection methods, and experimental results show that the proposed method takes advantages in both posture reconstruction accuracy and model training time. In the 6 sensors configuration, the posture reconstruction errors of our model for walking, running, and playing basketball are 7.25°, 8.84°, and 14.13°, respectively.     

传感器网络基于特征值分解的信号被动定位技术

基于传感器网络的信号被动定位技术在电磁学、声学、声呐系统以及传热学等领域具有广泛的应用前景,当传感器网络节点所接收噪声强度不同或传输信道存在阴影衰落效应时,给出了目标信号到达距离比定位关联度量的估计方法与基于信号到达距离比的被动定位算法.将特征值分解技术引入到信号到达距离比定位关联度量估计中,通过接收信号协方差矩阵特征值分解技术估计各节点所接收噪声强度,并通过网络参考节点轮换与特征值分解方法消除阴影衰落效应所引入的定位误差,最后给出该算法的最小二乘定位解.该方法可较好的消除由于节点接收噪声强度不同以及阴影衰落效应等因素所带来的定位性能恶化.     

多尺度传感网络失效节点检测系统设计

对多尺度传感网络中的失效节点进行准确检测与定位,实现故障节点的高效检测,保障传感网络的可靠运行。提出一种基于多传感器量化融合跟踪滤波检测的失效节点检测算法,并进行检测系统优化设计。构建多尺度传感网络的节点分布实体对象模型,进行失效节点检测系统总体设计和技术指标分析。设计基于多传感器量化融合跟踪滤波检测的失效节点检测算法。进行系统的硬件设计,包括A/D模块设计、时钟电路设计、程序加载电路设计、传感器通信模块设计和系统电源模块设计。在ARM Cortex?-M0平台上进行检测系统软件开发。系统仿真结果表明,该系统进行多尺度传感网络失效节点检测的准确度较高,提高了传感器网络的寿命周期。     

基于L-M算法的反向传播网络的湿度传感器输出误差补偿研究

针对湿度传感器的输出非线性问题,提出了基于L-M算法建立BP神经网络进行补偿校正,实现电阻湿度传感器的输入与输出非线性补偿,并与共轭梯度算法、拟牛顿算法所建立的神经网路模型进行对比,重点比较了模型误差性能、收敛速度。结果表明：基于L-M算法建立的神经网络模型,在收敛速度、误差性能等方面具有更高效的表现,更适合湿度传感器的非线性特性的补偿校正。     

基于片上系统协同计算的实时数据自动检测电化学传感器

针对应用于生物体内特征物质检测与跟踪的电化学传感器的其智能化和集成化问题,本文提出了一种基于片上系统的支持实时数据自动检测的电化学传感器。首先,将应用于电化学传感器结构,通过将测量样品、检测溶液接口、多电极和检测传导器等单元集成在片上系统,结合信号转换和电源装置,实现电化学传感器的独立计算、存储和通信功能。接着,通过实现待测量物多电极、检测溶液电极子和单片机逻辑控制的协同计算满足自动检测需求。最后,基于数据整合与自动化处理设计了支持实时数据检测的电化学传感器。验证结果表明,在数据检测精度和实时性方面所提方案明显优于传统的非片上系统电化学传感器。     

基于高斯混合概率假设滤波的水下目标跟踪算法

为了解决传统水下目标跟踪中目标数目估计不准确、状态估计误差增长过快的问题,提出了一种基于高斯混合概率假设滤波的水下目标跟踪算法。该算法基于双基地观测模型,采用高斯混合概率假设滤波算法处理方位和时延信息,利用粒子群算法处理多普勒频率获得矢量速度,进一步提升算法的跟踪精度。结果表明,该算法能完成在杂波环境下对目标的跟踪,相比传统的关联算法,能够有效地实现目标个数估计和抑制状态误差增长的目的。     

电视经纬仪跟踪测量中运动目标快速识别定位算法

为解决电视经纬仪跟踪测量系统中对运动目标快速识别定位的要求,在深入分析运动目标特性和电视经纬仪跟踪测量系统特点的基础上,利用经纬仪角度变化信息,对运动目标图像序列进行帧间差值计算以获得目标残差图,并基于残差图提出了一种新型的运动目标快速识别方法,大大减少了运算量. 结合中值滤波和图像二值化,实现了运动目标的快速定位. 通过对目标实测图像序列的实验, 结果证明：该算法具有快速、稳定、有效等优点,能够满足电视经纬仪跟踪测量系统对运动目标快速识别定位的要求.     

Detection and tracking of targets in infrared images using Bayesian techniques

Simple yet robust techniques for detecting targets in infrared (IR) images are an important component of automatic target recognition (ATR) systems. In our previous works, we have developed IR target detection and tracking algorithms based on image correlation and intensity. In this paper, we discuss these algorithms, their performances and problems associated with them and then propose novel algorithms to alleviate these problems. Our proposed target detection and tracking algorithms are based on frequency domain correlation and Bayesian probabilistic techniques, respectively. The proposed algorithms are found to be suitable for real-time detection and tracking of static or moving targets, while accommodating for detrimental affects posed by the clutter and background noise. Finally, limitations of all these algorithms are discussed.     

水下目标多平台协同定位和跟踪方法

提出了一种融合多平台方位观测数据的水下声学目标定位跟踪方法。该方法采用多级多假设跟踪对目标状态进行滤波和关联,第一级基于每个平台波束域检测结果独立使用多假设跟踪方法对目标方位角进行初级跟踪;第二级首先通过多平台联合似然概率估计多目标初始位置,再用多假设跟踪方法对不同平台初级跟踪的方位角航迹进行关联,最后通过关联的角度值和扩展卡尔曼滤波对多目标位置进行序贯估计。仿真结果证明,该方法能以更少计算量实现更优定位性能,优于单级多假设跟踪方法.海试数据处理结果表明,存在较多干扰的情况下,单级方法跟踪过程中丢失目标,而多级方法实现了对目标的全程跟踪,且收敛后定位误差在70m内。该方法可降低噪声干扰对数据关联及目标定位的影响,并通过多平台协同消除线阵的方位模糊,适用于移动多平台声学目标定位和跟踪场景。      

Cross-calibration of participatory sensor networks for environmental noise mapping

Participatory measurements appear as a promising technique for performing noise mapping and monitoring. However, the confidence in the quality of raw data collected through participatory measurements controls the faithfulness of the output noise maps. In this paper, a cross-calibration method is proposed, which aims at both selecting the best candidate sensors and improving the furnished raw data. The method rests upon four steps: (i) an outlier detection, (ii) the crowd sensors-based correction, (iii) a fixed sensors-based correction, and (iv) the L_den estimation. The efficiency of the approach for different characteristics of the network of mobile sensors is evaluated on its ability to reconstruct an artificial reference sound field, which consists of the one-month L_10s evolution, on a twenty streets network. The main conclusions are: (i) the systematic errors of the sensors can be efficiently corrected by a cross-calibration method, and thus do not affect the L_den estimation, (ii) the fixed sensor network helps estimating the average error of the network of mobile sensors, (iii) the dispersion in an individual sensor measurements, which is due for example to the operator, stands for a much more critical concern and should be flagged by a rigorous outlier detection method, as the one proposed in this paper, (iv) although individual measures are improved by the proposed cross-calibration, some errors remain on the L_den estimation because of the shortness of the collected samples, (v) increasing the number of sensors does not improve the L_den estimation as long as individual measurements dispersions remain too large.     

A restricted-direction target search approach based on coupled routing and optical sensor tasking optimization

A key issue in UAVs (Unmanned Air Vehicles) and UAV-mounted sensor control is the target search problem: finding targets in minimum time. In this paper, we proposed a restricted-direction target search approach based on coupled routing and optical sensor tasking optimization. In this method, we consider a single UAV, which is equipped with two optical sensors to view a limited large region of the dynamic environment. The UAV moves in the dynamic environment, searches for targets of interest, and is capable of avoiding obstacles and threats immediately. The paths are obtained considering actual maneuverability limitations of the UAV and are evaluated according to optimization of the optical sensor tasks for the duration of the path. Series of comparative experimental results demonstrate that this algorithm makes effective use of the coupled method of optimization and performs significantly better than previously proposed approaches.