Three-Dimensional Distance-Error-Correction-Based Hop Localization Algorithm for IoT Devices

The Internet of Things (IoT) is envisioned as a network of various wireless sensor nodes communicating with each other to offer state-of-the-art solutions to real-time problems. These networks of wireless sensors monitor the physical environment and report the collected data to the base station, allowing for smarter decisions. Localization in wireless sensor networks is to localize a sensor node in a two-dimensional plane. However, in some application areas, such as various surveillances, underwater monitoring systems, and various environmental monitoring applications, wireless sensors are deployed in a three-dimensional plane. Recently, localization-based applications have emerged as one of the most promising services related to IoT. In this paper, we propose a novel distributed range-free algorithm for node localization in wireless sensor networks. The proposed three-dimensional hop localization algorithm is based on the distance error correction factor. In this algorithm, the error decreases with the localization process. The distance correction factor is used at various stages of the localization process, which ultimately mitigates the error. We simulated the proposed algorithm using MATLAB and verified the accuracy of the algorithm. The simulation results are compared with some of the well-known existing algorithms in the literature. The results show that the proposed three-dimensional error-correction-based algorithm performs better than existing algorithms.     

Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks

Due to uncertainties in target motion and limited sensing regions of sensors, single-sensor-based collaborative target tracking in wireless sensor networks (WSNs), as addressed in many previous approaches, suffers from low tracking accuracy and lack of reliability when a target cannot be detected by a scheduled sensor. Generally, actuating multiple sensors can achieve better tracking performance but with high energy consumption. Tracking accuracy, reliability, and energy consumed are affected by the sampling interval between two successive time steps. In this paper, an adaptive energy-efficient multisensor scheduling scheme is proposed for collaborative target tracking in WSNs. It calculates the optimal sampling interval to satisfy a specification on predicted tracking accuracy, selects the cluster of tasking sensors according to their joint detection probability, and designates one of the tasking sensors as the cluster head for estimation update and sensor scheduling according to a cluster head energy measure (CHEM) function. Simulation results show that, compared with existing single-sensor scheduling and multisensor scheduling with a uniform sampling interval, the proposed adaptive multisensor scheduling scheme can achieve superior energy efficiency and tracking reliability while satisfying the tracking accuracy requirement. It is also robust to the uncertainty of the process noise.      

采用四象限探测器的双轴跟踪控制技术

采用四象限探测器作为位置传感器，设计出以DSP作为控制器的双轴跟踪控制试验演示系统。通过误差数据采样、计算和比较，在反馈作用下对误差修正，以快速扫描系统和大范围扫描系统构成的双轴联合机构实现运动目标的捕获跟踪。试验结果表明，该系统能够快速捕获并平稳跟踪合作目标，且调试方便，可靠性好，为下一步工程化奠定了基础。     

基于数据融合和多重智能模型的目标识别和跟踪

考虑到单传感器的系统存在着局限性,提出了基于多传感器（雷达和红外）信号融合的目标识别和跟踪系统,以利用数据的互补和冗余。特征层融合能利用各传感器提供的特征为提高目标识别能力;对于点目标和面目标分别提出了智能规则推理和神经网分类器的目标识别方法。决策层融合能提高目标跟踪的精度并提高抗干扰性;提出了可信度决策的目标跟踪方法。     

非线性无序测量无迹滤波在地面跟踪目标指示器的应用

对于多传感器融合系统,在处理中心往往由于通信数据链的失误,出现滞后数据(即无序量测),这对数据不利于融合的正常进行.本文对此提出一种最优和次优的无序量测无迹卡尔曼(UKF)滤波器,基于UT变换,把多步滞后量测转化为等价一步滞后量测,用无序量测直接滤波更新.通过最优、次优UKF滤波器和扩展KF滤波器在不同情况下对地面跟踪目标指示器(GMTI)组合跟踪的无序量测处理作比较,可看出本文所述滤波器提高了非线性条件下的无序量测滤波精度.     

基于稀疏信号功率迭代补偿的矢量传感器阵列DOA估计

针对现有稀疏信号功率迭代算法对方位相近目标分辨概率与估计精度较低问题,提出了一种稀疏信号功率迭代补偿的矢量传感器阵列波达方向(Direction of Arrival, DOA)估计方法。基于稀疏信号补偿原理和加权协方差矩阵拟合准则,构建了关于稀疏信号功率与补偿权重的目标函数。推导了稀疏信号功率迭代更新表达式的闭式解。通过对稀疏信号功率进行谱峰搜索获得DOA估计值。理论分析表明,所提算法通过对离散网格点上的信号功率进行补偿提高了方位相近目标的分辨率概率与估计精度。仿真结果表明,相较于经典子空间算法与现有稀疏功率迭代算法,所提算法对方位相近目标具有较高的分辨概率与估计精度。     

The human-based multisensor fusion method for artificial nose andtongue sensor data

Presently, an increased interest is apparent for the development of integrated human-like smell and taste sensing capabilities, e.g. for chemical, paper pulp, food, and medicine applications. This paper will present an original sensor fusion method based on human expert opinions about smell and taste and measurement data from artificial nose and taste sensors. The “electronic nose” consists of an array of gas sensors with different selectivity patterns, signal handling, and a sensor signal pattern recognition and decision strategy. The “electronic tongue”, which was developed for the taste analysis of liquids is based on pulse voltammetry. Measurement data from the artificial smell and taste sensors are used to produce sensor-specific opinions about these two human-like sensing modalities. This is achieved by a team of artificial neural networks and conventional signal handling which approximates a Bayesian decision strategy for classifying the sensor information. Further, a fusion algorithm based on the maximum likelihood principle provides a combination of the smell and, respectively, taste opinions, into an overall integrated opinion similar to human beings. The proposed integrated smell- and taste-sensing method is then illustrated by an application of real world measurements in the food industry     

Integrated approach for automatic target recognition using a network of collaborative sensors

We introduce what is believed to be a novel concept by which several sensors with automatic target recognition (ATR) capability collaborate to recognize objects. Such an approach would be suitable for netted systems in which the sensors and platforms can coordinate to optimize end-to-end performance. We use correlation filtering techniques to facilitate the development of the concept, although other ATR algorithms may be easily substituted. Essentially, a self-configuring geometry of netted platforms is proposed that positions the sensors optimally with respect to each other, and takes into account the interactions among the sensor, the recognition algorithms, and the classes of the objects to be recognized. We show how such a paradigm optimizes overall performance, and illustrate the collaborative ATR scheme for recognizing targets in synthetic aperture radar imagery by using viewing position as a sensor parameter.     

基于修正时延粒子滤波的水声传感器网络目标跟踪

在水声传感器网络中,利用多个传感器节点探测到的方位信息进行目标跟踪是水下目标跟踪领域的一种新思路。由于水中声速的限制,信号到达各个节点的时间不是同步的,提出了一种修正时间延迟的方法,并将其与粒子滤波(PF)、扩展卡尔曼粒子滤波(EKPF)结合来解决该非线性跟踪问题。仿真分析表明修正时延后,算法的跟踪性能有较大提高;并且在相同条件下,EKPF的跟踪性能比PF好。     

基于双曲调频距离误差的目标跟踪修正算法

双曲调频信号的多普勒不变性可使运动目标匹配滤波输出的能量损失达到最小，在运动目标检测与参数估计中具有独特优势，已广泛应用于新型主动声呐。然而其多普勒不变性在提高检测能力的同时，会引入目标距离估计误差，从而影响主动声呐跟踪性能。文章在分析双曲调频信号距离估计误差的基础上，提出了目标跟踪算法的误差修正方法，通过将距离估计误差公式与传统观测方程及滤波算法进行融合，实现目标跟踪算法的误差补偿，显著提高了算法的跟踪性能，尤其是对快速运动目标的跟踪性能。     

改进的EKF算法在目标跟踪中的运用

过程噪声和测量噪声影响Kalman滤波的性能,通常很难得到它们准确的值。提出观测噪声和过程噪声实时估计的自适应算法。该算法可以用在非线性和机动目标跟踪问题中,不必预先知道准确的噪声方差。重新估测观测噪声方差矩阵,可以较好地消除由观测噪声带来的误差;建立一个简单的线性Kalman滤波器对过程噪声进行实时估计,这对于机动目标来说是必要的,因为原有的过程噪声将受到加速度影响,不能包含全部的信息。实验表明,该算法保证EKF稳定性,提高了跟踪性能。模拟实验300次后,X,Y方向位置均方误差分别为7.8099,9.6838。     

Nonlinear inverse dynamic models of gas sensing systems based onchemical sensor arrays for quantitative measurements

Gas sensing systems based on low-cost chemical sensor arrays are gaining interest for the analysis of multicomponent gas mixtures. These sensors show different problems, e.g., nonlinearities and slow time-response, which can be partially solved by digital signal processing. Our approach is based on building a nonlinear inverse dynamic system. Results for different identification techniques, including artificial neural networks and Wiener series, are compared in terms of measurement accuracy     

Quantification of ternary mixtures of heavy metal cations from metallochromic absorbance spectra using neural network inversion

A new method based on artificial neural networks (ANN) for the processing of spectrophotometric data is proposed and illustrated on the example of the simultaneous quantification of ternary mixtures of zinc, cadmium, and mercury cations in aqueous solutions. Three types of commercially available metallochromic indicators were used as a simple model setup to create spectral data analogous to those normally received from an optical sensor array. In conventional ANN training methods for chemical sensors based on spectrophotometric data, a calibration is established by mathematically correlating the measured optical signal as network input with the concentration of the calibration sample as network output. In several situations, however, especially when dealing with mixed sample solutions, the relationship between a measured absorption spectrum and the corresponding ion concentrations is ambiguous, resulting in an "ill-posed problem". On the other hand, if the training direction is reversed by correlating known sample concentrations with measured optical signals, the relationship becomes reasonable for the ANN to obtain its structure. The proposed model illustrated in this paper is based on a more reasonable direct mapping and estimation by artificial neural network inversion (ANNI). In the training step, sample mixtures of known concentrations are optically measured to construct networks correlating the input data (ion concentrations) and the output data (absorption spectra). In the estimation step, the ion concentrations of unknown samples are estimated using the constructed ANN. The measured spectra of the unknown samples are fed to the output layer, and the appropriate input concentrations are determined by ANNI. When training the ANN system with 143 ternary mixtures of Zn2+, Cd2+, and Hg2+ in a concentration range from 1 to 100 microM, root-mean-square errors of prediction (RMSEP) of 0.45 (Zn2+), 0.96 (Cd2+), and 0.32 microM (Hg2+) were observed for the estimation of concentrations in 30 test samples, using the ANNI procedure. This newly proposed model, which involves the construction of an ANN based on direct mapping and estimation by ANNI, opens up one way to overcome the limitations of nonselective sensors, allowing the use of more easily accessible semiselective receptors to realize smart chemical sensing systems.     

形心和匹配跟踪算法的改进

从数字图象处理方法着手介绍了一些有效的跟踪算法 ,分析了这些算法的优缺点 ,并根据工程应用的要求 ,对这些算法进行了改进 ,以满足工程要求。实验表明这些改进后的算法对复杂背景中目标的跟踪性能有较大提高。     

High-dynamic-range magnetometry with a single electronic spin in diamond

Magnetic sensors capable of detecting nanoscale volumes of spins allow for non-invasive, element-specific probing. The error in such measurements is usually reduced by increasing the measurement time, and noise averaging the signal. However, achieving the best precision requires restricting the maximum possible field strength to much less than the spectral linewidth of the sensor. Quantum entanglement and squeezing can then be used to improve precision (although they are difficult to implement in solid-state environments). When the field strength is comparable to or greater than the spectral linewidth, an undesirable trade-off between field strength and signal precision occurs. Here, we implement novel phase estimation algorithms on a single electronic spin associated with the nitrogen-vacancy defect centre in diamond to achieve an ～8.5-fold improvement in the ratio of the maximum field strength to precision, for field magnitudes that are large (～0.3?mT) compared to the spectral linewidth of the sensor (～4.5?μT). The field uncertainty in our approach scales as 1/T(0.88), compared to 1/T(0.5) in the standard measurement approach, where T is the measurement time. Quantum phase estimation algorithms have also recently been implemented using a single nuclear spin in a nitrogen-vacancy centre. Besides their direct impact on applications in magnetic sensing and imaging at the nanoscale, these results may prove useful in improving a variety of high-precision spectroscopy techniques.     

振动信号频率跟踪的反馈修正自适应陷波器法

采用自适应陷波器跟踪振动信号频率时,存在持续跟踪精度不高的问题。通过分析指出持续跟踪精度不高的根本原因是ANF误差收敛至局部最优值,借鉴反馈控制思想,提出一种反馈修正ANF。根据ANF输入输出信号的相关性,设计频率跟踪精度评估因子,实时监控ANF频率跟踪精度。若评估因子小于给定阈值,则认为ANF丢失振动信号频率,通过反馈调整ANF参数而非重新初始化来适当增加陷波带宽,使其能重新跟踪到信号频率又具有较快的重新收敛速度。以一种基于Steiglitz-McBride方法的ANF(SMM-ANF)为例,分析了反馈修正策略,给出了流程和具体算法。仿真比较了格型ANF、SMM-ANF和反馈修正SMM-ANF的性能,给出了科里奥利质量流量计应用实例,结果表明：反馈修正SMM-ANF收敛速度稍慢于SMM-ANF,快于格型ANF,持续跟踪精度明显提高。     

声矢量信号双谱与互双谱估计算法

提出了声矢量信号双谱与互双谱估计算法,给出了算法的具体步骤。将算法应用于两类水中目标的特征提取,并用所提取特征构造了LMBP神经网络的输入向量集,对矢量水听器实测的水中目标进行了分类识别。识别结果验证了所提出算法的有效性。实验表明,B类目标识别率优于A类目标,原因是由于B类目标特征频率较集中,而A类目标特征频率较分散所致。互双谱特征分类结果优于双谱特征分类结果这个事实是与声压振速联合信号处理优于声压或振速单一信号处理相吻合的。     

Distributed inference in wireless sensor networks

Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized research in this area in recent years. The goal of this paper is to introduce the readers to these novel features and to summarize recent research developments in this area. In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the communication network connecting the sensors and the resource constraints at the sensors.     

矢量水听器阵列自适应子空间跟踪算法

矢量水听器能同时共点获得声场中声压和振速,与其他水听器相比,能获得更多的信息量,具有很好的应用前景。矢量水听器阵列的MUSIC算法能实现360°无模糊方位估计,然而对于方位时变的目标源,该算法很难完成对上述目标源方位进行实时跟踪估计。鉴于此,将MALASE算法和MUSIC算法相结合,提出了一种矢量水听器阵列的自适应子空间跟踪算法。仿真结果表明,该算法既保留了MUSIC算法的性能,又实现了对目标源进行实时跟踪估计,且方位估计误差仅为0．4°左右。     

圆柱结构声辐射估计中的振动传感器数目要求

使用近似Kirchhoff积分法估计点力源激励下圆柱壳体的辐射声压和声功率,研究壳体表面速度传感器均匀布放时传感器数目对估计精度的影响。单个场点声压估计和声功率估计误差给定的情况下,随着频率的升高,所需的传感器越来越多,但在场点声压级曲线或辐射声功率级曲线的峰值频率附近,需要的传感器数目会出现谷值;且激励源位置对单个场点声压估计时的传感器数目要求影响不大。进一步研究了全空间声压估计时的传感器数目需求,发现此时传感器需求数目曲线比单个场点声压估计时的传感器数目需求曲线平滑,不会在曲线峰值频率附近出现谷值。