Joint 2D-DOA and polarization estimation for sparse nonuniform rectangular array composed of spatially spread electromagnetic vector sensor

In this paper,a sparse nonuniform rectangular array based on spatially spread electromagnetic vector sensor(SNRASSEMVS)is introduced,and a method for estimating 2D-direction of arrival(DOA)and polarization is devised.Firstly,according to the special structure of the sparse nonuniform rectangular array(SNRA),a set of accurate but ambiguous direction-cosine estimates can be obtained.Then the steering vector of spatially spread electromagnetic vector sensor(SSEMVS)can be extracted from the array manifold to obtain the coarse but unambiguous direction-cosine estimates.Finally,the disambiguation approach can be used to get the final accurate estimates of 2DDOA and polarization.Compared with some existing methods,the SNRA configuration extends the spatial aperture and refines the parameters estimation accuracy without adding any redundant antennas,as well as reduces the mutual coupling effect.Moreover,the proposed algorithm resolves multiple sources without the priori knowledge of signal information,suffers no ambiguity in the estimation of the Poynting vector,and pairs the x-axis direction cosine with the y-axis direction cosine automatically.Simulation results are given to verify the effectiveness and superiority of the proposed algorithm.

Improved control for distributed parameter systems with time-dependent spatial domains utilizing mobile sensor-actuator networks

A guidance policy for controller performance enhancement utilizing mobile sensor-actuator networks （MSANs） is proposed for a class of distributed parameter systems （DPSs）, which are governed by diffusion partial differential equations （PDEs） with time-dependent spatial domains. Several sufficient conditions for controller performance enhancement are presented. First, the infinite dimensional operator theory is used to derive an abstract evolution equation of the systems under some rational assumptions on the operators, and a static output feedback controller is designed to control the spatial process. Then, based on Lyapunov stability arguments, guidance policies for collocated and non-collocated MSANs are provided to enhance the performance of the proposed controller, which show that the time-dependent characteristic of the spatial domains can significantly affect the design of the mobile scheme. Finally, a simulation example illustrates the effectiveness of the proposed policy.

Estimation of spatially distributed processes using mobile sensor networks with missing measurements

This paper investigates the estimation problem for a spatially distributed process described by a partial differential equation with missing measurements.The randomly missing measurements are introduced in order to better reflect the reality in the sensor network.To improve the estimation performance for the spatially distributed process,a network of sensors which are allowed to move within the spatial domain is used.We aim to design an estimator which is used to approximate the distributed process and the mobile trajectories for sensors such that,for all possible missing measurements,the estimation error system is globally asymptotically stable in the mean square sense.By constructing Lyapunov functionals and using inequality analysis,the guidance scheme of every sensor and the convergence of the estimation error system are obtained.Finally,a numerical example is given to verify the effectiveness of the proposed estimator utilizing the proposed guidance scheme for sensors.

基于环形阵列的油管损伤三维成像检测方法研究

传统的瞬变电磁套管损伤检测技术是通过采集数据来反映套管损伤情况的二次场信息,反演套管二维壁厚曲线,为套管损伤提供解释依据。为了使套管损伤解释得更加高效、直观、精准,满足井口油管三维成像的需求,建立一种基于环形阵列式的井口油管三维成像检测系统,利用探测仪器中环形阵列分布的TMR传感器,对井口油管各个方位的信息进行采集,使得采集到的数据能够描述不同深度、不同角度的损伤情况。结合套管半径、涡流扩散时间以及径向探测角度之间的关系,通过对井口三维数据的处理,实现套管内外壁三维成像,进而实现井口油管的非对称损伤解释。通过与传统的电磁探伤曲线的对比,验证了所提井口油管三维成像检测方法的有效性,为现场套管损伤解释工作提供重要依据。

基于TCPSO的三维无线传感器网络覆盖

针对三维无线传感器网络(3D WSNs)的节点部署问题,提出了一种基于双层混沌粒子群优化(TCPSO)算法的解决方案。利用TCPSO算法对节点进行优化部署,以提高网络的空间覆盖率。TCPSO算法通过非线性分类系数将种群分为精英种群和普通种群,分别采取不同的速度、位置更新公式进行迭代优化。TCPSO提出了基于Logistic混沌映射的递减惯性权重,用来控制算法在局部的开发,并且为了避免算法早熟,引入了Levy飞行策略增强算法的全局搜索能力。通过在1个三维网格空间中进行仿真实验,验证TCPSO以及粒子群优化(PSO)算法解决3D WSNs的节点部署问题的能力。分别在不同数量的节点数、不同的通信半径以及不同的种群规模上进行了3组实验,实验采用控制变量法,观察在不同条件下TCPSO的性能。TCPSO在全部的实验中提出的节点部署方案均明显优于PSO的部署方案。

Sensors deployment optimization in multi-dimensional space based on improved particle swarm optimization algorithm

Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space.Compared to the traditional form, sensors deployment in multidimensional space has greater research significance and practical potential to satisfy the detecting needs in complex environment.Aiming at solving this issue, a multi-dimensional space sensor network model is established, and the radar system is selected as an example. Considering the possible working mode of the radar system(e.g., searching and tracking), two distinctive deployment models are proposed based on maximum coverage area and maximum target detection probability in the attack direction respectively. The latter one is usually ignored in the previous literature.For uncovering the optimal deployment of the sensor network, the particle swarm optimization(PSO) algorithm is improved using the proposed weights determination scheme, in which the linear decreasing, the pooling strategy and the cloud theory are combined for weights updating. Experimental results illustrate the effectiveness of the proposed method.

基于声阵列时空关联特征融合的不平衡局部放电类型识别方法

麦克风阵列能非接触且灵活地对电力设备局部放电现象进行检测,但现有方法对麦克风阵列的数据特点考虑不足,对局放类型识别的研究不足。针对麦克风阵列数据的关联性特征和不平衡分布特点,首先对麦克风阵列数据的时间关联性和空间关联性特征进行深入分析。然后,以1维卷积神经网络和压缩-激活关联性挖掘方法为基础,提出基于时空关联特征融合的声阵列数据局部放电类型识别模型。最后,针对麦克风阵列数据类别间分布不平衡问题,使用损失函数调整法和数据分布调整法进行应对。仿真结果表明:相对不考虑关联性的方法,该文所提方法的精确率、召回率提升均大于12%;相对不考虑样本不均衡性方法,该文所用方法在精确率和召回率均提高大于60%,验证了基于声阵列数据的局放类型识别中考虑数据关联性和不平衡性的必要性。

基于改进人工免疫算法的天然气泄漏空间关联监测方法

为了实现对天然气泄漏的全面、快速检测,提出基于改进人工免疫算法的天然气泄漏空间关联监测方法。设计天然气泄漏空间关联监测网络,利用各传感器节点采集天然气管道上下游压力、流量、声波等数据。为不同位置传感网络节点分配不同强度等级,以高级网络节点为标准节点,结合各节点位置权值判定相邻节点。各相邻节点间,基于空间相关性,通过滑动窗口的相似度感知异常天然气数据,并通过改进人工免疫算法聚类分析天然气数据空间感知结果,实现天然气泄漏空间关联监测。实验结果说明,所提方法可以有效监测出天然气管道泄漏情况,并即刻断定泄漏地点,监测相对偏差平均值仅为0.12,应用所提方法可提升天然气管道的安全指数,降低管道被损毁程度。

基于深度学习的矿用救援机器人自动避障方法

救援机器人自动避障性能存在不足,在实际中救援成功率和避障平滑系数较低,无法达到预期的避障效果,为此,提出基于深度学习的矿用救援机器人自动避障方法。首先,利用电子罗盘和超声波传感器感知救援机器人与障碍物的方位角和距离,搭建救援机器人空间状态;然后,建立具有三层卷积层和两层全连接层结构的深度学习网络模型,并搭建用于深度学习网络模型训练的救援机器人避障动作集合;最后,通过深度学习网络模型训练救援机器人的空间状态信息,提取救援机器人移动的空间特征,自动生成避障决策。实践证明,应用该设计方法后,救援机器人避障成功率在95%以上,平滑系数在0.85以上,具有良好的应用前景。

基于Lin-GA的磁性示踪粒子六自由度检测技术

针对基于永磁体和磁传感器阵列的磁定位系统中定位算法存在执行速度慢、定位精度低等问题,提出基于线性算法和遗传算法(linear algorithm-genetic algorithms, Lin-GA)的混合搜索算法。利用自制定位装置中的三轴隧道磁阻(TMR)传感器阵列采集磁性示踪粒子在空间产生的弱磁信号,首先通过线性算法利用磁场绝对向量信息进行粗定位,再利用遗传算法对示踪粒子的六自由度参数进行并行寻优,最终实现对磁性示踪粒子三维位置和三维方向的空间信息反演。实验结果表明,基于Lin-GA的混合搜索算法具有更高的定位精度和更短的执行时间,平均定位误差为(0.55±0.10) mm,平均定向误差为(0.68±0.30)°,平均执行时间为(30.49±4.89) s。

基于多传感器融合的行人异常行为识别方法

常规的行人异常行为识别方法无法针对行人行为特征进行识别,导致识别准确性较低,因此,设计了一种基于多传感器融合的行人异常行为识别方法。在多传感器融合中,通过摄像头得到人体不同行为的最小外接矩形框,并计算出人的宽高比,以反映行人的运行特征;然后根据平行式双目视觉测量原理,获取行人位置信息,并利用空间对齐的方式,获得行人位置信息,实现行人异常行为点云信息与图像信息一致的目标,从而提高信息融合的精准度;最后结合行人的步长、速度、方向等轨迹数据对行人异常行为进行分类,并通过奖励或惩罚机制优化分类识别结果,完成行人异常行为识别。实验结果表明,使用该方法进行行人异常行为识别时,行人异常行为识别的平均精确度在0.98~1.00的范围内变化,行人异常行为识别的均值平均精度在0.97~1.00的范围内变化。由此表明,该方法的识别准确性更高,能够应用于实际生活中。

一种基于目标点云分布特性的动态聚类算法

激光雷达在自动驾驶系统的目标检测任务中发挥着重要作用,但其扫描机理会使得点云分布不均匀,常规聚类算法由于参数固定会导致较多的错误聚类。为解决该问题,该文以椭圆形状作为邻域空间,设计基于采样点位置的邻域自适应调整策略,提出一种基于目标点云分布特性的动态聚类算法。通过正确聚类、过聚类等综合结果评估算法的性能,在KITTI数据集上进行了数值分析得到算法参数,并在校园环境中进行了实车对比实验。结果表明:所提算法能减少基于密度的噪声应用空间聚类(DBSCAN)中固定邻域所造成的70.60%过聚类、49.76%欠聚类等错误结果,从而有效提高算法的综合聚类性能。

基于包络线的防风垃圾桶空间凸轮弧面设计

针对目前凸轮的设计方法不能实现某些特殊零部件从动件空间准确动作的设计要求,提出采用包络线方法的凸轮轮廓弧面设计方法,该设计方法可实现凸轮准确控制垃圾桶盖因风力大小改变开度的能力。首先基于凸轮的设计原则,建立简单平面凸轮的包络曲线模型,对包络线方程进行求解,然后利用感风桶风力方向的所在平面建立相应包络线曲线模型,形成相交空间包络曲线簇,而后组集凸轮轮廓点数据,建立平行空间包络曲线簇,通过随后的轮廓点拟合建立平行空间包络曲线簇方程,并考查空间包络曲线拟合方程参数随曲线簇垂直方向坐标的变化,而后通过两组拟合参数建立曲面求解数值方程。该空间凸轮轮廓弧面设计方法可准确设计凸轮的空间轮廓,并能实现凸轮的准确控制。该空间凸轮轮廓弧面设计方法过程连续,求解规范可实现程序移植推广。

A new measure for evaluating spatially related properties of traffic information credibility

With the wide applications of sensor network technology in traffic information acquisition systems,a new measure will be quite necessary to evaluate spatially related properties of traffic information credibility.The heterogeneity of spatial distribution of information credibility from sensor networks is analyzed and a new measure,information credibility function(ICF),is proposed to describe this heterogeneity.Three possible functional forms of sensor ICF and their corresponding expressions are presented.Then,two feasible operations of spatial superposition of sensor ICFs are discussed.Finally,a numerical example is introduced to show the calibration method of sensor ICF and obtain the spatially related properties of expressway in Beijing.The results show that the sensor ICF of expressway in Beijing possesses a negative exponent property.The traffic information is more abundant at or near the locations of sensor,while with the distance away from the sensor increasing,the traffic information credibility will be declined by an exponential trend.The new measure provides theoretical bases for the optimal locations of traffic sensor networks and the mechanism research of spatial distribution of traffic information credibility.

Implementation of spatial touch system using time-of-flight camera

Recently developed time-of-flight principle based depth-sensing video camera technologies provide precise per-pixel range data in addition to color video. Such cameras will find application in robotics and vision-based human computer interaction scenarios such as games and gesture input systems. Time-of-flight principle range cameras are becoming more and more available. They promise to make the 3D reconstruction of scenes easier, avoiding the practical issues resulting from 3D imaging techniques based on triangulation or disparity estimation. A spatial touch system was presented which uses a depth-sensing camera to touch spatial objects and details on its implementation, and how this technology will enable new spatial interactions was speculated.

Spatially smoothed quaternion-Capon beamforming in the presence of coherent interferences

The performance the quaternion-Capon（ Q-Capon） beamformer degraded when suppressing the interferences that are coherent with the signal of interest（ SOI）. To tackle the problem,the spatial smoothing technique is adopted in quaternion domain to decorrelate the interferences by using linearly and uniformly spaced two-component electromagnetic vector-sensors. By averaging several translational invariant subarray quaternion covariance matrices,the quaternion spatial smoothing is performed to prevent the SOI cancellation phenomena caused by the presence of coherent interferences. It is demonstrated that the quaternion spatial smoothing Q-Capon beamformer can suppress the coherent interferences remarkably while the computational cost is lower than the complex domain long vector spatial smoothing counterpart. Theoretical analyses and simulation results validate the efficacy of the spatially smoothed Q-Capon beamformer in terms of coherent interference suppression capability.

Spatial Distribution of Surface Soil Moisture in a Small Forested Catchment

Predicting the spatial distribution of soil moisture is an important hydrological question. We measured the spatial distribution of surface soil moisture (upper 6 cm) using an Amplitude Domain Reflectometry sensor at the plot scale (2 × 2 m) and small catchment scale (0.84 ha) in a temperate forest. The spatial variation of soil water content was higher during dry conditions than that during wet conditions. Results indicated 3.1 samples at the plot scale were sufficient to estimate mean soil water content when the precision was 0.1. Soil water content increased with increasing topographic index (TI) and soil-topographic index (STI) at the small catchment scale. The correlation between soil water content and TI was higher than that between soil water content and STI. This suggests that topography is more important for estimating surface soil moisture than soil depth as formation of surface soil moisture occurs at ≤6 cm.

Media Access with Spatial Reuse for Cooperative Spectrum Sensing

The increasing interest for wireless communication services and scarcity of radio spectrum resources have created the need for a more flexible and efficient usage of the radio frequency bands. Cognitive Radio (CR) emerges as an important trend for a solution to this problem. Spectrum sensing is a crucial function in a CR system. Cooperative spectrum sensing can overcome fading and shadowing effects, and hence increase the reliability of primary user detection. In this paper we consider a system model of a dedicated detect-andforward wireless sensor network (DetF WSN) for cooperative spectrum sensing with k-out-of-n decision fusion in the presence of reporting channels errors. Using this model we consider the design of a spatial reuse media access control (MAC) protocol based on TDMA/OFDMA to resolve conflicts and conserve resources for intra-WSN communication. The influence of the MAC protocol on spectrum sensing performance of the WSN is a key consideration. Two design approaches, using greedy and adaptive simulated annealing (ASA) algorithms, are considered in detail. Performance results assuming a grid network in a Rician fading environment are presented for the two design approaches.

船用柴油机机体孔系平行度在机检测方法

船用柴油机机体孔系平行度的高精度在机检测是制约其制造过程智能化水平的瓶颈技术之一。为此,基于多传感器技术,提出一种空间孔系平行度在机检测方法。在传感器不放入孔内的情况下,仅将测头伸入孔内进行测量,由多个位移传感器的测量值,根据对应传感器所处的位置,计算获得孔系平行度大小。结果表明,该检测方法可有效对空间孔系平行度进行检测,为孔系零件高效在机检测提供了理论基础。

一种联合时域和空域残差的网络异常检测与节点定位方法

无线传感器网络中,有效检测网络异常并定位异常节点是确保数据可靠性的前提.传统的基于图信号处理的网络异常检测和异常节点定位方法无法兼顾检测性能和定位性能.为克服此缺点,提出了一种联合图信号时域和空域残差的网络异常检测与节点定位方法 .首先,建立一个基于历史数据相关性和节点距离的图信号模型.接着,联合图信号高频分量的时域残差和空域残差实现网络异常检测.然后,利用图信号时域残差把传感器节点分成两组.在分组过程中,通过最大化有序残差分组间的均值差将异常节点划分到同一组.最后,判定具有较大残差值分组的传感器节点为异常节点.基于全球海平面压力和温度数据的仿真结果表明了所提方法的有效性.针对异常节点海平面压力误差为4 kPa、温度误差为5℃和3℃的三种情况,与双通道图滤波方法相比,所提方法的检测概率提高了至少20%,正确定位率提高了至少15%.