基于迭代卡尔曼滤波器的GPS-激光-IMU融合建图算法

在当前机器人导航和环境感知领域,室外大尺度场景下的三维激光SLAM一直是一个挑战性问题。由于GPS信号在某些环境下的不稳定性和激光SLAM的误差累积特性,传统算法在大尺度场景下表现不佳。针对室外大尺度场景下三维激光SLAM(同步定位和地图构建)存在的误差累积严重问题,本文提出了一种基于迭代卡尔曼滤波器的GPS-激光-IMU融合建图算法。该算法通过利用惯性测量单元(IMU)数据对机器人状态进行预测,同时以激光和全球定位系统(GPS)数据作为观测,更新机器人状态,推导出观测方程和雅可比矩阵,显著提高了建图的精度和鲁棒性。里程计中融合GPS数据的绝对位置信息以解决长时间运行中的误差累积问题。在特征稀疏的环境中,由于约束不足可能导致算法崩溃,GPS数据的引入可以提高系统的鲁棒性。此外,重力对于IMU数据预测机器人状态起到关键的作用。虽然重力是三维向量,但在不发生区域变化的情况下,其模长是不变的,因此被视为二自由度向量。通过将重力的优化转化为旋转矩阵群上的优化,成功避免了重力过参数化的问题,提高了算法的精度。在室外场景下与其他算法进行了性能测试对比并且验证了在大尺度场景下的鲁棒性和精度,结果表明:本文算法的均方根误差为0.089 m,与其他算法相比降低了54%。

混合灰狼-自适应蝴蝶算法下的多目标无线传感器网络覆盖研究

为提高无线传感器网络的覆盖率、减少二次部署中节点移动的能量消耗以及减少牵涉节点数目,提出了一种混合灰狼-自适应蝴蝶算法.首先,通过将灰狼算法与蝴蝶算法融合寻找最优解,在种群间优化中加入种群内部优化,提升算法的寻优能力,提高算法收敛速度;其次,在蝴蝶算法中改进自适应开关概率,根据当前迭代情况决定寻优方式,加快搜索速度;最后,在算法中融入反馈机制,引入随机蝴蝶进行漫步,防止陷入局部最优.从最优覆盖、最小冗余、最低能量消耗3个方向综合考量,建立函数模型,进行覆盖优化.实验结果表明,与其他6种算法相比,本文算法在多种场景下,有效地提高覆盖率,降低节点冗余,减少节点使用数目,降低能量消耗.

基于CNN-SE-LSTM和多传感器数据的轴向柱塞泵故障诊断

轴向柱塞泵是液压系统中的核心部件,其状态监测和故障诊断是保证液压系统安全可靠运行的关键。然而,由于轴向柱塞泵结构复杂,工作环境恶劣,采集的信号中往往夹杂着强烈的噪声,利用单传感器数据监测其健康状态往往达不到预期效果。为此,提出一种基于通道注意力机制的卷积神经网络-长短期记忆网络(CNN-LSTM)和多传感器数据(MSD)的轴向柱塞泵故障诊断方法。改进CNN中卷积核的尺寸来优化CNN-LSTM结构参数,提高模型抗噪性能,并引入通道注意力机制模块SENet提升模型的表征能力,然后将2个不同位置的振动传感器数据进行数据端通道融合作为输入,最后将融合后的数据输入改进CNN-SE-LSTM中并通过Softmax层输出诊断结果。实验结果表明:在不添加噪声的情况下,所提方法故障诊断准确率达100%,具有较好的准确性和快速性;在不同信噪比的噪声干扰下,所提方法相比多层感知器(MLP)、首层宽卷积核深度卷积神经网络(WDCNN)等模型具有更高的故障诊断准确率,鲁棒性更好。

基于IQPSO-EKF的多传感器融合姿态测量方法研究

为解决自动化竖井掘进设备的定位调姿精度对竖井、孔桩挖掘效率与质量的影响,提出了一种基于改进量子粒子群(IQPSO)-扩展卡尔曼滤波(EKF)的姿态测量算法,以提高微机电系统(MEMS)传感器测量精度。首先,对MEMS传感器数据进行了预处理(除噪、滤波、校准等);然后,参考现有飞行器的坐标系,建立了姿态解算模型,通过姿态角数学模型及运动学分析,构建了EFK状态方程,针对EKF方法参数估计不准确的问题,以分段混沌映射优化初始种群,引入平均位置最优值来避免陷入局部最优的IQPSO-EFK算法,优化EKF的系统、测量噪声的协方差参数;最后,对改进算法和三组姿态误差估计进行了对比实验。研究结果表明:对比三种典型目标函数,IQPSO-EFK相较于普通粒子群算法(QPSO-EFK)具有更强的寻优能力与收敛精度;对比三组旋转速度姿态测量误差,基于IQPSO-EKF算法的姿态测量方法在测量误差时比真实测量误差减少了约86.3%,比扩展卡尔曼滤波减少了约68.7%,比普通粒子群算法减少了约28.2%,证明该算法有效地提高了MEMS传感器测量精度。

面向降频感知平台的视觉-惯性-轮式里程估计算法

受地外能源稀缺性的影响,探测器通常降低传感器频率以限制功耗。针对降频传感里程估计系统中存在的惯性先验退化问题,进行了基于轮速观测的先验融合以及面向低速平台的视觉算法改进。基于相机频率降为1 Hz,惯性测量单元(IMU)频率降为4 Hz的探测器地面样机展开实验。为实现降频感知平台的高精度里程估计,提出一种基于多状态约束卡尔曼滤波(MSC⁃KF)的视觉-惯性-轮式多传感器融合算法,并通过相机外参在线标定算法实现探测器长时间运行中自动修正系统参数,最后使用零速检测和状态保持等方法进一步抑制机器人静止模式的状态退化。在公开数据集进行充分验证后,基于探测器地面样机在地外复杂环境模拟场地进行了实验,结果表明:可以实现1.2%以内的累计误差,在降频感知平台上实现了高于现有最优方法的里程估计精度,能有效应用于地外探测器的自主导航功能实现。

多传感器融合和MHA-LSTM的电机轴承剩余寿命预测

轴承作为电机的核心部件,主要起到支撑引导轴、减小设备摩擦、连接不同设备等作用,其剩余寿命预测对系统健康管理起着十分重要的作用。针对单一传感器信号通常难以全面描述系统的潜在退化机制,论文提出一种基于多头注意力机制和长短时记忆神经网络的电机轴承剩余寿命预测模型。首先,基于马氏距离确定轴承性能退化起始点,将滚动轴承全寿命周期分为正常阶段与退化阶段;其次,使用自编码器自动提取振动信号特征,并将其与电机电流、轴承温度融合,构成多源信息特征矩阵;然后基于多头注意力机制和长短时记忆网络模型动态选择相关度较高的特征,提高寿命预测的准确性。最后,采用实验数据进行验证,结果表明所提出的模型具有更高的准确性。

基于多传感器信息融合和CNN-BIGRU-Attention模型的液压防水阀故障诊断方法

在建筑工程领域,尤其是在工程搅拌设备中,涉及到复杂液压工作介质,液压防水阀往往会出现不同程度的故障。此外,恶劣的工作环境和复杂的噪声背景使得对液压防水阀的故障进行诊断变得困难。为了解决这一难题,提出了一种基于多传感器信息融合和卷积神经网络-双向门控循环单元-自注意力机制(CNN-BIGRU-Attention)模型的防水阀故障诊断方法。首先,考虑到单一传感器振动信号难以充分表达故障特征,该方法使用了3个传感器采集含噪声的振动信号,并进行了必要的预处理;其次,提取了信号的16个时域特征、5个频域特征以及3个时频域特征,并利用熵权法进行了特征融合,达到了增强特征的目的;然后,将融合的多维特征集输入到CNN-BIGRU-Attention模型中进行了特征识别;最后,利用实际的液压防水阀故障诊断实验,验证了该方法的有效性。研究结果表明:采用多传感器提取的特征更为全面,信息融合有助于捕捉更完整的隐藏数据特征,从而显著提高诊断的准确率;相较于其他特征识别方法,采用CNN-BIGRU-Attention模型的液压防水阀故障诊断准确率可分别提高6.7%、4.6%和14.2%,达到了96.86%,证明了该方法的有效性。该方法将先进的机器学习技术与实际工程应用相结合,为建筑工程问题提供了一种新颖、有效的解决方案。

Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

STUDY ON THE COAL-ROCK INTERFACE RECOGNITION METHOD BASED ON MULTI-SENSOR DATA FUSION TECHNIQUE

The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.

基于IMSGP-WEDI的水电机组故障预警方法

水电机组故障预警指标对于机组早期故障预警时间影响较大,而当前预警指标多基于单传感器信息构建,且其特征信息单一,难以更全面地表征机组运行状态,针对此问题,提出了一种基于集成多传感器遗传规划(integrated multi-sensor genetic programming,IMSGP)与权重欧式距离指标(weighted euclidean distance index,WEDI)的水电机组故障预警方法。首先,将多传感器信号进行预处理,剔除干扰信息;然后从预处理后的信号中提取多元特征,构建原始预警特征集;接下来利用复合检测指数(composite detection index,CDI)进行特征选择,并利用IMSGP进行特征构造;最后结合主成分分析(principal component analysis,PCA)与欧式距离构建WEDI,判别机组异常状态。通过对水电机组实测数据的分析,证明了提出的方法可及时发现早期故障,实现故障预警。

Design and construction of a multi-sensor position monitoring system applied to key components of synchrotron sources

High-accuracy position monitoring of key components is required for modern synchrotron sources,such as free-electron lasers and diffraction-limited storage rings.Although various position monitoring sensors have been adopted to monitor the displacement of key components in each direction in real time,these monitoring systems are usually based on their own coordinate system.Data from such systems are meaningful when evaluating and examining the data from each positioning monitoring system in a unified coordinate system.This paper presents the design and construction of a multi-sensor position monitoring system(MPMS).A hydrostatic levelling system,a wire position sensor(WPS) and a tiltmeter are fixed to a stainless steel plate that has been calibrated by a coordinate-measurement machine.Several plates form the MPMS.The system must compensate for the sag of the stretched wires so that the WPSs create a straight line.The method of the coordinate transformation from the sensor coordinate system to the MPMS coordinate system was thoroughly studied.An experimental MPMS that includes five plates was setup in a 20-m tunnel,and a validation study to verify fully the feasibility of the MPMS was performed.

基于自适应多尺度注意力机制的CNN-GRU矿用电动机健康状态评估

利用多传感器信息融合技术进行电动机健康状态评估时,矿用电动机监测数据中存在异常值和缺失值,而卷积神经网络和循环神经网络等深度学习模型在数据质量下降严重的情况下难以有效提取数据特征和更新网络权重,导致梯度消失或爆炸等问题。针对上述问题,提出了一种基于自适应多尺度注意力机制的CNN-GRU(CNN-GRU-AMSA)模型,用于评估矿用电动机健康状态。首先,对传感器采集的电动机运行数据进行填补、剔除和标准化处理,并以环境温度变化作为依据对矿用电动机运行数据进行工况划分。然后,根据马氏距离计算出电动机电流、电动机三相绕组温度、电动机前端轴承温度和电动机后端轴承温度等健康评估指标的健康指数(HI),采用Savitzky–Golay滤波器对指标HI进行降噪、平滑、归一化处理,并结合主成分分析法计算的不同指标对矿用电动机的贡献度,对指标HI进行加权融合得到矿用电动机HI。最后,将矿用电动机HI输入CNN-GRU-AMSA模型中,该模型通过动态调整注意力权重,实现对不同尺度特征的信息融合,从而准确输出电动机健康状态评估结果。实验结果表明,与其他常见的深度学习模型CNN,CNN-GRU,CNN-LSTM,CNN-LSTM-Attention相比,CNN-GRU-AMSA模型在均方根误差、平均绝对误差、准确率、Macro F1及Micro F1等评价指标上更优,且预测残差的波动范围更小,稳定性更优。

New multi-layer data correlation algorithm for multi-passive-sensor location system

Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective.

A multi-channel chemical sensor and its application in detecting hydrothermal vents

There are well-established chemical and turbidity anomalies in the plumes occurring vicinity of hydrothermal vents, which are used to indicate their existence and locations. We here develop a small, accurate multi-channel chemical sensor to detect such anomalies which can be used in deep-sea at depths of more than 4 000 m. The design allowed five all-solid-state electrodes to be mounted on it and each (apart from one reference electrode) could be changed according to chemicals to be measured. Two experiments were conducted using the chemical sensors. The first was a shallow-sea trial which included sample measurements and in situ monitoring. pH, Eh, CO3^2- and SO4^2- electrodes were utilized to demonstrate that the chemical sensor was accurate and stable outside the laboratory. In the second experiment, the chemical sensor was integrated with pH, Eh, CO3^2- and H2S electrodes, and was used in 29 scans of the seabed along the Southwest Indian Ridge (SWIR) to detect hydrothermal vents, from which 27 sets of valid data were obtained. Hydrothermal vents were identified by analyzing the chemical anomalies, the primary judging criteria were decreasing voltages of Eh and H2S, matched by increasing voltages of pH and CO3^2- . We proposed that simultaneous detection of changes in these parameters will indicate a hydrothermal vent. Amongst the 27 valid sets of data, five potential hydrothermal vents were targeted using the proposed method. We suggest that our sensors could be widely employed by marine scientists.

MRBCH: A Multi-Path Routing Protocol Based on Credible Cluster Heads for Wireless Sensor Networks

Wireless sensor networks are widely used for its flexibility, but they also suffer from problems like limited capacity, large node number and vulnerability to security threats. In this paper, we propose a multi-path routing protocol based on the credible cluster heads. The protocol chooses nodes with more energy remained as cluster heads at the cluster head choosing phase, and then authenticates them by the neighbor cluster heads. Using trust mechanisms it creates the credit value, and based on the credit value the multi-path cluster head routing can finally be found. The credit value is created and exchanged among the cluster heads only. Theoretical analysis combined with simulation results demonstrate that this protocol can save the resource, prolong the lifetime, and ensure the security and performance of the network.

An Indoor Pedestrian Localization Algorithm Based on Multi-Sensor Information Fusion

For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion. The pedestrian’s localization in indoor environment is described as dynamic system state estimation problem. The algorithm combines the smart mobile terminal with indoor localization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization information have been used to improve the filtering precision and the stability. Moreover, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic changes of the indoor environment. Experimental results show that the algorithm based on multi-sensor improves the localization accuracy and robustness compared with the location algorithm based on Wi-Fi.

Multi-type sensor placement and response reconstruction for building structures: Experimental investigations

Estimation of lateral displacement and acceleration responses is essential to assess safety and serviceability of high-rise buildings under dynamic loadings including earthquake excitations. However, the measurement information from the limited number of sensors installed in a building structure is often insufficient for the complete structural performance assessment. An integrated multi-type sensor placement and response reconstruction method has thus been proposed by the authors to tackle this problem. To validate the feasibility and effectiveness of the proposed method, an experimental investigation using a cantilever beam with multi-type sensors is performed and reported in this paper. The experimental setup is first introduced. The finite element modelling and model updating of the cantilever beam are then performed. The optimal sensor placement for the best response reconstruction is determined by the proposed method based on the updated FE model of the beam. After the sensors are installed on the physical cantilever beam, a number of experiments are carried out. The responses at key locations are reconstructed and compared with the measured ones. The reconstructed responses achieve a good match with the measured ones, manifesting the feasibility and effectiveness of the proposed method. Besides, the proposed method is also examined for the cases of different excitations and unknown excitation, and the results prove the proposed method to be robust and effective. The superiority of the optimized sensor placement scheme is finally demonstrated through comparison with two other different sensor placement schemes： the accelerometer-only scheme and non-optimal sensor placement scheme. The proposed method can be applied to high-rise buildings for seismic performance assessment.

Monitoring Land-Use Change in Nakuru (Kenya) Using Multi-Sensor Satellite Data

Recently land-use change has been the main concern for worldwide environment change and is being used by city and regional planners to design sustainable cities. Nakuru in the central Rift Valley of Kenya has undergone rapid urban growth in last decade. This paper focused on urban growth using multi-sensor satellite imageries and explored the potential benefits of combining data from optical sensors (Landsat, Worldview-2) with Radar sensor data from Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data for urban land-use mapping. Landsat has sufficient spectral bands allowing for better delineation of urban green and impervious surface, Worldview-2 has a higher spatial resolution and facilitates urban growth mapping while PALSAR has higher temporal resolution compared to other operational sensors and has the capability of penetrating clouds irrespective of weather conditions and time of day, a condition prevalent in Nakuru, because it lies in a tropical area. Several classical and modern classifiers namely maximum likelihood (ML) and support vector machine (SVM) were applied for image classification and their performance assessed. The land-use data of the years 1986, 2000 and 2010 were compiled and analyzed using post classification comparison (PCC). The value of combining multi-temporal Landsat imagery and PALSAR was explored and achieved in this research. Our research illustrated that SVM algorithm yielded better results compared to ML. The integration of Landsat and ALOS PALSAR gave good results compared to when ALOS PAL- SAR was classified alone. 19.70 km2 of land changed to urban land-use from non-urban land-use between the years 2000 to 2010 indicating rapid urban growth has taken place. Land-use information is useful for the comprehensive land-use planning and an integrated management of resources to ensure sustainability of land and to achieve social Eq- uity, economic efficiency and environmental sustainability.

基于GA-BP神经网络的多无人机协同目标定位

无人机作为高机动平台,往往存在较大的自身位置误差。针对该情况下特定区域内的辐射源目标,提出了一种遗传算法优化反向传播(genetic algorithm optimized back-propagation,GA-BP)神经网络的实时定位方法。首先从特定区域中获取不同已知目标的位置信息作为网络的期望输出,并计算它们的到达时间差作为网络的输入,构建训练数据集;然后利用遗传算法的自适应性,优化BP神经网络初始权重和阈值,使其能够快速跳出局部最优解,实现高精度定位。通过训练得到相应的GA-BP网络模型,未知目标可以通过该模型进行实时定位。仿真实验将该算法与两步加权最小二乘算法以及BP神经网络的定位结果进行对比,结果表明所提的方法定位精度更接近克拉美罗界。