Fast filtering algorithm based on vibration systems and neural information exchange and its application to micro motion robot

This paper develops a fast filtering algorithm based on vibration systems theory and neural information exchange approach. The characters, including the derivation process and parameter analysis, are discussed and the feasibility and the effectiveness are testified by the filtering performance compared with various filtering methods, such as the fast wavelet transform algorithm, the particle filtering method and our previously developed single degree of freedom vibration system filtering algorithm, according to simulation and practical approaches. Meanwhile, the comparisons indicate that a significant advantage of the proposed fast filtering algorithm is its extremely fast filtering speed with good filtering perfi^rmance. Further, the developed fast filtering algorithm is applied to the navigation and positioning system of the micro motion robot, which is a high real-time requirement for the signals preprocessing. Then, the preprocessing data is used to estimate the heading angle error and the attitude angle error of the micro motion robot. The estimation experiments illustrate the high practicality of the proposed fast filtering algorithm.

Digital Channelization Technology for HF Communication Base on Fast Filter Bank

Channelization typically realized by digital filter banks is an important topic in high frequency(HF) communication and software defined radios(SDR) areas. Channelization has a rigorous requirement for the characteristic of frequency response, e.g., steep transitional band and sharp decay. To address this issue, we investigated the feasibility and implementation of applying fast filter bank(FFB) in channelization in this paper. We analyzed the butterfly structure of FFB similar with fast Fourier transform(FFT), in which prototype sub-filters are cascaded to achieve a low complexity. Hence, it is suitable for designing filter bank with steep transitional band and sharp decay in stop-band. Moreover, we designed a pipelined structure of FFB to achieve a balance between area and performance. Design example shows that FFB has lower computational complexity compared with the other filter banks.

Effective implementation and improvement of fast labeled multi-Bernoulli filter

Effective implementation of the fast labeled multi-Bernoulli(FLMB)filter is addressed for target tracking with interval measurements.Firstly,a sequential Monte Carlo(SMC)implementation of the FLMB filter,SMC-FLMB filter,is derived based on generalized likelihood function weighting.Then,a box particle(BP)implementation of the FLMB filter,BP-FLMB filter,is developed,with a computational complexity reduction of the SMC-FLMB filter.Finally,an improved version of the BP-FLMB filter,improved BP-FLMB(IBP-FLMB)filter,is proposed,improving its estimation accuracy and real-time performance under the conditions of low detection probability and high clutter.Simulation results show that the BP-FLMB filter has a great improvement of the real-time performance than the SMC-FLMB filter,with similar tracking performance.Compared with the BP-FLMB filter,the IBP-FLMB filter has better estimation performance and real-time performance under the conditions of low detection probability and high clutter.

A novel fast classification filtering algorithm for LiDAR point clouds based on small grid density clustering

Clustering filtering is usually a practical method for light detection and ranging(LiDAR)point clouds filtering according to their characteristic attributes.However,the amount of point cloud data is extremely large in practice,making it impossible to cluster point clouds data directly,and the filtering error is also too large.Moreover,many existing filtering algorithms have poor classification results in discontinuous terrain.This article proposes a new fast classification filtering algorithm based on density clustering,which can solve the problem of point clouds classification in discontinuous terrain.Based on the spatial density of LiDAR point clouds,also the features of the ground object point clouds and the terrain point clouds,the point clouds are clustered firstly by their elevations,and then the plane point clouds are selected.Thus the number of samples and feature dimensions of data are reduced.Using the DBSCAN clustering filtering method,the original point clouds are finally divided into noise point clouds,ground object point clouds,and terrain point clouds.The experiment uses 15 sets of data samples provided by the International Society for Photogrammetry and Remote Sensing(ISPRS),and the results of the proposed algorithm are compared with the other eight classical filtering algorithms.Quantitative and qualitative analysis shows that the proposed algorithm has good applicability in urban areas and rural areas,and is significantly better than other classic filtering algorithms in discontinuous terrain,with a total error of about 10%.The results show that the proposed method is feasible and can be used in different terrains.

Multi-source image fusion algorithm based on fast weighted guided filter

In last few years,guided image fusion algorithms become more and more popular.However,the current algorithms cannot solve the halo artifacts.We propose an image fusion algorithm based on fast weighted guided filter.Firstly,the source images are separated into a series of high and low frequency components.Secondly,three visual features of the source image are extracted to construct a decision graph model.Thirdly,a fast weighted guided filter is raised to optimize the result obtained in the previous step and reduce the time complexity by considering the correlation among neighboring pixels.Finally,the image obtained in the previous step is combined with the weight map to realize the image fusion.The proposed algorithm is applied to multi-focus,visible-infrared and multi-modal image respectively and the final results show that the algorithm effectively solves the halo artifacts of the merged images with higher efficiency,and is better than the traditional method considering subjective visual consequent and objective evaluation.

Fast Rate Fault Detection Filter for Multirate Sampled-data Systems

This paper focuses on the fast rate fault detection filter (FDF) problem for a class of multirate sampled-data (MSD) systems. A lifting technique is used to convert such an MSD system into a linear time-invariant discrete-time one and an unknown input observer (UIO) is considered as FDF to generate residual. The design of FDF is formulated as an H∞ optimization problem and a solvable condition as well as an optimal solution are derived. The causality of the residual generator can be guaranteed so that the fast rate residual can be implemented via inverse lifting. A numerical example is included to demonstrate the feasibility of the obtained results.

采用局部-全局区域重检测机制的无人机长期跟踪算法

为解决基础跟踪器面对遮挡和移出视野等长期跟踪场景时易出现跟踪失败等问题,提出了一种基于局部-全局区域重检测的无人机长期跟踪算法。设计了基础滤波器,将高置信度样本与其结合,并融入自适应时空正则化,解决了滤波器退化问题,提高了模型鲁棒性以及复杂场景下的性能;优化了滤波器更新策略,通过评价跟踪结果进行自适应更新;设计快速尺度滤波器,解决了跟踪过程中的尺度变化问题;设计了局部-全局区域重检测机制,跟踪失败时启动重检测器恢复跟踪目标,先完成局部区域重检测,若恢复跟踪失败,再利用全局区域重检测器继续恢复目标跟踪状态。实验结果表明:所提算法在UAV20L数据集上的精确度和准确率分别可达0.724和0.621,与基于时空正则化相关滤波器的跟踪算法(STRCF)相比分别提升了25.9%和20.6%,与同类主流算法相比,跟踪效果得到提升,证明了算法的有效性。

基于快速局部对比度和目标特征的星图弱小目标检测算法

为了解决局部对比度方法在用于星图空间目标检测时存在运算量大和去除背景噪声困难的问题,提出了一种基于快速局部对比度和目标特征的方法来检测目标。在对比度计算前、对比度计算中和对比度计算后3个环节,分别提高了算法实时性、对复杂背景的抑制和去除噪声。首先,通过中值滤波去除高频噪声;然后,通过快速局部极大值滤波确定目标区域,通过局部对比度计算抑制背景,突出目标成像特征;最后,根据目标成像特征,设置目标能量分布、目标能量集中和目标能量传递3个特征函数,通过设置特征阈值去除噪声,提取真实目标。实验结果表明,本文所提方法在检测率和时间消耗上均具有优越性,对于信噪比为1.5的目标有95%的检测率,平均耗时仅为某些对比方法的1/30~1/6。本文所提方法更适用于星图复杂背景条件下的目标快速检测,满足星图空间目标检测算法鲁棒性强、实时性高的要求。

基于车载激光点云的城市道路标线提取方法

针对车载激光点云道路标线反射强度特性,提出了一种基于车载激光点云的城市道路标线提取方法。具体而言,首先提出一种联合布料模拟滤波和高差偏度平衡的地面滤波方法,利用偏度平衡滤波的自适应性,剔除布料滤波后残留的低矮植被的问题;随后利用基于法向量密度聚类以提取路面点云,并通过反距离加权插值将路面点云转为强度特征图;为了缓解标线提取出的锯齿状现象,引入快速引导滤波来平滑道路标线的边缘信息;最后采用最大熵阈值分割和形态学比值滤波对道路标线进行精化处理。实验表明,该方法能够有效地提取出道路标线点云,提取的平均召回率为80.98%,平均准确率为96.89%,平均综合评定指标为88.19%,能够利用道路标线点云强度信息较为完整地提取出道路标线点云。

结合FastICA与EKF的腹部源胎儿心电信号提取

针对腹部源信号,本文提出一种结合快速独立分量分析(FastICA)与扩展卡尔曼滤波(EKF)的胎儿心电信号提取方法。首先抑制原始母体腹壁混合信号中的基线漂移、工频干扰和脉冲伪迹。然后使用FastICA从母体腹壁混合信号中分离得到母体心电信号估计以及含有残留母体心电成分和其他噪声的胎儿心电信号估计。使用EKF对胎儿心电信号估计进行滤波得到残留的母体心电成分估计,将其抑制后获得含噪声的胎儿心电信号。最后再次使用EKF提取得到清晰的胎儿心电信号。采用临床数据进行实验,本文提出的胎儿心电信号提取方法的灵敏度、阳性预测值和F_(1)分数分别为99.27%,94.35%,96.71%,其基于互相关系数和基于特征值分析的信噪比分别为6.1454 dB和6.5096 dB。实验结果表明本文提出的方法在主观视觉效果和客观评价指标上均优于传统的胎儿心电信号提取方法。

基于事件触发的航空器目标跟踪算法

设计了一种基于事件触发的航空器目标跟踪算法,对航空器运动状态进行估计。首先,为了解决带宽受限问题,传感器的测量信息仅在满足随机事件触发条件时发送给局部估计器。其次,考虑事件触发机制对于状态更新和估计误差协方差更新的影响,局部估计器采用改进的无迹卡尔曼滤波(UKF)算法得到局部状态估计。此外,利用快速协方差交叉方法融合局部估计器的状态估计,得到目标的融合状态估计。最后,对一个航空器目标跟踪实例进行仿真实验,该算法在取得与UKF算法接近的估计效果时通信次数降低了17%。

基于双目视觉的水下动态目标识别与定位方法研究

针对动态水下目标跟踪定位过程需要良好的实时性与鲁棒性问题,提出一种基于双目视觉的水下动态目标定位方法;利用快速引导滤波提取水下图像的光照分量,构造了一种改进的二维伽马函数,并对其参数利用光照分量的分布特性进行调整,实现了对水下不同环境图像下的亮度自适应校正处理;利用卡尔曼滤波预估下一时刻目标的位置,将预测空间作为ROI区域进行图像校正,极大降低了算法的运行时间;在HSV空间对目标进行掩膜提取,识别之后通过双目定位算法对目标进行准确定位;经过水箱试验验证,与多尺度高斯函数、双边滤波等算法相比,该方法在运行速度上有着显著的提高,达到了35FPS,在定位过程中有着较高定位精度,在方向的平均相对误差为(3.59%,3.35%,1.42%);结果表明,该算法可以满足水下动态目标跟踪定位的实时性与鲁棒性要求。

基于VMDFK和图像编码技术CNN网络刀具磨损状态识别

针对采集刀具加工数据过程中有冗余信息和干扰信号,导致刀具磨损状态特征识别困难、识别精度不高等问题,提出一种基于快速谱峭度图的变分模态分解模态分量选取(VMDFK)与格拉姆角场(GAF)图像编码技术相结合的卷积神经网络(CNN)刀具磨损状态识别方法。首先通过变分模态分解和快速谱峭度图,筛选符合要求的模态分量并重构;再采用形态滤波对重构信号去噪和增强;最后通过格拉姆角场图像编码技术,将经去噪增强后的信号转换为格拉姆角场图,并将其输入卷积神经网络中提取特征,较好地解决了信号中的干扰和图像识别中图像特征不明显问题。实验结果表明:该方法可准确清晰地展现刀具磨损状态的特征,在即时性、准确度等方面有较大提高,实现对刀具不同磨损状态的实时智能识别,具有较好的效果。

基于改进FAST算法的移动机器人导航

针对移动机器人在单目视觉导航方面实时性与鲁棒性较差的问题,提出一种基于FAST算法和一点RANSAC算法的移动机器人导航新方法,该方法主要由3个步骤组成:首先,机器人通过自身摄像头采用改进FAST角点提取算法采集周围的环境信息;其次,在匹配过程中采用结合扩展卡尔曼滤波的一点RANSAC算法进行误匹配剔除;最后,用得到的匹配点进行三维环境重建和地图创建。实验结果表明,改进方法能有效提高移动机器人定位与导航的实时性与鲁棒性。

基于ZYNQ的信号分析与处理电路系统设计

提出一种基于Z Y NQ硬件平台的信息分析与处理方法,实现在硬件平台上对未知信号的精确分析与处理.设计基于FFT算法对信号进行频谱分析,提取其主要频率分量,依据频谱分析结果设计对应的FIR数字滤波器,达到精确滤波的目的.仿真实验结果表明,本设计可有效过滤干扰波,完全达到了设计要求.

基于16相快速滤波实现采样率4~8 GS/s中频信号预处理

在机载雷达或电子战接收系统对大带宽数字中频信号的预处理过程中,针对传统并行多相滤波方式存在FPGA乘法器资源消耗过多的缺陷,提出对并行多相分解系数进行快速滤波算法构建,实现高速ADC采样率在4~8 GS/s之间的数字下变频处理。即先将高速中频采样信号解析为并行32支路,再通过数字混频及2倍抽取将基带复信号的并行度降至16,最后基于短卷积算法构建的16相快速滤波架构,实现对高采样率、大带宽信号的数字下变频预处理。通过基于并行16相快速滤波算法的宽带数字下变频设计与应用,将FPGA乘法器资源降至传统并行多相滤波方式的32%左右,大幅节省资源并提升单片FPGA对多通道、高采样率中频信号的预处理能力。

一种基于SR-UKF的FastSLAM算法

标准FastSLAM算法存在着粒子集退化和线性化误差累积的缺陷。针对上述问题,提出了基于平方根无迹卡尔曼滤波(SR-UKF)的FastSLAM算法。SR-UKF选取一组能够代表状态向量统计特性的代表点带入非线性函数处理后重新构建出新的统计特性;使用SR-UFK取代EKF来估计每个粒子的后验位姿提议分布,可以提高粒子采样精度,减缓粒子集的退化;同时SR-UKF可以确保协方差矩阵的非负定,保证了SLAM算法的稳定性。仿真实验结果表明,基于SR-UKF的FastSLAM算法在估计精度和鲁棒性两方面均优于FastSLAM 2.0算法。

基于滤波算法的隧道全时域激发极化超前探测正演方法

为进一步提高隧道全时域激发极化多参数超前探测的效果,采用滤波算法进行计算时间域等效电阻率,采用有限单元法进行计算,提出一种基于滤波算法的隧道全时域激发极化超前探测正演方法。该方法针对时间域Cole-Cole表达式计算收敛性差的问题,引入滤波算法进行时间域激发极化等效电阻率的计算,提高算法的收敛性;针对正演过程中大型矩阵求解影响计算效率的问题,引入OpenMP并行算法,实现隧道全时域激发极化超前探测正演的加速运算。最后,开展数值算例和物理模型试验。研究结果表明:该正演方法对异常体有敏感响应,且在加快正演速度具有显著优势。通过基于滤波算法的隧道全时域激发极化超前探测获得的观测数据与实际探测数据较为相符,这为基于全时域激发极化隧道超前探测提供了一种可行的正演算法。

选煤厂高效低耗快开压滤机的性能研究

选煤厂所应用的传统快开压滤机存在着低效和浮选精煤水分高的问题,需要积极进行高效低耗快开压滤机的应用性能研究,在确保煤泥脱水整体性能得到提高下,使选煤厂具有更高的竞争力.如何有效从入料组成、隔膜压榨时间和泵给料时间等因素进行高效低耗快开压滤机性能的研究,也成为选煤厂设备优化中需要考虑的问题.基于此,对快开压滤机运行情况进行概述,并对其性能进行研究,讨论了降水效果,以期可以提高选煤的质量及效率.

基于容积粒子滤波的无人机FastSLAM算法研究

FastSLAM算法中粒子滤波存在粒子退化问题,严重影响了滤波的精度。为解决此问题,提出了基于容积粒子滤波的FastSLAM算法。将容积卡尔曼滤波作为粒子滤波的重要性密度函数,获得所需要的加权粒子,进而通过计算粒子均值,获得系统状态的最小均方误差估计,能够很好地逼近系统状态后验概率,抑制粒子退化问题。在无人机应用环境下对该算法进行仿真验证,结果表明该算法在SLAM的估计精度和一致性方面都显示出较好的性能。