Amended Kalman Filter for Maneuvering Target Tracking

The conventional Kalman filter (KF) which uses the current measurement to estimate the current state is a posterior estimation.KF is identified as the optimal estimation in linear models with Gaussian noise.However,the performance of KF with incomplete information may be degraded or diverged.In order to improve the performance of KF,an Amended KF (AKF) is proposed by using more posterior measurements.The principle,derivation and recursive process of AKF are presented.The differences among Kalman smoother,adaptive fading method and AKF are analyzed.The simulation results of target tracking with different covariance of motion model indicate the high precision and robustness of AKF.     

Influence of stochastic noise statistics on Kalman filter performance based on video target tracking

The system stochastic noises involved in Kalman filtering are preconditioned on being ideally white and Gaussian distributed. In this research, efforts are exerted on exploring the influence of the noise statistics on Kalman filtering from the perspective of video target tracking quality. The correlation of tracking precision to both the process and measurement noise covariance is investigated; the signal-to-noise power density ratio is defined; the contribution of predicted states and measured outputs to Kalman filter behavior is discussed; the tracking precision relative sensitivity is derived and applied in this study case. The findings are expected to pave the way for future study on how the actual noise statistics deviating from the assumed ones impacts on the Kalman filter optimality and degradation in the application of video tracking.     

基于扩展集员滤波与信息几何的机动目标跟踪

面对当前日益复杂的雷达跟踪目标和环境,广泛应用的卡尔曼滤波及其扩展类算法虽然能以较少的计算量得到较好的跟踪效果,但其要求过程噪声与量测噪声符合零均值高斯分布,否则会导致滤波发散。为解决系统状态及噪声未知但有界情况下的机动目标跟踪问题,介绍了一种扩展集员滤波机动目标跟踪算法,并辅以基于信息几何的跟踪性能监测处理。通过仿真分析验证了基于集员滤波与信息几何的机动目标跟踪边界约束性能及跟踪准确度较好,且能通过统计流形距离的变化来探测目标是否发生机动。     

基于DSP利用粒子滤波算法实现目标跟踪

以TMS320DM643数字信号处理器(DSP)为核心,构建了高性能的视频处理平台,并利用该硬件平台实现粒子滤波(PF)算法完成对目标的跟踪。介绍了视频处理平台的基本结构和核心器件,并阐述了DSP实现运动目标跟踪的软件设计。实验表明,该系统能够实时跟踪目标,且相对于传统的图像采集卡与计算机结合的处理系统,具有体积小、低功耗、模块化和移动性好的特点,容易集成到各种移动设备中实现自主导航、安防监控等功能。     

新的自适应盲噪声抑制在语音处理中的应用

传统的自适应滤波去噪的系统中需要一个参考信号,实际中一般事先并不知道噪声的特性,而且噪声也是时变的。因此提出一种新的基于盲噪声抑制方法,可降噪并跟踪噪声特性的变化,随时调节滤波器的系数,比传统的自适应噪声控制具有优势。     

基于广义卡尔曼滤波的光学膜厚监控信号处理

为了解决光学膜厚监控系统中经锁相放大输出的监控信号精度较低、极值点附近变化不灵敏等问题,采用卡尔曼滤波(KF)对光学膜厚监控信号进行处理,建立了针对非线性光学监控系统的扩展KF模型,模型输出包括监控信号和导数信号。仿真分析首先利用TFCalc软件生成4层增透膜理想监控曲线,加入高斯噪声后,信噪比为15dB,通过卡尔曼滤波处理后监控信号信噪比改善达16dB且实时动态跟踪特性良好,延时小于1s;利用平均值滤波处理KF输出导数信号,提前预判读获取导数过零点对应膜厚。同时通过对比TFCalc理想监控曲线极值点坐标和预判导数信号过零点坐标,得到理论膜厚误差小于4nm。此项研究提高了膜厚检测的准确性。     

PR-based SAR reconstruction algorithm for phase noise mitigation based on the hidden convexity

The performance of synthetic aperture radar (SAR) reconstruction is significantly deteriorated by the random phase noises arising from the atmospheric turbulence or frequency jitter of the transmit signal. Recently, the emerging phase retrieval (PR) technique is gradually extended to the SAR reconstruction problem via the phase-corrupted data attributing to its alluring potential for phase noise mitigation. In this paper, a novel PR-based SAR reconstruction algorithm for phase noise mitigation is proposed by jointing alternating direction method of multipliers (ADMM) and Kolmogorov spectral factorization (KoSF). Owing to the exploiting of the hidden convexity of PR-based SAR reconstruction problem and the structure advantage of the quadratic magnitude measurement, the proposed algorithm acquires better robustness for the complex-valued Gaussian white noises and the random phase noises than the existing PR-based SAR reconstruction algorithms. In the experiments, the synthetic scene data and the moving and stationary target recognition Sandia laboratories implementation of cylinders (MSTAR SLICY) target data are provided to verify the validity of the proposed algorithm.     

Human tracking from a mobile agent: Optical flow and Kalman filter arbitration

Tracking moving objects is one of the most important but problematic features of motion analysis and understanding. The Kalman filter (KF) has commonly been used for estimation and prediction of the target position in succeeding frames. In this paper, we propose a novel and efficient method of tracking, which performs well even when the target takes a sudden turn during its motion. The proposed method arbitrates between KF and Optical flow (OF) to improve the tracking performance. Our system utilizes a laser to measure the distance to the nearest obstacle and an infrared camera to find the target. The relative data is then fused with the Arbitrate OFKF filter to perform real-time tracking. Experimental results show our suggested approach is very effective and reliable for estimating and tracking moving objects.     

雷达动态多目标模拟系统的设计

雷达动态多目标模拟系统旨在解决如何检测相控阵测量雷达的多目标跟踪性能这一技术难题.系统采用程控延时技术、数字控制技术及中断处理技术,可模拟10个以上运动目标,目标距离不受雷达重复频率的限制;采用数字存贮技术对地物杂波信号进行模拟,杂波分布可随场地和天线波束指向变化;对多台雷达同时工作时的同频干扰信号进行了模拟.系统可选择高频和视频两种接口将目标信号输入雷达,以适应各种检测要求.该系统可以有效检测相控阵雷达多目标跟踪性能,同时为雷达操作人员提供了实用的模拟训练器材.     

基于核密度多伯努利视频多目标跟踪算法

针对复杂环境下数目变化、目标紧邻及尺寸变化的 视频多目标跟踪问题,在多伯努利滤波框架 下,提出一种自适应的变数目视频多目标跟踪算法。算法通过引入核密度背景减除技术,可 以有效抑制 背景干扰;然后融入连续自适应均值漂移(CAMShift)技术,并提出目标紧邻和尺寸变化处理 机制,可 以有效提高算法的自适应性;最后引入粒子标记技术,可以有效实现对视频多目标的轨迹跟 踪。对彩色视频和红外视频序列图像的测试结果表明,本文提出算法可以有效实现对复杂环 境下数目变化的视频多目标自适应跟踪,且具有较好的鲁棒性。     

基于粒子滤波的空-地目标跟踪算法

针对空-地目标跟踪中目标大幅度变速运动而引 起的跟踪失败问题,基于Kristan等人提出的双步(TS)动态模型框架,对空-地目标跟 踪中目标运动特点进行分析与建模,改进TS模型中 的保守模型以适应加速运动,提出适于描述大幅度变速运动的加速度双步(TSA)动态模型作 为粒子滤波(PF)跟踪算法的动态模 型,实现对粒子状态的精确预测,进而达到使用较少粒子即可对目标鲁棒跟踪的目的。对空 -地目标跟踪的测试视频进行测 试,结果表明,本文算法可对大幅度变速运动目标稳定跟踪,正确跟踪率为92%,对目标 尺寸约为25pixel×30pixel时的处理帧率为29frame/s。本文算法具有较好的鲁棒性与实时性。     

光电位置传感器中形态学滤波方法的研究

为了有效滤除目标跟踪过程中光电位置传感器(PSD)中存在的噪声干扰,提高PSD空间探测精度和稳定性,研究了一种基于开-闭和闭-开级联组合的形态学滤波方法,通过选取适当的结构元素对一维和二维位置信号进行分析和降噪处理,获得稳定的位置信息,供后续位置跟踪设备使用。经实验数据分析,该方法具有快速滤除随机噪声、脉冲噪声以及混合噪声干扰的能力,处理速度快、实现过程简单,便于现场实时信号处理应用。     

自调整分层卡尔曼粒子滤波的快速目标跟踪

分层卡尔曼粒子滤波成功应用于目标跟踪,但其只对目标位置进行了优化,忽略了其他仿射参数,导致跟踪中的粒子数目仍然很大。为了实现复杂环境下的快速目标跟踪,提出一种带有自调整策略的分层卡尔曼粒子滤波方法。该方法将目标划分为线性和非线性状态空间,并通过少量粒子的迭代过程在非线性状态空间逐步搜索最优状态。其详细过程如下:首先,利用卡尔曼滤波预测目标位置,结合目标运动信息计算潜在目标区域;然后在该区域内生成一组随机粒子,通过在线姿态估计对粒子状态进行调整,并将观测结果与目标模板进行比较,修正粒子摄动的方向以逼近目标。把该方法应用于大机动目标的视频序列中,并与现有的跟踪方法进行了对比。结果表明,所提方法能够以少量粒子实现准确、稳定的目标跟踪,大大降低了跟踪算法的运算量,提高了跟踪效果。     

昆虫目标雷达散射截面积特征辅助跟踪算法

害虫迁飞具有规模大、突发性强的特点,会导致病虫害异地大爆发,粮食产量下降,造成重大的经济损失。昆虫雷达是监测迁飞性害虫的一种有效手段。昆虫目标的雷达散射截面积(RCS)较小,回波能量弱,在保证高检测率的同时会带来高虚警率问题,进而导致在目标跟踪的数据关联环节,易受虚假量测的影响出现关联错误。幅度特征辅助跟踪算法利用目标与噪声点迹的幅度差异,可以有效提高目标与噪声的识别度,改善跟踪性能,但是其需要已知目标的RCS起伏模型作为先验信息来计算幅度似然比。因此,该文基于Ku波段高分辨昆虫雷达外场实测昆虫回波数据,分析了昆虫目标的RCS起伏特性,得出Gamma分布可以较好地拟合昆虫目标的RCS统计分布,并将其作为先验信息,推导出Gamma起伏目标在高斯白噪声背景下的幅度似然比。通过在不同信噪比、不同量测噪声及不同起伏模型参数下的仿真结果及性能指标分析,验证了相比于概率数据互联滤波算法(PDAF)算法,目标RCS特征辅助的跟踪算法可以有效提高昆虫目标的跟踪精度。      

Adaptive Threshold Median Filter for Multiple-Impulse Noise

Attenuating the noises plays an essential role in the image processing. Almost all the traditional median filters concern the removal of impulse noise having a single layer, whose noise gray level value is constant. In this paper, a new adaptive median filter is proposed to handle those images corrupted not only by single layer noise. The adaptive threshold median filter （ATMF） has been developed by combining the adaptive median filter （AMF） and two dynamic thresholds. Because of the dynamic threshold being used, the ATMF is able to balance the removal of the multiple-impulse noise and the quality of image. Comparison of the proposed method with traditional median filters is provided. Some visual examples are given to demonstrate the performance of the proposed filter.     

天基非合作卫星高精度跟踪算法研究

空间非合作目标跟踪技术可以在多方面发挥重要作用,目前效果较好的图像跟踪算法多是基于视频流处理,但是由于面对的工况与航天应用面对工况不同,在跟踪精度、运算速度、预警率和虚警率等要求上不满足空间目标跟踪需求与任务要求,并且运算复杂难以在航天器中实现,不适合天基卫星跟踪。为解决这一问题,一种面向空间应用的卫星目标高精度跟踪算法被提出,该算法以图像相关、曲线拟合、卡尔曼滤波、SURF算法为基础,并将预测、跟踪和矫正过程相融合,最终获得在天基平台中具有可行性的高速稳定跟踪算法。相关实验表明,这种算法可以对平面内自由旋转、0.4~2.1倍尺度内缩放、有光照变化的图像进行连续跟踪,仿真试验平均跟踪误差小于0.9像素且大多数工况下计算速度高于200帧/s,并且算法对图像模糊、高斯噪声以及椒盐噪声都有较好兼容能力,对于实际模型目标跟踪仍有稳定跟踪能力。     

Multi-target visual tracking and occlusion detection by combining Bhattacharyya Coefficient and Kalman filter innovation

This paper introduces an approach for visual tracking of multi-target with occlusion occurrence. Based on the author’s previous work in which the Overlap Coefficient (OC) is used to detect the occlusion, in this paper a method of combining Bhattacharyya Coefficient (BC) and Kalman filter innovation term is proposed as the criteria for jointly detecting the occlusion occurrence. Fragmentation of target is introduced in order to closely monitor the occlusion development. In the course of occlusion, the Kalman predictor is applied to determine the location of the occluded target, and the criterion for checking the re-appearance of the occluded target is also presented. The proposed approach is put to test on a standard video sequence, suggesting the satisfactory performance in multi-target tracking.     

正则化粒子滤波在水下目标跟踪中的应用

针对传统卡尔曼滤波(KF)及扩展卡尔曼滤波(EKF)在非线性目标跟踪模型中,跟踪精度较差的问题,本文给出了一种基于正则化粒子滤波(RPF)的水下目标跟踪算法。文中在一种模拟水下目标跟踪环境的非线性动态模型中对所提出的算法进行了仿真试验,并将其跟踪性能与扩展卡尔曼滤波和标准粒子滤波算法(PF)进行了比较。仿真结果表明,PF算法比EKF算法滤波精度更高,RPF的跟踪性能优于PF和RPF,而且随着粒子数的增加,PF和RPF的跟踪性能也不断提高。     

M滤波的自适应背景抑制算法

提出了一种M滤波的自适应背景抑制算法.该算法将目标和观测噪声作为图像背景的混合干扰,依据M估计原理自适应的估计真实背景.算法具有较好的稳健性能,能够自适应的低抗高强度的干扰.仿真和实验表明,与均值滤波(MF)、中值滤波(ModF)以及Wiener滤波(WF)相比,该算法能够更有效地从混合噪声环境下估计背景,增强目标信噪比(SNR).     

基于最小二乘的多特征概率数据关联EM方法

提出了一种多目标多特征信息的数据关联算法。在强噪声密集杂波环境下,针对传统PDA算法对多目标跟踪时出现精度较差的问题,在跟踪过程中融入目标的特征状态信息,利用期望极大化（EM）算法对目标状态估计的最小二乘（LS）误差函数迭代求最小,将目标运动状态和特征联合的关联概率作为估计参数不断修正,从而获得对目标状态的最优估计。仿真结果表明,该算法能够增强区分目标和杂波的能力,减小相近特征量测所引起的跟踪误导,弱化对检测概率的依赖性,显著并稳定地提高对目标的跟踪精度。