抗野值自适应Kalman滤波在MEMS加速度计信号处理中的应用

针对MEMS加速度计在复杂应用环境下输出信号中存在野值,且其噪声统计先验知识不足的问题,运用了一种抗野值自适应Kalman滤波算法,采提高其测量精度.该算法用一个修正函数加权于自适应Kalman滤波方程的新息上,根据新息的方差和均值变化自适应调整修正权值,从而保持序列原有性质.通过对输出信号的算法验证,表明该算法能够有效在线去除野值,防止滤波发散,在提高MEMS加速度计测量精度上有一定的可行性.     

锥扫型光学传感器像平面多目标轨迹跟踪

积分概率多假设跟踪(IPMHT)是一种基于期望极大化(EM)的准最优贝叶斯多目标迭代跟踪算法,研究了该算法在锥扫型光学传感器像平面多目标轨迹跟踪中的问题。为提高算法的跟踪性能和计算效率,利用逻辑概率数据关联滤波(PDAF)方法进行目标初始状态估计,并利用目标幅度信息和波门技术对IPMHT进行优化。针对锥扫型传感器非线性观测下的多目标跟踪,将扩展无味卡尔曼滤波(AUKF)与优化的IPMHT算法相结合,实现像平面多目标轨迹的起始、维持和终结。蒙特卡洛仿真实验表明,该算法成功地解决了锥扫型传感器的像平面多目标轨迹跟踪问题,在提高目标跟踪性能的同时改善了计算效率。     

基于复杂场景下特征融合与估计的目标跟踪算法

针对图像跟踪过程中的背景干扰、光照条件变化与目标特性漂移等问题,提出一种粒子滤波框架下灰度和纹理特征融合与估计的目标跟踪算法.首先建立基于灰度直方图和边缘梯度直方图的特征集合,以增加目标描述的信息量和稳定性;然后在跟踪过程中依据每个特征对目标与背景的区分度赋予其相应的权值,突出贡献较大的特征,并将该加权系数用于目标和候选目标的相似性度量;最后,为了克服复杂场景对单幅图像中特征可信度的影响,将跟踪的连续性引入权值的自适应调整过程,利用当前帧的特征区分度和前一帧的特征权值对当前帧的特征权值进行估计,从而实现特征权值的在线更新.实验结果表明,通过对特征的融合及其权值的在线估计,实现了复杂场景下的稳定目标跟踪.     

基于光电跟踪系统的联合Kalman滤波器算法研究

传统光电跟踪系统中,采用单传感器方式进行目标跟踪存在跟踪不稳定及传感器切换时跟踪精度大幅度下降等问题.利用融合光电跟踪系统多个传感器信息可以提高系统性能的特点,研究了采用联合Kalman滤波器的融合算法.首先对各传感器获取的目标轨迹数据进行局部滤波,然后由全局滤波器对各局部滤波器的目标轨迹估计进行融合处理,得到目标状态估计的更新.实验仿真结果表明,该融合算法实现了目标的稳定跟踪,其跟踪精度高于传统单传感器跟踪模式,并且有效地解决了传感器失效造成的跟踪精度严重下降问题.     

粒子滤波算法在多传感器测量中的应用

目标跟踪是粒子滤波算法在处理非线性问题的一种典型应用,但由于在线处理能力或传输条件的限制,实际应用中往往无法对多个传感器数据同时处理。据此,给出了一种基于多传感器选优的粒子滤波算法。假设每个时刻可以处理一个测量数据,该算法先采用加权的概率密度函数来评价每个传感器获得的测量值,并用粒子滤波对概率密度函数的加权进行实时更新,基于最大熵标准来选取最优测量数据进行处理。同时,最大熵标准保证了最优似然函数分布最宽,从而缓解粒子衰竭问题。通过数值仿真实验证明,该算法可以选择最优观测数据进行处理,有效降低多传感器测量中粒子滤波在线实时处理性能的要求,也较好地缓解了粒子滤波的"衰竭"问题。     

多特征融合的优化粒子滤波红外目标跟踪

针对粒子滤波重采样中粒子贫化问题,采用了权值选择的优化方法,对每个粒子的权值进行排序,选取其中权值较大的粒子参与跟踪估计,使权值较小的粒子有机会参与下一状态的估计,保证参与状态估计的大部分粒子具有多样性,有效克服粒子贫化现象。为了进一步提高跟踪性能,根据红外目标成像特点,融合目标梯度特征和灰度特征建立观测模型,并根据置信度实时调整每个特征对跟踪结果的影响,且自适应更新模板。经仿真验证,红外目标在复杂背景或遇到遮挡情况下,该算法能够精确鲁棒地跟踪目标。     

光电探测多传感器数据融合方法研究

首先分析了光电探测传感器的观测特点.简要介绍了各传感器在进行数据融合时必做的重要工作--数据对准及几种数据对准方法.重点研究了具有不同传感器测量精度的光电探测系统信息加权融合概念及融合准则.给出了每种准则下的目标跟踪算法,并进行了算法仿真.仿真结果表明,对于具有不同测量精度的传感器,选择不同的融合准则,可更为准确地预测目标飞行轨迹,大大提高目标的跟踪效果和精度.     

基于IMM-UKF的主/被动雷达融合算法

在主/被动雷达双传感器目标跟踪背景下,提出一种基于IMM-UKF滤波的加权数据融合算法。该算法采用UKF对观测数据进行滤波估计,避免了计算雅克比矩阵,克服EKF滤波方法受滤波初值影响大、易发散的缺点。与单一传感器进行仿真比较,结果表明该算法可以比任一单传感器滤波精度高,稳定性更好。     

基于角度划分的2D传感器三维跟踪定位算法

根据2D传感器的扫描特点和观测信息,建立了单站2D传感器对三维运动目标的观测模型,基于该模型提出了三维匀速运动目标的仰角参数化扩展卡尔曼滤波算法。此算法将传感器的仰角观测范围划分为若干个子区间,对各仰角子区间执行并行独立的EKF跟踪算法,并根据滤波结果动态更新各滤波器的权值,最后融合各EKF估计值得到最终的目标状态估计。计算机仿真结果证明了该算法在2D传感器对三维空间内目标跟踪定位时的有效性和优越性。     

分布式传感器多目标跟踪改进算法

利用分布式传感器网络进行目标跟踪,能够有效增加传感器的覆盖范围,提高对运动目标的检测和跟踪能力,但如何充分利用相邻传感器之间的信息进行有效的融合,仍然是一个难点问题。本文在多伯努利滤波框架下,提出了一种改进的分布式融合跟踪算法用于目标数未知且变化的多目标跟踪。提出算法包含三种精度提升策略,即特征级融合反馈、决策级融合输出及交互反馈;其中,决策级融合输出策略可以提取更加准确的估计状态,特征级融合反馈策略可以降低错误融合结果对后续滤波过程的不良影响,交互反馈策略可以避免单传感器因漏检而导致的滤波失败。实验结果表明,提出算法的跟踪精度明显要优于传统的基于GCI分布式融合算法以及粒子多伯努利跟踪算法,具有较好的跟踪性能。      

一种改进的多传感器数据融合方法

最优加权法在数据融合时需要确知噪声方差信息,其权值固定不变,而联合卡尔曼滤波法需要确知噪声协方差信息。在噪声先验信息与实际数值差别较大或传感器系统噪声相关时,这两种方法的参数估计精度将急剧下降。针对以上不足,文中提出了一种新的动态最优加权和最小二乘滤波相结合的数据融合方法,该方法能随传感器信号起伏的变化动态调整权值,不需要噪声的统计信息,在系统噪声相关时仍能获得较高的测量精度,具有较强的稳健性。仿真结果验证了新方法的有效性。     

The research of gradation fusion algorithm based on multisensor asynchronous sampling system

This letter explores the distributed multisensor dynamic system, which has uniform sampling velocity and asynchronous sampling data for different sensors, and puts forward a new gradation fusion algorithm of multisensor dynamic system. As the total forecasted increment value between the two adjacent moments is the forecasted estimate value of the corresponding state increment in the fusion center, the new algorithm models the state and the forecasted estimate value of every moment. Kalman filter and all measurements arriving sequentially in the fusion period are employed to update the evaluation of target state step by step, on the condition that the system has obtained the target state evaluation that is based on the overall information in the previous fusion period. Accordingly, in the present period, the fusion evaluation of the target state at each sampling point on the basis of the overall information can be obtained. This letter elaborates the form of this new algorithm. Computer simulation demonstrates that this new algorithm owns greater precision in estimating target state than the present asynchronous fusion algorithm calibrated in time does.     

Adaptive time-delay estimation in nonstationary signal and/or noisepower environments

A model for an adaptive time-delay estimator is proposed to improve its performance in estimating the difference in arrival time of a bandlimited random signal received by two spatially separated sensors in an environment where the signal and noise power are time varying. The system comprises two adaptive units: a filter to compensate time shift between the two receiver channels and a gain control to provide Wiener filtering. Both the filter coefficients and the variable gain are adjusted simultaneously by using modifications from the stochastic mean-square-error gradient in the traditional adaptive least-mean-square time-delay estimation (LMSTDE) method. The convergence characteristics of the proposed system are analyzed in detail and compared with those obtained by the traditional technique. Theoretical results show that, unlike the LMSTDE configuration, this arrangement can decouple the adaptation of time shift from the changing signal and/or noise power, which in turn gives rise to better convergence behavior of the delay estimate. Simulation results are included to illustrate the effectiveness of the new model and corroborate the theoretical developments     

旋转均值滤波用于提高红外点目标图像信噪比

当距离红外传感器很远时,目标在红外图像中表现为点目标。充分利用点目标,背景杂波及噪声在空间域中的表现形式,并结合均值滤波滤除噪声的特性,提出了一种新的滤波方法——旋转均值滤波法。将此算法运用在信噪比不大于3的红外点目标图像中,实现了背景抑制,突出目标以及提高信噪比作用,并能够满足实时处理的要求。     

Noise emissions sources direction-finding in the process of their background air threats detection in radars with phased antenna array

We analyze two options of combined systems for spatial signal processing in radars with phased antenna array, where the detection of noiseless point targets with background noise emissions is accompanied by the direction finding of their sources. In the first option the direction finding of noise emissions is based on the shape analysis of the adaptive radiation pattern of the phased antenna array, formed during the adaptive target finding with background noise emissions. In the second option the bearing angles are determined on the basis of maxima of “spectral functions” of different evaluations of correlation matrix, formed on the basis of input readings. An example how to build such a combined system for spatial signal processing based on general adaptive grid filter is presented. It is shown that the effect of simultaneous target finding in external noise background and direction finding of their sources is achieved by single utilization of the most complex tuning operation of the adaptive grid filter. This operation is same for both of these tasks, and it is easier comparing to solving these two problems separately.     

A real-time adaptive line enhancer hardware implementation designed for a plasma estimation application†

A real-time adaptive line enhancer hardware system has been implemented for noise cancellation in a nuclear fusion instrumentation project. The noise cancellation circuitry, realized with a standard digital signal processor (DSP) chip, uses a direct form adaptive filter and adapts using the least-mean-squares (LMS) algorithm. The noise cancellation circuitry adapts the noise filter weights to the line noise prior to an experiment. Upon initiation of the experiment, the adaption is stopped for the duration of the experiment. Experimental data with noise cancelling may be recorded during the experiment. After the experiment, noise filter weights may be read out to be included with the experimental record for possible later evaluation. The use of these hardware systems could significantly reduce the presently enormous requirements for off-line signal processing to extract plasma information. In this paper, real-time capabilities of the line enhancer circuitry for noise cancellation are demonstrated for simple examples and for the nonstationary fusion experiment data. Performance trade-offs in terms of sampling rate and filter complexity are given.     

An adaptive Kalman filter for ECG signal enhancement

The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement of the SNR and loss of clinically relevant physiological signal dynamics. Using a bayesian framework, in this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive estimation of the process and measurement noise covariances is performed by maximizing the bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to fluctuate.     

多传感器数据融合技术研究

本文采用证据理论进行三传感器的数据融合,开发了基于证据理论的三传感器数据融合算法,进行了软件仿真,研制出用于数据融合的高速红外图像采集和点目标处理系统,解决了红外点目标检测以及红外焦平面输出图像的高速传输、显示等难题.系统采用PCI总线作为数据传输的接口,大大提高了系统数据传输速率;采用带禁带的Robinson空间滤波器,完成在低信噪比条件下红外点目标的检测及其背景的抑制;并采用高速DSP器件对滤波算法进行实现,提高了目标的信噪比,保证了系统实时性的要求,具有功耗低、体积小、可靠性高等优点.     

位置敏感探测器的信号噪声自适应抑制方法

针对位置敏感探测器噪声来源及特点,提出了一种基于自适应FIR维纳滤波器的自适应抑制方法。该方法以维纳滤波器为数字滤波器,自适应地调整传输特性,提高PSD的信噪比,从而大幅提高了PSD应用系统的测量精度。     

采用模糊推理自适应加权融合的双色红外成像目标跟踪

针对双色红外成像制导系统中多传感器目标跟踪的实际问题,提出了一种基于模糊推理自适应加权融合的目标跟踪算法。该算法首先采用BP神经网络与模糊推理相结合的方法对各传感器的工作性能进行判决;然后根据各传感器的性能测度对多传感器测量数据进行自适应加权融合,得到目标状态的多传感器重建测量;最后采用卡尔曼滤波器对多传感器重建测量进行滤波得到目标状态的最终估计。实验结果证明了该算法的有效性和稳健性。