Optimality analysis of one-step OOSM filtering algorithms in target tracking

In centralized multisensor tracking systems, there are out-of-sequence measurements (OOSMs) frequently arising due to different time delays in communication links and varying pre-processing times at the sensor. Such OOSM arrival can induce the “negative-time measurement update” problem, which is quite common in real multisensor tracking systems. The A1 optimal update algorithm with OOSM is presented by Bar-Shalom for one-step case. However, this paper proves that the optimality of A1 algorithm is lost in direct discrete-time model (DDM) of the process noise, it holds true only in discretized continuous-time model (DCM). One better OOSM filtering algorithm for DDM case is presented. Also, another new optimal OOSM filtering algorithm, which is independent of the discrete time model of the process noise, is presented here. The performance of the two new algorithms is compared with that of A1 algorithm by Monte Carlo simulations. The effectiveness and correctness of the two proposed algorithms are validated by analysis and simulation results.     

Joint state and parameter estimation in particle filtering and stochastic optimization

In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown static parameters based on combination of particle filtering and Simultaneous Perturbation Stochastic Approxi- mation （SPSA） technique. The estimations of parameters are obtained by maximum-likelihood estimation and sampling within particle filtering framework, and the SPSA is used for stochastic optimization and to approximate the gradient of the cost function. The proposed algorithm achieves combined estimation of dynamic state and static parameters of nonlinear systems. Simulation result demonstrates the feasibilitv and efficiency of the proposed algorithm     

基于极大后验估计和指数加权的自适应UKF滤波算法

针对传统Unscented卡尔曼滤波器(Unscented Kalman filter, UKF)在噪声先验统计未知时变情况下非线性滤波精度下降甚至发散的问题, 设计了一种带噪声统计估计器的自适应UKF滤波算法. 首先根据极大后验(Maximum a posterior, MAP)估计原理, 推导出一种次优无偏MAP常值噪声统计估计器; 接着在此基础之上, 采用指数加权的方法, 给出了时变噪声统计估计器的递推公式; 最后对自适应UKF算法进行了性能分析. 相比于传统UKF, 该自适应UKF算法在噪声统计未知时变情况下不仅滤波依然收敛, 滤波精度及稳定性显著提高, 而且其具有应对噪声变化的自适应能力. 仿真实例验证了其有效性.     

A combination algorithm for MEMS hydrophone signal and noise separation北大核心CSCD

In the light of the problem of MEMS hydrophone data “submerged” in strong noise field, the combination filter of LMS adaptive noise cancellation and Fourier transform filtering is proposed. The filter algorithm is applied to MEMS hydrophone signal and noise separation. When the frequency of signal is given, the combination filter algorithm is used for signal extraction and the ideal signal performance comparison. Simulation results show that the effect of extraction resolution in strong noise field of -15 dB is higher in the algorithm. The algorithm can be used to search similar to the “black box” case. The filter is used to separate signal and noise in the Fen machine test of North University of China. The results show that the algorithm is efficient and Practicability. ©, 2014, The Editorial Office of Chinese Journal of Sensors and Actuators. All right reserved.     

基于期望最大化算法的自适应噪声交互多模型滤波

A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online. For the existing IMM filtering theory, the matrix Q is determined by means of design experience, but Q is actually changed with the state of the maneuvering target. Meanwhile it is severely influenced by the environment around the target, i.e., it is a variable of time. Therefore, the experiential covariance Q can not represent the influence of state noise in the maneuvering process exactly. Firstly, it is assumed that the evolved state and the initial conditions of the system can be modeled by using Gaussian distribution, although the dynamic system is of a nonlinear measurement equation, and furthermore the EM algorithm based on IMM filtering with the Q identification online is proposed. Secondly, the truncated error analysis is performed. Finally, the Monte Carlo simulation results are given to show that the proposed algorithm outperforms the existing algorithms and the tracking precision for the maneuvering targets is improved efficiently.     

A Fusion Kalman Filter and UFIR Estimator Using the Influence Function Method

In this paper,the Kalman filter(KF)and the unbiased finite impulse response(UFIR)filter are fused in the discrete-time state-space to improve robustness against uncertainties.To avoid the problem where fusion filters may give up some advantages of UFIR filters by fusing based on noise statistics,we attempt to find a way to fuse without using noise statistics.The fusion filtering algorithm is derived using the influence function that provides a quantified measure for disturbances on the resulting filtering outputs and is termed as an influence finite impulse response(IFIR)filter.The main advantage of the proposed method is that the noise statistics of process noise and measurement noise are no longer required in the fusion process,showing that a critical feature of the UFIR filter is inherited.One numerical example and a practice-oriented case are given to illustrate the effectiveness of the proposed method.It is shown that the IFIR filter has adaptive performance and can automatically switch from the Kalman estimate to the UFIR estimates according to operating conditions.Moreover,the proposed method can reduce the effects of optimal horizon length on the UFIR estimate and can give the state estimates of best accuracy among all the compared methods.     

Analysis of quantization noise and state estimation with quantized measurements

The approximate correction of the additive white noise model in quantized Kalman filter is investigated under certain conditions. The probability density function of the error of quantized measurements is analyzed theoretically and experimentally. The analysis is based on the probability theory and nonparametric density estimation technique, respectively. The approximator of probability density function of quantized measurement noise is given. The numerical results of nonparametric density estimation algorithm demonstrate that the theoretical conclusion is reasonable. Based on the analysis of quantization noise, a novel algorithm for state estimation with quantized measurements also is proposed. The algorithm is based on the least-squares estimator and unscented transform. By least-squares estimator, the effective information is extracted from the quantized measurements. Also, using the information to update the estimated state can give a better estimation under the influence of quantization. The root mean square error (RMSE) of the proposed algorithm is compared with the RMSE of the existing methods for a typical tracking scenario in wireless sensor networks systems. Simulations provide a strong evidence that this tracking algorithm could indeed give us a more precise estimated result.     

A new FIR filter for state estimation and its application

This paper proposes a new FIR （finite impulse response） filter under a least squares criterion using a forgetting factor. The proposed FIR filter does not require information of the noise covariances as well as the initial state, and has some inherent properties such as time-invariance, unbiasedness and deadbeat. The proposed FIR filter is represented in a batch form and then a recursive form as an alternative form. Prom discussions about the choice of a forgetting factor and a window length, it is shown that they can be considered as useful parameters to make the estimation performance of the proposed FIR filter as good as possible. It is shown that the proposed FIR filter can outperform the existing FIR filter with incorrect noise covariances via computer simulations. Finally, as a useful application, an image sequence stabilization problem is considered. Through this application, the FIR filtering based approach is shown to be superior to the Kalman filtering based approach.     

A robust watermarking algorithm based on QR factorization and DCT using quantization index modulation technique

We propose a robust digital watermarking algorithm for copyright protection.A stable feature is obtained by utilizing QR factorization and discrete cosine transform(DCT) techniques,and a meaningful watermark image is embedded into an image by modifying the stable feature with a quantization index modulation(QIM) method.The combination of QR factorization,DCT,and QIM techniques guarantees the robustness of the algorithm.Furthermore,an embedding location selection method is exploited to select blocks with small modifications as the embedding locations.This can minimize the embedding distortion and greatly improve the imperceptibility of our scheme.Several standard images were tested and the experimental results were compared with those of other published schemes.The results demonstrate that our proposed scheme can achieve not only better imperceptibility,but also stronger robustness against common signal processing operations and lossy compressions,such as filtering,noise addition,scaling,sharpening,rotation,cropping,and JPEG/JPEG2000 compression.     

Reduced-order H_∞ filtering for navigation with carrier phase

A reduced-order H∞filtering algorithm for navigation with carrier phase is presented.By taking advantage of decoupling between filtering states in a navigation solution,this algorithm reduces computational cost and is robust in colored noise.Furthermore,the estimation precision is also improved by taking ambiguities as nuisance states when the filtering process converges.Applications to kinetic simulations under diferent noise are presented to demonstrate robustness and efciency of the algorithm.     

自适应UKF算法在目标跟踪中的应用

针对目标跟踪中系统噪声统计特性未知导致滤波发散或者滤波精度不高的问题, 提出了一种自适应无迹卡尔曼滤波(Unscented Kalman filter, UKF)算法.该算法在滤波过程中,利用改进的Sage-Husa估 计器在线估计未知系统噪声的统计特性,并对滤波发散的情况进行判断和抑制, 有效提高了滤波的数值稳定性,减小了状态估计误差. 仿真实验结果表明,与标准UKF算法相比,自适应UKF算法明显改善了目标跟踪的精度和稳定性.     

带有色量测噪声的非线性系统 Unscented 卡尔曼滤波器

传统Unscented卡尔曼滤波器(Unscented Kalman filter, UKF)要求噪声必须为高斯白噪声, 无法解 决带有色噪声的非线性系统滤波问题. 为此, 本文提出了一种带有色量测噪声的UKF滤 波新算法. 首先,基于量测信息增广和最小方差估计, 推导出一类带有色量测噪声的非 线性离散系统状态的最优滤波框架, 接着采用Unscented变换(Unscented transformation, UT)来计算最优框架中的 非线性状态后验均值和协方差, 进而得到有色量测噪声下UKF滤波递推公式. 所设 计的UKF新方法能有效地解决传统UKF在量测噪声有色情况下非线性滤波失效的问题, 数 值仿真实例验证了其可行性和有效性.     

基于极大似然准则和最大期望算法的自适应UKF 算法

针对噪声先验统计特性未知情况下的非线性系统状态估计问题,提出了基于极大似然准则和 最大期望算法的自适应无迹卡尔曼滤波(Unscented Kalman filter, UKF) 算法.利用极大似然准则构造含有噪声统计特性的对数似然函数,通 过最大期望算法将噪声估计问题转化为对数似然函数数学期望极大化问题,最终得到带次优递 推噪声统计估计器的自适应UKF算法.仿真分析表明,与传统UKF算法相比,提出的自适应UKF算法 有效克服了传统UKF算法在系统噪声统计特性未知情况下滤波精度下降的问题,并实现了系统噪 声统计特性的在线估计.     

基于通用FLAC的模糊自适应UKF算法及其应用

针对量测噪声方差统计值未知的非线性UKF(Unscented Kalman Filter)滤波问题,提出了一种基于通用FLAC(Fussy Logic Adaptive Controller)的模糊自适应UKF算法.在标准的非线性UKF算法基础上,以残差的实际方差与理论方差的比值作为FLAC的输入,使FLAC对滤波模型的依赖性减弱,强化了模糊自适应UKF方法的通用性;在对未知的量测噪声方差阵进行动态调节的过程中设置了指数调节参数,可不同程度地放大或缩小方差阵调节的幅度,使算法的调节速度和精度得到控制.将算法应用于GPS/DR(Dead-Reckoning)组合导航系统中,仿真结果表明了该算法的有效性.     

基于新息正交原理的抗差UKF及其在INS/GPS组合导航中的应用

为提高标准UKF对异常的量测噪声统计的鲁棒性,提出了一种基于新息正交原理的抗差UKF算法.该算法根据新息序列的正交性确定最优的抗差因子,而后通过对新息协方差阵引入抗差因子在线调整滤波增益,进而抑制异常量测对滤波解的影响.将提出的算法应用于INS/GPS组合导航系统进行仿真验证,并与标准UKF和现有的抗差UKF进行比较,结果表明,当量测噪声统计不准确时,提出的基于新息正交原理的抗差UKF滤波性能明显优于上述两种算法,提高了组合导航系统的定位精度.     

噪声相关条件下Unscented卡尔曼滤波器设计

针对传统Unscented卡尔曼滤波器(UKF)在噪声相关条件下非线性滤波失效的问题,研究了一类带相关噪声的非线性离散系统UKF设计方法.文中基于最小均方误差估计准则,给出了系统噪声和量测噪声相关时UKF滤波递推公式,并采用Unscented变换(UT)来计算系统状态的后验均值和协方差.所设计的噪声相关条件下UKF有效克服了传统UKF必须假设系统噪声和量测噪声为互不相关高斯白噪声的局限性,拓展了UKF的应用范围.仿真实例验证了其可行性和有效性.     

FAST-SR-UKF算法及其在组合导航系统中的应用

为解决无迹卡尔曼滤波（UKF）算法在组合导航应用中遇到的系统模型不确定、系统噪声统计特性未知以及计算误差较大等问题,提出了模糊自适应强跟踪平方根无迹卡尔曼滤波（FAST-SR-UKF）算法,该算法不仅具有传统UKF的优势,而且包含如下特点：通过模糊自适应强跟踪模块,增强了系统对模型不确定性以及噪声统计参数未知的适应能力;利用平方根滤波的思想,提高了模糊自适应强跟踪无迹卡尔曼滤波算法的数值稳定性,改善了由于计算误差导致的滤波发散问题。仿真结果表明：相对于传统的UKF算法,该算法精度更高、鲁棒性更强。     

高动态下基于AUKF的载波跟踪算法

当载体处于高动态运动状态时,GPS接收机载波跟踪信号极易受到外部环境不确定因素的影响。若采用标准的无迹卡尔曼滤波 (UKF),在先验的噪声统计特性与实际的噪声统计特性不相符时,状态估计性能将变差甚至发散。针对上述问题,提出采用主从式自适应UKF的算法(AUKF)。AUKF能自适应调整过程噪声方差,从而达到减小模型估计误差、抑制滤波发散的目的。Matlab仿真结果表明,在高动态下噪声统计特性发生变化时,基于AUKF的载波跟踪算法具有较好的稳定性。     

闪烁噪声下轨道机动目标自适应鲁棒跟踪算法

针对闪烁噪声下存在未知机动的空间目标跟踪问题,将自适应鲁棒滤波技术嵌入到无迹卡尔曼滤波,设计自适应鲁棒无迹卡尔曼滤波(ARUKF),再利用ARUKF产生粒子滤波的重要性密度函数,从而得到一种自适应鲁棒无迹粒子滤波(ARUPF)算法。将ARUPF与瞬态跟踪模型相结合,对空间机动目标进行自主跟踪。实验结果表明,该算法在跟踪精度和鲁棒性方面优于传统的跟踪算法。