基于自适应卡尔曼滤波的导航信息融合方法

针对组合导航系统在测量噪声未知的情况下,常规自适应卡尔曼滤波方法的实时性难以满足的问题,提出了一种实时的自适应卡尔曼滤波方法.该方法通过一个简单的指数函数实时调节卡尔曼滤波模型中的测量噪声协方差矩阵,将测量噪声的理论协方差矩阵与实际协方差矩阵的差值作为指数函数的输入,将函数的输出值与上次测量噪声的协方差矩阵之和送入卡尔...     

基于眨眼修正卡尔曼滤波的人眼跟踪研究

眼睛运动容易受到头部姿势变化、外界仿真干扰、实际光照条件等影响,已有眼部跟踪算法的准确率、鲁棒性较低。为此,提出一种基于眨眼修正卡尔曼滤波的人眼跟踪算法。采用垂直积分投影函数和水平积分投影函数得到人脸图像的眼睛区域,运用眼睛区域的颜色熵消除不相关因素,定位出瞳孔的位置,用卡尔曼滤波进行实时眼部跟踪,结合眨眼检测实时修正跟踪结果。实验结果表明,该算法准确率较高,实时性较好。     

Constrained output feedback model predictive control for nonlinear systems

A constrained output feedback model predictive control approach for nonlinear systems is presented in this paper. The state variables are observed using an unscented Kalman filter, which offers some advantages over an extended Kalman filter. A nonlinear dynamic model of the system, considered in this investigation, is developed considering all possible effective elements. The model is then adaptively linearized along the prediction horizon using a state-dependent state space representation. In order to improve the performance of the control system as many linearized models as the number of prediction horizons are obtained at each sample time. The optimum results of the previous sample time are utilized for linearization at the current sample time. Subsequently, a linear quadratic objective function with constraints is formulated using the developed governing equations of the plant. The performance and effectiveness of the proposed control approach is validated both in simulation and through real-time experimentation using a constrained highly nonlinear aerodynamic test rig, a twin rotor MIMO system (TRMS).     

Speech enhancement using a masking threshold constrained Kalman filter and its heuristic implementations

A masking threshold constrained Kalman filter for speech enhancement is derived in the paper. A key step in a traditional Kalman filter requires minimizing an estimation error variance between a clean signal and its estimation. Our new method is to minimize the estimation error variance under the constraint that the energy of the estimation error is smaller than a masking threshold, computed from both time-domain forward masking and frequency-domain simultaneous masking properties of human auditory systems. The new Kalman filter provides a theoretical base for the application of the masking properties in Kalman filtering for speech enhancement. Due to the high computation cost of the proposed perceptually constrained Kalman filter, a perceptual post-filter concatenated with a standard Kalman filter is also proposed as a heuristic alternative for real-time implementation. The post-filter is constructed to make the estimation error obtained from the Kalman filter lower than the masking threshold. A wavelet Kalman filter with post-filtering is introduced to further reduce the computational load. Experimental results with colored noise show that the new constrained Kalman filter method produces the best performance when compared with other recent methods, and that the proposed heuristics with post-filtering can also produce a significant performance gain over other recent methods.     

A comparison of several nonlinear filters for reentry vehicle tracking

This paper compares the performance of several non-linear filters for the real-time estimation of the trajectory of a reentry vehicle from its radar observations. In particular, it examines the effect of using two different coordinate systems on the relative accuracy of an extended Kalman filter. Other filters considered are iterative-sequential filters, single-stage iteration filters, and second-order filters. It is shown that a range-direction-cosine extended Kalman filter that uses the measurement coordinate system has less bias and less rms error than a Cartesian extended Kalman filter that uses the Cartesian coordinate system. This is due to the fact that the observations are linear in the range-direction-cosine coordinate system, but nonlinear in the Cartesian coordinate system. It is further shown that the performance of the Cartesian iterative-sequential filter that successively relinearizes the observations around their latest estimates and that of a range-direction-cosine extended Kalman filter are equivalent to first order. The use of a single-stage iteration to reduce the dynamic nonlinearity improves the accuracy of all the filters, but the improvement is very small, indicating that the dynamic nonlinearity is less significant than the measurement nonlinearity in reentry vehicle tracking under the assumed data rates and measurement accuracies. The comparison amongst the nonlinear filters is carried out using ten sets of observations on two typical trajectories. The performance of the filters is judged by their capability to eliminate the initial bias in the position and velocity estimates.     

Adaptive extended Kalman filtering for visual motion estimation of 3D objects

An algorithm for the real-time estimation of the position and orientation of a moving object of known geometry is presented in this paper. An estimation algorithm is adopted where a discrete-time extended Kalman filter computes the object pose on the basis of visual measurements of the object features. The scheme takes advantage of the prediction capability of the extended Kalman filter for the pre-selection of the features to be extracted from the image at each sample time. To enhance the robustness of the algorithm with respect to measurement noise and modelling error, an adaptive version of the extended Kalman filter, customized for visual applications, is proposed. Experimental results on a fixed single-camera visual system are presented to test the performance and the feasibility of the proposed approach.     

基于Mean Shift和自适应预测的非刚性目标跟踪算法

传统Ｍｅａｎ Ｓｈｉｆｔ跟踪算法在目标发生机动或存在遮挡的情况下跟踪效果不理想．对此,结合目标的形状特征和颜色的可区分度对传统的颜色直方图进行改进,给出了将Ｍｅａｎ Ｓｈｉｆｔ和卡尔曼滤波器或粒子滤波器相结合的目标运动自适应跟踪算法,并针对粒子滤波器计算量大的问题,给出了运用两种不同运动式粒子进行有效预测的方法．结果表明,该算法可实现快速的非刚性目标跟踪,对目标的不规则运动和严重遮挡具有很好的鲁棒性．     

Real-time road traffic state prediction based on ARIMA and Kalman filter

The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper, a real-time road traffic state prediction based on autoregressive integrated moving average (ARIMA) and the Kalman filter is proposed. First, an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second, this ARIMA model is combined with the Kalman filter to construct a road traffic state prediction algorithm, which can acquire the state, measurement, and updating equations of the Kalman filter. Third, the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally, four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the Kalman filter is feasible and can achieve high accuracy.     

一种自适应联邦滤波在车载组合导航中的应用

联邦滤波在组合导航的应用中,具有容错性好、滤波精度高、计算量小以及实时性好的特点,但在无法得到准确的系统模型时,使用联邦滤波会出现滤波精度低甚至发散的情况。针对车载组合导航信息融合的高精度、高可靠性等要求,提出了一种组合导航的自适应联邦滤波算法。其主要思想是以判别观测数据中的野值存在与否为算法切换条件,存在野值时采用改进的增益矩阵滤波处理方法,不存在野值时则采用模糊自适应联邦滤波方法。将此方法用于SINS/GPS车载组合导航系统中,实验表明,采用的这种自适应滤波方法,能够有效抑制滤波发散,其滤波精度和收敛速度要优于常规联邦滤波,是一种有效的车载组合导航算法。     

Reduced-order estimation Part 1. Filtering

This paper presents a method for designing an ‘optimum’ unbiased reduced-order filter. For the proposed approach to work, the order of the filter must be greater than a certain minimum determined by the number of independent observations of the system available. The filler is much like a Luenberger observer for the state to be estimated, but with parameters optimized with respect to the noises in the system. A reduced-order innovation process is proposed that has properties similar to those of the full-order innovation process when the reduced filter is optimized. The approach offers the possibility of significant reduction in real-time computational requirements compared with the full-order filter, though at the cost of some loss of performance. The algorithm for the reduced-order filter is simple to implement— quite similar to that of the Kalman filter. An example is presented to compare the performance of the proposed method with the full-order Kalman filter.     

基于双眼同步运动特征约束的Kalman瞳孔跟踪算法

针对注视点研究中,红外光照明下双眼瞳孔运动的定位跟踪存在误差的问题,以双眼实时图像为研究对象,提出一种基于双眼同步运动特征约束的瞳孔跟踪算法.根据人类双眼在注视过程中的同步运动特征,把双眼瞳孔间距矢量作为隐式参数进行估计,简化包含左右眼位置、速度和双眼瞳孔间距的模型为统一的双眼同步跟踪模型,运用Kalman滤波器实现了运动特征估计和状态跟踪.实验采用自制的头戴式注视点传感装置进行眼部图像的采集.实验表明,该算法跟踪精度高,抗干扰能力强.相较于传统的以左右瞳孔位置与速度以及左右眼相对位置为状态量的算法,本文算法在位置跟踪和速度跟踪的鲁棒性上有明显改善,算法计算量也明显减少.另外,经过本文算法处理后的注视点位置估计精度大大提高,为注视点在人机交互领域中的进一步应用奠定了基础.     

一种改进的瑞利小波算法无线传感器网络流媒体传输研究

针对无线传感器网络中带宽和能量受限、误码率高、信道不稳定等因素严重影响了实时流媒体传输的问题,采用瑞利小波模型模拟无线传感器网络流媒体通信,并给出概率分布和突发特性的分析模型,基于Kal-man滤波器实时预测网络带宽,自适应地在SCTP与PRSCTP之间进行切换。仿真实验表明,瑞利小波模型能够准确地描述实时流媒体通信流,Kalman滤波器可以准确地预测实时网络带宽,而且基于带宽预测的流媒体传输技术与原有的技术相比在分组成功投递率、端到端时延和吞吐率上均具有良好的性能。     

Adaptive Fuzzy Prediction of Low-Cost Inertial-Based Positioning Errors

Kalman filter (KF) is the most commonly used estimation technique for integrating signals from short-term high performance systems, like inertial navigation systems (INSs), with reference systems exhibiting long-term stability, like the global positioning system (GPS). However, KF only works well under appropriately predefined linear dynamic error models and input data that fit this model. The latter condition is rather difficult to be fulfilled by a low-cost inertial measurement unit (IMU) utilizing microelectromechanical system (MEMS) sensors due to the significance of their long- and short-term errors that are mixed with the motion dynamics. As a result, if the reference GPS signals are absent or the Kalman filter is working for a long time in prediction mode, the corresponding state estimate will quickly drift with time causing a dramatic degradation in the overall accuracy of the integrated system. An auxiliary fuzzy-based model for predicting the KF positioning error states during GPS signal outages is presented in this paper. The initial parameters of this model is developed through an offline fuzzy orthogonal-least-squares (OLS) training while the adaptive neuro-fuzzy inference system (ANFIS) is implemented for online adaptation of these initial parameters. Performance of the proposed model has been experimentally verified using low-cost inertial data collected in a land vehicle navigation test and by simulating a number of GPS signal outages. The test results indicate that the proposed fuzzy-based model can efficiently provide corrections to the standalone IMU predicted navigation states particularly position.     

一种基于高斯混合模型的轨迹预测算法

在智能交通控制系统、军事数字化战场、辅助驾驶系统中,实时、精确、可靠的移动对象不确定性轨迹预测具有极高的应用价值.智能轨迹预测不仅可以提供精准的基于位置的服务,而且可以提前监测和预判交通状况,进而推荐最佳路线,已经成为移动对象数据库研究的热点,亟需设计准确而高效的位置预测方法.针对现有方法的不足,提出了基于高斯混合模型的轨迹预测方法GMTP,主要步骤包括:(1) 针对复杂运动模式利用高斯混合模型建模;(2) 利用高斯混合模型计算不同运动模式的概率分布,进而将轨迹数据划分为不同分量;(3) 利用高斯过程回归预测移动对象最可能的运动轨迹.GMTP是高斯非线性概率统计模型,其优势在于:计算结果不仅是位置预测值,更是关于移动对象未来所有可能运动轨迹的概率分布,可以利用概率统计分布特性获得某种运动模式(如匀加速运动)下的位置预测.大量真实轨迹数据集上的实验结果表明:与相同参数设置下的高斯回归预测和卡尔曼滤波预测法相比,GMTP的预测准确性平均提高了22.2%和23.8%,预测时间平均缩减了92.7%和95.9%.     

A real-time gaze position estimation method based on a 3-D eye model.

This paper proposes a new gaze-detection method based on a 3-D eye position and the gaze vector of the human eyeball. Seven new developments compared to previous works are presented. First, a method of using three camera systems, i.e., one wide-view camera and two narrow-view cameras, is proposed. The narrow-view cameras use autozooming, focusing, panning, and tilting procedures (based on the detected 3-D eye feature position) for gaze detection. This allows for natural head and eye movement by users. Second, in previous conventional gaze-detection research, one or multiple illuminators were used. These studies did not consider specular reflection (SR) problems, which were caused by the illuminators when working with users who wore glasses. To solve this problem, a method based on dual illuminators is proposed in this paper. Third, the proposed method does not require user-dependent calibration, so all procedures for detecting gaze position operate automatically without human intervention. Fourth, the intrinsic characteristics of the human eye, such as the disparity between the pupillary and the visual axes in order to obtain accurate gaze positions, are considered. Fifth, all the coordinates obtained by the left and right narrow-view cameras, as well as the wide-view camera coordinates and the monitor coordinates, are unified. This simplifies the complex 3-D converting calculation and allows for calculation of the 3-D feature position and gaze position on the monitor. Sixth, to upgrade eye-detection performance when using a wide-view camera, the adaptive-selection method is used. This involves an IR-LED on/off scheme, an AdaBoost classifier, and a principle component analysis method based on the number of SR elements. Finally, the proposed method uses an eigenvector matrix (instead of simply averaging six gaze vectors) in order to obtain a more accurate final gaze vector that can compensate for noise. Experimental results show that the root mean square error of gaze detection was about 0.627 cm on a 19-in monitor. The processing speed of the proposed method (used to obtain the gaze position on the monitor) was 32 ms (using a Pentium IV 1.8-GHz PC). It was possible to detect the user's gaze position at real-time speed.     

Transformer-based two-source motion model for multi-object tracking

Recently, benefit from the development of detection models, the multi-object tracking method based on tracking-by-detection has greatly improved performance. However, most methods still utilize traditional motion models for position prediction, such as the constant velocity model and Kalman filter. Only a few methods adopt deep network-based methods for prediction. Still, these methods only exploit the simplest RNN(Recurrent Neural Network) to predict the position, and the position offset caused by the camera movement is not considered. Therefore, inspired by the outstanding performance of Transformer in temporal tasks, this paper proposes a Transformer-based motion model for multi-object tracking. By taking the historical position difference of the target and the offset vector between consecutive frames as input, the model considers the motion of the target itself and the camera at the same time, which improves the prediction accuracy of the motion model used in the multi-target tracking method, thereby improving tracking performance. Through comparative experiments and tracking results on MOTchallenge benchmarks, the effectiveness of the proposed method is proved.

     

Evolutionary-Programming-Based Kalman Filter for Discrete-Time Nonlinear Uncertain Systems

Some observations and improvements on the conventional Kalman filtering scheme to function properly are presented. The improvements can be achieved using the minimal principle evolutionary programming (EP) technique. A new linearization methodology is presented to obtain the exact linear models of a class of discrete-time nonlinear time-invariant systems at operating states of interest, so that the conventional Kalman filter can work for the nonlinear stochastic systems. Furthermore, a Kalman innovation filtering algorithm and such an algorithm based on the evolutionary programming optimal-search technique are proposed in this paper for discrete-time time-invariant nonlinear stochastic systems with unknown-but-bounded plant uncertainties and noise uncertainties to find a practically implementable “best” Kalman filter. The worst-case realization of the discrete-time nonlinear stochastic uncertain systems represented by the interval form with respect to the implemented “best” nominal filter is also found in this paper for demonstrating the effectiveness of the proposed filtering scheme.     

A cascaded scheme for eye tracking and head movement compensation

The authors previously (1995) proposed an efficient method for tracking the eye movements. The proposed algorithm did not address the issue of compensating for head movements. Head movements are normally much slower than eye movements and can be compensated for using another tracking scheme for head position. In this paper, a hybrid method employing the two tracking schemes is developed. To this end, first a measurement model for the compensation of the head movement is formulated and then the overall tracking scheme is implemented by cascading two Kalman filters. The tracking of the iris movement is followed by the compensation of the head movement for each image frame. Experimental results are presented to demonstrate the accuracy aspects and the real-time applicability of the proposed approach     

On the optimality of two‐stage Kalman filtering for systems with unknown inputs

This paper is concerned with the optimal solution of two‐stage Kalman filtering for linear discrete‐time stochastic time‐varying systems with unknown inputs affecting both the system state and the outputs. By means of a newly‐presented modified unbiased minimum‐variance filter (MUMVF), which appears to be the optimal solution to the addressed problem, the optimality of two‐stage Kalman filtering for systems with unknown inputs is defined and explored. Two extended versions of the previously proposed robust two‐stage Kalman filter (RTSKF), augmented‐unknown‐input RTSKF (ARTSKF) and decoupled‐unknown‐input RTSKF (DRTSKF), are presented to solve the general unknown input filtering problem. It is shown that under less restricted conditions, the proposed ARTSKF and DRTSKF are equivalent to the corresponding MUMVFs. An example is given to illustrate the usefulness of the proposed results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society