基于可变减载率超速控制的双馈异步风机参与微电网调频研究

为了使双馈异步风机(DFIG)能够更好地参与微电网频率调节并减少有功控制成本,综合考虑风机向微电网输送有功功率和提供备用容量参与系统调频两方面因素,提出了变减载率超速控制法。计及风速出现概率,采用概率加权求和法,考虑减载成本、调频效益和调频恢复成本求解超速控制成本(COC),得到变减载率曲线。通过对一个含DFIG的微电网系统进行仿真分析,验证所提方法可有效改善微电网频率动态特性,减少风机有功控制成本,有利于微电网的稳定运行。     

Multi-EAP:Extended EAP for multi-estimate extraction for SMC-PHD filter

The ability to extract state-estimates for each target of a multi-target posterior, referred to as multi-estimate extraction (MEE), is an essential requirement for a multi-target filter, whose key performance assessments are based on accuracy, computational efficiency and reliability. The probability hypothesis density (PHD) filter, implemented by the sequential Monte Carlo approach, affords a computationally efficient solution to general multi-target filtering for a time-varying num-ber of targets, but leaves no clue for optimal MEE. In this paper, new data association techniques are proposed to distinguish real measurements of targets from clutter, as well as to associate par-ticles with measurements. The MEE problem is then formulated as a family of parallel single-estimate extraction problems, facilitating the use of the classic expected a posteriori (EAP) estima-tor, namely the multi-EAP (MEAP) estimator. The resulting MEAP estimator is free of iterative clustering computation, computes quickly and yields accurate and reliable estimates. Typical sim-ulation scenarios are employed to demonstrate the superiority of the MEAP estimator over existing methods in terms of faster processing speed and better estimation accuracy.     

基于UKF自主定姿方法研究

轻小型飞行器在飞行中卫星导航失效时,余度控制回路要求导航系统具有自主确定姿态的能力.提出了基于IMU的输出确定水平姿态的方法,并采用UKF实现飞行中的实时滤波估计.对某无人机实际飞行的MEMS型IMU数据进行了仿真,结果表明该方法给出的姿态角信息满足控制精度需求.将UKF与EKF滤波估计结果进行比较,UKF更具有优越性.     

基于粒子滤波算法的非刚性目标实时跟踪

基于颜色的粒子滤波实时跟踪算法主要是利用视频图像的颜色直方图信息,综合考虑运动预测和帧间的相似性来确定目标的位置。针对影响粒子滤波算法性能的关键技术,提出了基于混合高斯模型的粒子滤波算法,并将其用于基于颜色的非刚性目标的实时跟踪相关问题。该算法使用混合高斯模型表示粒子,在每个时刻的修正步骤之后,采用EM算法对粒子进行重新拟合。仿真实验表明,本算法在保证跟踪准确度的同时,可以满足实时跟踪的要求。     

A variational Bayesian-based robust adaptive filtering for precise point positioning using undifferenced and uncombined observations

In the application of precise point positioning (PPP), especially in the dynamic mode, the classical Kalman filter (KF) usually produces a large number of estimation errors or diverges when there are gross errors in the observation data or unexpected turbulences occur in target motion state or both of them. For such problem, a variational Bayesian (VB)-based robust adaptive Kalman filtering (VB-RAKF) is proposed in this paper. This filter introduces a classification robust equivalent weight function to resist observation gross error and the inverse Wishart prior to model inaccurate process noise covariance matrix (PNCM). To improve the instantaneous accuracy of state estimation, the VB approach is used to obtain better estimations of inaccurate PNCM. Several sets of observation data collected by IGS reference stations and vehicles are employed to check the robustness and positioning accuracy of the VB-RAKF model. The results show that the VB-RAKF algorithm is more robust than the KF, and can effectively resist the gross error in observation data and control state disturbance. In the IGS reference station tests, when compared to the KF, the static positioning accuracies of the VB-RAKF in the north, east and up directions are improved by 13%, 8% and 22%, respectively, and the simulated dynamic positioning accuracies of the VB-RAKF in the north, east and up directions are improved by 19%, 9% and 21%, respectively. The in-vehicle dynamic test verifies that the VB-RAKF outperforms the KF, and shows that the VB-RAKF has better performance than the KF when dealing with observation data which has obvious gross errors, and similar performance as the KF when gross errors are small.     

Lightweight hybrid visual-inertial odometry with closed-form zero velocity update

Visual-Inertial Odometry (VIO) fuses measurements from camera and Inertial Measurement Unit (IMU) to achieve accumulative performance that is better than using individual sensors. Hybrid VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter (MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, one-dimensional inverse depth parametrization is utilized to parametrize the augmented feature state. This modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene, a novel closed-form Zero velocity UPdaTe (ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.^①     

Probabilistic resident space object detection using archival THEMIS fluxgate magnetometer data

Recent progress in the detection of small space objects, at geosynchronous altitudes, through ground-based optical and radar measurements is demonstrated as a viable method. However, in general, these methods are limited to detection of objects greater than 10?cm. This paper examines the use of magnetometers to detect plausible flyby encounters with charged space objects using a matched filter signal existence binary hypothesis test approach. Relevant data-set processing and reduction of archival fluxgate magnetometer data from the NASA THEMIS mission is discussed in detail. Using the proposed methodology and a false alarm rate of 10%, 285 plausible detections with probability of detection greater than 80% are claimed and several are reviewed in detail.     

Real-time sensor fault detection and isolation for LEO satellite attitude estimation through magnetometer data

Attitude estimation is a critical component of the Attitude Determination and Control System (ADCS) of any satellite. It is used to convert the sensor observation data to an estimated attitude using filtering algorithms. However, in the presence of sensor faults, the ADCS fails to achieve the desired attitude accuracy. In this paper, the Fault Tolerant Extended Kalman Filter (FTEKF) is proposed to handle this imperfection. In accordance, various filtering steps are included in the FTEKF design to enhance both attitude estimation and sensor fault detection. The developed algorithm can detect and isolate any unexpected sensor faults in real time, which provides a reliable attitude estimation. A comparative study with the classical and robust Kalman filters is performed through numerical simulations in order to validate the effectiveness of the adopted filter in case of magnetometer fault data.     

Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm

The receiver autonomous integrity monitoring (RAIM) is one of the most important parts in an avionic navigation system. Two problems need to be addressed to improve this system, namely, the degeneracy phenomenon and lack of samples for the standard particle filter (PF). However, the number of samples cannot adequately express the real distribution of the probability density function (i.e., sample impoverishment). This study presents a GPS receiver autonomous integrity monitoring (RAIM) method based on a chaos particle swarm optimization particle filter (CPSO-PF) algorithm with a log likelihood ratio. The chaos sequence generates a set of chaotic variables, which are mapped to the interval of optimization variables to improve particle quality. This chaos perturbation overcomes the potential for the search to become trapped in a local optimum in the particle swarm optimization (PSO) algorithm. Test statistics are configured based on a likelihood ratio, and satellite fault detection is then conducted by checking the consistency between the state estimate of the main PF and those of the auxiliary PFs. Based on GPS data, the experimental results demonstrate that the proposed algorithm can effectively detect and isolate satellite faults under conditions of non-Gaussian measurement noise. Moreover, the performance of the proposed novel method is better than that of RAIM based on the PF or PSO-PF algorithm.     

Doppler utilised Kalman estimation (DUKE) of ionospheric delay for satellite navigation

The ionospheric delay experienced by the satellite navigation signals depends upon the Total Electron Content (TEC) and needs to be corrected. While the single frequency receivers always use parametric models to correct this delay, dual frequency receivers, when suffers a loss of lock of one of its signal, also has to resort to these models. Here, an alternative method, based on Doppler, surrogated by range rate variation, has been attempted to estimate the ionospheric delay using a Kalman filter. GPS data have been used for all visible satellites over four days selected around the equinox and solstice with nominal geomagnetic conditions and estimations done in continuous and calibrated modes. Results of continuous estimation, obtained for a mid latitude station, showed moderate accuracy while it was significantly better for the calibrated mode with no seasonal dependence. Estimations done for station within the extent of equatorial anomaly, has not only resulted in relative deterioration in performance, but also shown seasonal dependence. Compared with estimates of Klobuchar model, the Calibrated estimation showed superior performance, conspicuously in the mid latitude station. However, for the continuous mode, performance was at par with the model at higher latitudes but inferior to it in regions within the extent of the equatorial anomaly.