Research on satellite attitude determination with adaptive filter

In the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) of non-linear system, the mathematical model and the noise statistics are essential; this limitation was settled by adaptive algorithm. The adaptive extended Kalman filter (AEKF) and adaptive unscented filter (AUKF) were proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. The simulation result shows that the adaptive algorithm in EKF and UKF can improve the performance of filter and decrease the computing time.

MEMS (micro-electro-mechanical-system) based gyroscopes are commonly used for satellite's attitude determination and control system in recent years. They are packaged in a small form factor and have lower power consumption. They provide a low-cost solution to the emerging new space industry. MEMS gyroscopes exhibit some time-zero null bias with variation over temperature. To overcome this problem, this paper proposes a novel on-orbit gyroscope bias compensation technique based on the model predictive control approach. The proposed method can estimate the gyroscope bias instantly which is not feasible with the typical method. Furthermore, the proposed method has a low computational requirement as compared to the typical EKF (Extended Kalman Filter) method. While the proposed method allows on-orbit compensation of gyroscope bias, the satellite's attitude is required to be nearly at steady state. This limitation will be addressed in detail.

An adaptive Kalman filter algorithm is adopted to estimate the state of charge (SOC) of a lithium-ion battery for application in electric vehicles (EVs). Generally, the Kalman filter algorithm is selected to dynamically estimate the SOC. However, it easily causes divergence due to the uncertainty of the battery model and system noise. To obtain a better convergent and robust result, an adaptive Kalman filter algorithm that can greatly improve the dependence of the traditional filter algorithm on the battery model is employed. In this paper, the typical characteristics of the lithium-ion battery are analyzed by experiment, such as hysteresis, polarization, Coulomb efficiency, etc. In addition, an improved Thevenin battery model is achieved by adding an extra <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> branch to the Thevenin model, and model parameters are identified by using the extended Kalman filter (EKF) algorithm. Further, an adaptive EKF (AEKF) algorithm is adopted to the SOC estimation of the lithium-ion battery. Finally, the proposed method is evaluated by experiments with federal urban driving schedules. The proposed SOC estimation using AEKF is more accurate and reliable than that using EKF. The comparison shows that the maximum SOC estimation error decreases from 14.96% to 2.54% and that the mean SOC estimation error reduces from 3.19% to 1.06%.

In this paper, Rauch-Tung-Striebel(RTS) optimal smoothing algorithm is applied to improving off-line satellite attitude determination accuracy. The measurements are first processed by the forward filter, and then, a separate backward smoothing pass is used for obtaining the smoothing solution. The performances between RTS and extended Kalman filter(EKF) in gyro/star-sensor integrated system are compared. RTS smoother exhibits better accuracy and robustness than EKF under the situation of lower sensors precision, different length of data and larger initial attitude error. The results verify the effectiveness and practicability of RTS smoothing algorithm in satellite attitude determination.

This paper aims to apply adaptive filtering algorithms in sensor array processing for underwater surveillance and detection systems with linear arrays. Adaptive filters are iterative in nature and continuously update the filter taps to minimize the error signal based upon certain criterion. Adaptive filters are used in beamforming to place the main beam in the direction of desired signal and place nulls in the direction of unwanted signals. Adaptive filters are also used in spectral analysis of acoustic signals for background noise cancellation. This paper evaluates the performance of three adaptive filtering algorithms with respect to sensor array design parameters i.e. number of sensors, sensor spacing, number of incident signals and their angular separation. The three adaptive filtering algorithms namely least mean square (LMS), normalized LMS (NLMS), and sample matrix inversion (SMI) have been compared in the context of linear array beamforming. The paper provides detailed discussions on the simulation results of these adaptive algorithms in terms of convergence speed, beamwidth, null depths and maximum side lobe levels. In addition the paper illustrates a practical example of applying adaptive filters in underwater sensor array processing systems for detection of target’s acoustic signatures.

Adaptive filters are prevalent in many real‐time signal processing applications. Many adaptive algorithms already exist, but most of them assume white Gaussian noise as disturbance. However, for many applications such as electrocardiogram, foetus heart rate measurement, low frequency atmospheric noise, underwater acoustic noise and signal measurement in instrumentation, the impulsive noise is more common. This study presents a modified robust mixed norm (MRMN) adaptive filter algorithm robust to impulsive noise with higher convergence rate and lower steady state error (SSE). MRMN adaptive filter algorithm has been simulated using Matlab and Xilinx system generator high level synthesis tool and a significant improvement in SSE and convergence speed is obtained compared with the existing adaptive filter algorithms for similar specifications. The proposed algorithm is also described using VHDL and synthesised using Xilinx synthesiser tool in order to implement on field‐programmable gate array (FPGA). The FPGA post route and place implementation results show nearly 90% reduction in resource utilisation and nearly 2.6 times improvement in clock frequency as compared with the existing FPGA based implementation for similar specifications.

The benefits of optical current transducers (OCT) have been intensively investigated in recent years. However, temperature and stress-induced linear birefringence in sensing materials cause error and instability, which limits severely the practical application of OCTs. In order to take advantages of both the conventional CT and OCT, the paper presents a new kind of hybrid optical CT formed by the conventional CT with high accuracy in the steady state and the optical CT with no saturation under fault current conditions. By means of the adaptive Kalman filter algorithm derived in this paper, the hybrid optical CT is adjusted adaptively to improve the accuracy to satisfy the need of measurement and protection in the power systems. In addition, applying the noise estimation with unknown prior statistics to the basic Kalman adaptive filter equations, the adaptive Kalman filter algorithm with noise estimation is derived in the paper. The test results show the validity of the hybrid optical CT and the adaptive Kalman filter algorithm.

Given the complex traffic factors and strong mobility of a transit vehicles, it is difficult to predict its travel time with the conventional prediction models. The emphasis of this research effort is to improve the adaptability of the Kalman filter model with regard to transit bus travel time prediction. In order to resolve the filter divergence and calculated divergence in the prediction process, an adaptive fading Kalman filter algorithm is put forward in this paper. Compared with the conventional Kalman filter prediction model, a "forgotten factor" is added to the adaptive fading Kalman filter algorithm to restrain the influence of the old data on the filter. Furthermore, it can correct the predictive value and the model parameters with the latest measured value, hence higher adaptability and predictive accuracy. Finally, the GPS data of transit vehicles in the city of Yichun is used in the paper to verify the effectiveness of the new model. The results reveal that the adaptive fading Kalman filter algorithm is more effective than the conventional one. It performs better in terms of convergence and dynamic adaptability, implying a brighter application prospect.

Objective.Seismocardiography (SCG) contains rich physiological information about the structure and function of the heart, providing a new dimension for early screening and dynamic monitoring of cardiovascular diseases. However, SCG is relatively weak, susceptible to severe external interference, even has strong individual differences in morphology, making it difficult for traditional algorithms to accurately detect AO, AC and other core features.Approach.Herein, Combining the time-frequency joint distribution characteristics of SCG, we improve a novel adaptive extended Kalman filter (KF) algorithm for accurate AO and AC detection. To achieve unified modeling for different individual SCG, Gaussian mixture module is used to fit the morphological template in the time-frequency domain based on the optimal estimation strategy. Then, in order to balance the estimation accuracy and computational efficiency of the nonlinear system constructed based on SCG, the extended KF is constructed by linearizing the state transition equation. Moreover, with the aim of accurately estimating the time-varying noise components in motion, the forgetting factorαkis introduced based on the residualekwith the low-pass filtering strategy to adaptively update the measurement noise covariance matrixR, thereby achieving high-quality filtering to SCG with the AO and AC area.Main results.In addition, the experiment is conducted on the open-source CEBS dataset, indicating that the proposed algorithm has better filtering effect and higher AO and AC' detection accurate on the static SCG. Furthermore, the portable hardware system is designed for collecting SCG during 6 min walk test. Meanwhile, the impedance cardiography equipment is employed to record heart rate, left ventricular ejection time and other hemodynamics parameters. Compared with these common algorithms, the proposed algorithm also has better detection performance on the SCG during exercise.Significance.In the future, the proposed algorithm will be integrated with the portable SCG hardware system designed, which is expected to be applied in the convenient diagnosis of heart diseases, the dynamic measurement of cardiovascular parameters, the dynamic blood pressure measurement without the need for wearing cuffs and more medical scene.

Estimation of battery state of charge based on changing window adaptive extended Kalman filtering

At the same orbital height, the radiation environment in SAA region is worse than that in other regions. The main difference is that the region contains high energy particles. CCD star sensors has been developed for use in the attitude determination and control system of satellites, we have processed star sensors flight data and star images acquired by satellites in orbit in an attempt to characterize the performance of CCD star sensors in the abnormal region of the South Atlantic. This paper also introduces the influence of high energy particles exist in the South Atlantic Anomaly on CCD photoelectric imaging detectors. The abnormal phenomena and generation mechanism in the star images has been analyzed. The improvement measures and suggestions for high precision star sensors to avoid or eliminate the influence of SAA has been given finally, which provides reference for improving the performance of star sensors.

The stochastic nature of noise signals affects the vehicle’s internal states and the outputs, resulting in the difficulty in estimation. The unknown or time-varying nature of noise signals if not taken into account for estimation, the results will diverge and be highly deteriorated. In this paper, a maximum likelihood principle (MLP) based adaptive robust extended kalman filter for estimating the states of the adopted non-linear vehicle model is proposed. An observability test is done for the purpose of estimation. A covariance matching (CM) based robust adaptive high forgetting factor based fault tolerant technique is also employed on the robust adaptive extended and unscented kalman filters for comparison purpose. The Robustness of the filter is analyzed by varying the parameter of the vehicle through a local sensitivity analysis. The results show that the MLP based approach to the extended kalman filter performs well in three simulations for sinusoidal steering, Double Lance Change, J-Turn, Fishhook, Slalom maneuver in comparison to robust adaptive unscented kalman filter. Friction coefficient of 0.8 (dry road) and 0.4 (wet road) is chosen for the simulation. The sideslip angle RMSE value for MLP based estimation is obtained as 2.62e-05, 4.545e-06 for Sine and DLC maneuver.

Robust adaptive smooth variable structure Kalman filter for spacecraft attitude estimation

This paper presents a new robust adaptive unscented particle filtering algorithm by adopting the concept of the adaptive robust filtering to the unscented particle filter. This algorithm adaptively determines the equivalent weight function and adaptively adjusts the adaptive factor constructed from predicted residuals to resist the disturbances of singular observations and the kinematic model noise, thus preventing particles from degeneracy. It also uses the unscented transformation to improve the accuracy of particle filtering, thus providing the reliable state estimation for improving the performance of adaptive robust filtering. Experiments and comparison analysis demonstrate that the proposed filtering algorithm can effectively resist disturbances due to system state noise and observation noise, and the filtering accuracy is much higher than the extended Kalman filter, unscented Kalman filter, standard particle filter and unscented particle filter.

A novel method of indoor mobile robot navigation is presented. The proposed approach fuses the data of odometry and electronic compass for navigation. It includes two calibration methods and a fusion algorithm. First of all, calibration method of systematic odometry error is used to reduce the error of navigation and provide the reliable estimate of pose of mobile robot for adaptive extended Kalman filter fusion algorithm later. Secondly, calibration method of electronic compass using an adaptive neural fuzzy inference system provides direction angle of mobile robot as observation for adaptive extended Kalman filter algorithm later. Finally, a fusion algorithm using adaptive extended Kalman filter algorithm fuses data of odometry and electronic compass that provides the position and orientation of mobile robot. In addition, the value of the parameter k of adaptive extended Kalman filter algorithm which is related to the process noise covariance is decided by fuzzy algorithm. In order to illustrate the feasibility of the proposed approach, two types of experiments are done: the first-type experiment is that the mobile robot runs along default path only with odometry, and the mobile robot with odometry and electronic compass in the second-type experiment utilizes the proposed approach for navigation. The results of experiments show that the error of localization and navigation in the first-type experiment is larger than one in the second-type experiment. They prove the good performance of the proposed approach.