Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations

An accurate and reliable positioning system (PS) is a significant topic of research due to its broad range of aerospace applications, such as the localization of autonomous agents in GPS-denied and indoor environments. The PS discussed in this work uses ultra-wide band (UWB) sensors to provide distance measurements. UWB sensors are based on radio frequency technology and offer low power consumption, wide bandwidth, and precise ranging in the presence of nominal environmental noise. However, in practical situations, UWB sensors experience varying measurement noise due to unexpected obstacles in the environment. The localization accuracy is highly dependent on the filtering of such noise, and the extended Kalman filter (EKF) is one of the widely used techniques. In varying noise situations, where the obstacles generate larger measurement noise than nominal levels, EKF cannot offer precise results. Therefore, this work proposes two approaches based on EKF: sequential adaptive EKF and piecewise adaptive EKF. Simulation studies are conducted in static, linear, and nonlinear scenarios, and it is observed that higher accuracy is achieved by applying the proposed approaches as compared to the traditional EKF method.

Robust UKF-based filtering for tracking a maneuvering hypersonic glide vehicle

To track a non-cooperative hypersonic glide vehicle (HGV) without any precise information, an approach to the state estimation is presented based on a robust UKF-based filter (RUKFBF) in this paper. The HGV has an uncertain reentry motion because of unknown maneuvers which is a primary factor leading to degradation of tracking accuracy. Aiming at enhancing accuracy, the strong tracking algorithm (STA) is introduced to addressing the model error caused by a bank-reversal maneuver of HGV. Furthermore, the Huber technique is employed to deal with possible measurement model errors. In the RUKFBF, mutual interferences are suppressed between the STA and the Huber technique via two strategies. The one is that the calculation of the fading factor in the STA adopts an unmodified measurement noise covariance, and the other one is that two judgment criteria are proposed to limit large fading factors in the presence of measurement model errors. To simulate real tracking scenarios, the RUKFBF is tested through tracking a HGV trajectory considering a practical guidance strategy. Simulation results demonstrate the effectiveness of the RUKFBF in the presence of model errors and the observability of the estimated state.

Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion

The ability of the underwater vehicle to determine its precise position is vital to completing a mission successfully. Multi-sensor fusion methods for underwater vehicle positioning are commonly based on Kalman filtering, which requires the knowledge of process and measurement noise covariance. As the underwater conditions are continuously changing, incorrect process and measurement noise covariance affect the accuracy of position estimation and sometimes cause divergence. Furthermore, the underwater multi-path effect and nonlinearity cause outliers that have a significant impact on positional accuracy. These non-Gaussian outliers are difficult to handle with conventional Kalman-based methods and their fuzzy variants. To address these issues, this paper presents a new and improved adaptive multi-sensor fusion method by using information-theoretic, learning-based fuzzy rules for Kalman filter covariance adaptation in the presence of outliers. Two novel metrics are proposed by utilizing correntropy Gaussian and Versoria kernels for matching theoretical and actual covariance. Using correntropy-based metrics and fuzzy logic together makes the algorithm robust against outliers in nonlinear dynamic underwater conditions. The performance of the proposed sensor fusion technique is compared and evaluated using Monte-Carlo simulations, and substantial improvements in underwater position estimation are obtained.

Position and Speed Estimation of PMSM Based on Extended Kalman Filter Tuned by Biogeography-Based-Optimization

In a sensorless control of PMSM based on Extended Kalman Filter (EKF), the correct selection of system and measurement noise covariance has a great influence on the estimation performances of the filter. In fact, it is extremely difficult to find their optimal values by trial and error method. Therefore, the main contribution of this work is to prove the efficiency of Biogeography-Based-Optimization (BBO) technique to obtain the optimal noise covariance matrices Q and R. The BBO and EKF combination gives a BBO-EKF algorithm, which allows to estimate all the state variables of PMSM drive particularly, the rotor position and speed. In this paper, three evolutionary algorithms namely Particle Swarm Optimization (PSO), genetic algorithms (GAs) and BBO are used to get the best Q and R of EKF. Simulations tests performed in Matlab Simulink environment show excellent performance of BBO-EKF compared to GAs-EKF and PSO-EKF approaches either in resolution or in convergence speed.

Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion

In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance.

Penentuan Orientasi dan Translasi Gerakan UAV menggunakan Data Fusion berbasis Kalman Filter

In an unmanned aircraft vehicle, a navigation system is needed to calculate its orientation and translation. The navigation system can utilize data from the accelerometer, gyroscope, magnetometer, and GPS. The orientation can be precisely calculated from the accelerometer and magnetometer data when the sensor is in a static state. Meanwhile, under dynamic conditions, the orientation can be more precisely calculated from the gyroscope data. In order to obtain the robust navigation system, a data fusion based on Kalman filter is built to calculate the orientation from the accelerometer, gyroscope, and magnetometer. The Kalman filter trusts more in the data from the accelerometer and magnetometer when the UAV is static and trusts more in to the gyroscope data when the UAV is in dynamic conditions. Meanwhile, the UAV translation is obtained by performing data fusion of the accelerometer data with location data from the GPS sensor. The Kalman filter combines data from the accelerometer and GPS when available, otherwise trusts in data from the accelerometer only. The trust level shifting is done by changing the measurement noise covariance. The data fusion based on Kalman filter provides more accurately the orientation and translation data. The orientation as a result of the calculation from the gyroscope has an average error of 18.12%, while the orientation as a result of the accelerometer and magnetometer has an error of 1.3%. By using Kalman filter-based data fusion, the error of the orientation decreases to 0.87%

A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference

In this paper, a novel anti-jamming technique based on black box variational inference for INS/GNSS integration with time-varying measurement noise covariance matrices is presented. We proved that the time-varying measurement noise is more similar to the Gaussian distribution with time-varying mean value than to the Inv-Gamma or Inv-Wishart distribution found by Kullback–Leibler divergence. Therefore, we assumed the prior distribution of measurement noise covariance matrices as Gaussian, and calculated the Gaussian parameters by the black box variational inference method. Finally, we obtained the measurement noise covariance matrices by using the Gaussian parameters. The experimental results illustrate that the proposed algorithm performs better in resisting time-varying measurement noise than the existing Variational Bayesian adaptive filter.

Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter based on Variational Bayes (VB) approximation is proposed in this paper. The measurement noise covariance is described in the linear system by the inverse Wishart (IW) distribution. Then, the fading factor in the strong tracking principle uses the optimal measurement noise covariance at the previous moment to control the state prediction covariance in real-time. The Gaussian IW (GIW) joint distribution adopts the VB approximation to jointly return the measurement noise covariance and the target state covariance. The simulation results show that, compared with the traditional Gaussian mixture PHD (GM-PHD) and the VB-adaptive PHD, the proposed algorithm has higher tracking accuracy and stronger robustness in a more reasonable calculation time.