Robust UKF orbit determination method with time-varying forgetting factor for angle/range-based integrated navigation system

In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex environment. Firstly, the Grubbs criterion was used to remove outliers, which are contained in the GPS measurement. Then, a fixed-length sliding window was used to estimate the decay factor adaptively. Based on the fixed-length sliding window method, the time-varying noises, which are considered in integrated navigation system, are addressed. Moreover, a MCC method is used to suppress the non-Gaussian noises, which are generated with external corruption. Finally, the method, which is proposed in this paper, is verified by the designed simulation and field tests. The results show that the influence of the non-Gaussian noise and time-varying noise of the GPS measurement is detected and isolated by the proposed algorithm, effectively. The navigation accuracy and stability are improved.

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.

Adaptive Kalman filters (AKF) have been widely applied to the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system. However, the traditional AKF methods suffer from the problems of filtering instability or covariance underestimation, especially when the GNSS measurement disturbances occur. In this paper, an enhanced redundant measurement-based AKF is developed to improve the filtering performance. The scheme is based on the mutual difference sequence derived from the redundant measurement of INS. By using the mutual difference sequence, the measurement noise covariance can be estimated without being affected by the inaccuracy estimates, hence avoiding the risk of filtering divergence. In addition, the kernel density estimation is used to estimate the GNSS measurement noise’s probability density to detect whether the Gaussian properties of the measurement noise are maintained. When the noise statistics are far from Gaussian distribution, the difference sequence will be modeled as an autoregressive process using the Burg’s method. The real variance of the difference sequence can then be updated relying on the autoregressive model in order to avoid the covariance underestimation. A field experiment was carried out to evaluate the performance of the proposed method. The test results demonstrate that the proposed method can effectively mitigate the GNSS measurement disturbances and improve the accuracy of the navigation solution.

Robust adaptive Unscented Kalman Filter with gross error detection and identification for power system forecasting-aided state estimation

With the advantages of high velocity measurement accuracy and fast dynamic response, the laser Doppler velocimeter (LDV) is expected to replace the odometer to be combined with a strapdown inertial navigation system (SINS) to form a higher precision integrated navigation system. However, LDV scale factor error and misalignment angles between LDV and inertial measurement unit will affect the accuracy of navigation. Considering that not all global navigation satellite system (GNSS) receivers can directly provide velocity information and current mainstream calibration methods are sensitive to the measurement noise and outliers of velocity and position information, a robust calibration method aided by GNSS is proposed in this paper, which is based on position observation. Different from current popular calibration methods, the attitude information of the GNSS/SINS integrated navigation system obtained by an adaptive Kalman filter is used to construct the observation vector together with LDV velocity outputs and GNSS position outputs in this method. The LDV scale factor error and the misalignment angle are determined by the ratio of two observation vector modulus and the Davenport's q-method method, respectively. The accuracy and robustness of the calibration method are verified by one vehicle test with normal GNSS signals and one vehicle test with GNSS signals with outliers. And the horizontal position error of dead reckoning of the calibrated LDV/SINS integrated system are less than 0.0314% and 0.1033% of the mileage, respectively.

In order to improve the positioning accuracy of two leader autonomous underwater vehicles (AUVs) in underwater cooperative navigation formation, a decentralized cooperative localization algorithm based on adaptive cubature Kalman filter (ACKF-DCL) for strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated navigation system is proposed. Firstly, the kinematic equations of the SINS are used to establish the direct filtering approach model for integrated navigation. Secondly, the Sage-Husa adaptive filtering algorithm and the sequential Kalman filtering (SKF) method are introduced into the nonlinear cubature Kalman filter (CKF) to adjust adaptively the measurement noise and maintain the positivity of the covariance matrix. Finally, the cross-correlation covariance between two AUVs is derived according to the characteristics of a decentralized data structure, so as to track the inter-AUV dependencies. And the acoustic ranging measurement is introduced to achieve relative measurement update. The lake trial demonstrates the proposed algorithm’s effectiveness and superiority, and the results show that the algorithm can effectively track the measurement noise covariance matrix and improve the positioning accuracy of both AUVs by position correction.

Filtering algorithm is One of the key technologies that affect the performance and reliability of INS/GPS integrated navigation systems. The paper adopts hybrid filtering algorithm based on neural network. Using the powerful learning and identification capabilities of the neural network, the hybrid filtering algorithm solve the problem of the Kalman filtering precision degradation caused by the system noise and the measurement noise and the absence of the GPS signal. The simulation results show that the hybrid filtering algorithm is effective.

Aiming at the situation that the underwater integrated navigation system is easy to be disturbed and the output is unavailable during the task of underwater vehicle, a RVBAKF/ LSTM hybrid method is proposed in this paper. Considering the actual underwater working environment of autonomous underwater vehicles (AUV), the measured value of Doppler velocity log (DVL) is prone to outliers or even interruption, so it is very important to improve the fault tolerance of integrated navigation system. For the processing of outliers, a robust adaptive filter based on VB theory is proposed. Its advantage is to realize the adaptation of time-varying noise and reduce the interference of outliers to the integrated navigation system. Firstly, selecting the SINS output related to AUV velocity, such as velocity, attitude, angular velocity and specific force as the training input when the DVL output is normal. When DVL output is interrupted, the long short-term memory (LSTM) model is used to provide pseudo measurement values for integrated navigation system. The effectiveness of the method is verified by the on-board experimental data. When the DVL output is interrupted, the method can effectively improve the accuracy of the integrated navigation system. The method in this paper is compared with four different hybrid methods, and the results are obviously better than the four methods.

Co-estimation for capacity and state of charge for lithium-ion batteries using improved adaptive extended Kalman filter

A method for detecting faults in integrated navigation system based on improved SPRT adaptive filtering

Navigation includes the integration of methodologies and systems for estimating time-varying position, velocity and attitude of moving objects. Navigation incorporating the integrated inertial navigation system (INS) and global positioning system (GPS) generally requires extensive evaluations of nonlinear equations involving double integration. Currently, integrated navigation systems are commonly implemented using the extended Kalman filter (EKF). The EKF assumes a linearized process, measurement models and Gaussian noise distributions. These assumptions are unrealistic for highly nonlinear systems like land vehicle navigation and may cause filter divergence. A particle filter (PF) is developed to enhance integrated INS/GPS system performance as it can easily deal with nonlinearity and non-Gaussian noises. In this paper, a hybrid extended particle filter (HEPF) is developed as an alternative to the well-known EKF to achieve better navigation data accuracy for low-cost microelectromechanical system sensors. The results show that the HEPF performs better than the EKF during GPS outages, especially when simulated outages are located in periods with high vehicle dynamics.

Integration is a current trend in navigation system research. MIMU (Micro Inertial Measurement Unit) is compact, cheap, low-power, and highly reliable. BD (BeiDou System) is a satellite navigation system with our own independent intellectual property right. Integrating MIMU with BD can lessen MIMU's error accumulation and BD's low robustness, thus making MIMU/BD an ideal integrated navigation system. However, MIMU/BD has poor heading error observability. A three-axis magnetometer (TAM) can measure earth's magnetic field to obtain carrier heading. This paper focuses on improving heading accuracy of MIMU/BD integrated navigation system with TAM aided. TAMs are used to calculate the heading angle. Calibrating TAMs using a RLS algorithm with a forgetting factor is introduced. An integrated navigation algorithm based on Kalman Filter is used. Vehicle experiments show that this method efficiently improves the heading accuracy.

The robust adaptive filtering method based on the State Dependent Riccati Equation technique for SINS/SAR integrated navigation system. This method adopts the state dependent coefficient form to convert nonlinear system model into linear system for avoiding the errors caused by the traditional numerical linearization process. It also adopts the concepts of robust estimation and adaptive factor to construct reasonably the discriminate statistics and adaptive factor for resisting the disturbances of singular observations and kinematic model error. Experimental results and comparison analysis demonstrate that the proposed filtering method can not only effectively resist disturbances from nonlinear system state noise and observation noise, but it also can achieve higher accuracy than the extended kalman filtering and SDRE filtering methods.

Accuracy and reliability are critical for integrated navigation systems. This paper presents an adaptive fault-tolerant in-motion alignment approach aimed at enhancing the reliability of the SINS/GPS/PNS integrated navigation system. Firstly, the error of polarization angle, influenced by environmental factors, is formulated for the purpose of real-time error estimation. Secondly, an improved adaptive federated strong tracking unscented Kalman filter (AFSTUKF) is proposed to proficiently address model uncertainties and to guarantee dependable performance in the presence of noise fluctuations or anomalies. Furthermore, the AFSTUKF integrates an adaptive feedforward information-sharing algorithm (ISA) and a fault detection and isolation (FDI) algorithm. The ISA is designed to dynamically modify information-sharing parameters in order to mitigate the risk of global information contamination resulting from inaccuracies in filtering or sensor malfunctions, thereby constituting a significant improvement over traditional federated filtering methodologies. The FDI algorithm employs Bayesian estimation techniques to recalibrate thresholds in accordance with system uncertainty, effectively addressing the shortcomings of conventional chi-square tests and enhancing overall robustness. Experimental results from a moving vehicle validate the high accuracy and reliability of the proposed method.

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.