Maximum Correntropy Research Articles

Robust joint estimation of sideslip angle and tire lateral force for distributed drive electric vehicle

Accurate estimation of vehicle sideslip angle (SA) and tire lateral force (TLF) is essential for the effective functioning of vehicle active safety control systems. However, effective estimation of SA and TLF using low-cost on-board sensors is a major challenge. In this paper, a robust joint estimation method of SA and TLF for distributed drive electric vehicle (DDEV) is proposed. Adaptive sliding-mode observer (ASMO) is used to estimate the TLF and is used as the input to the subsequent filter. Maximum correntropy criterion (MCC) is introduced to improve the robustness of the unscented Kalman filter (UKF) under mixed Gaussian noise, and the maximum correntropy unscented Kalman filter (MCUKF) algorithm is composed to estimate SA. The effectiveness of the proposed ASMO and MCUKF joint estimation method is verified by Simulink/Carsim joint simulation experiments. The experimental results show that compared with the existing methods, the estimation method proposed in this paper effectively improves the estimation accuracy and robustness of SA and TLF under the mixed Gaussian environment, which is of great significance for the improvement of vehicle active safety.

Event-Driven Maximum Correntropy Filter Based on Cauchy Kernel for Spatial Orientation Using Gyros/Star Sensor Integration

Gyros/star sensor integration provides a potential method to obtain high-accuracy spatial orientation for turntable structures. However, it is subjected to the problem of accuracy loss when the measurement noises become non-Gaussian due to the complex spatial environment. This paper presents an event-driven maximum correntropy filter based on Cauchy kernel to handle the above problem. In this method, a direct installation mode of gyros/star sensor integration is established and the associated mathematical model is derived to improve the turntable’s control stability. Based on this, a Cauchy kernel-based maximum correntropy filter is developed to curb the influence of non-Gaussian measurement noise for enhancing the gyros/star sensor integration’s robustness. Subsequently, an event-driven mechanism is constructed based on the filter’s innovation information for further reducing the unnecessary computational cost to optimize the real-time performance. The effectiveness of the proposed method has been validated by simulations of the gyros/star sensor integration for spatial orientation. This shows that the proposed filtering methodology not only has strong robustness to deal with the influence of non-Gaussian measurement noise but can also achieve superior real-time spatial applications with a small computational cost, leading to enhanced performance for the turntable’s spatial orientation using gyros/star sensor integration.

Active Impulsive Noise Control with Missing Input Data Based on FxImdMCC Algorithm

In this study, we address the challenge of noise reduction in environments characterized by impulsive noise and missing input data in active noise control (ANC) systems, where existing algorithms often fail to deliver satisfactory results. Background noise, especially impulsive noise, poses a significant obstacle to signal processing and noise reduction. Furthermore, data loss during transmission or acquisition further complicates the noise reduction task. In this paper, a filtered-x imputation of the missing data maximum correntropy criterion (FxImdMCC) algorithm is proposed based on an imputation model, least mean square, and the maximum correntropy criterion (MCC), which can effectively reduce the impact of outliers and missing input data. The simulation results demonstrate the efficacy of the proposed FxImdMCC algorithm, which significantly outperforms existing algorithms in the context of active impulsive noise control.

Credibility-based multi-sensor fusion for non-Gaussian conversion error mitigation

In a complex environment, a multi-sensor fusion algorithm can compensate for the limitations of a single sensor’s performance. In a distributed fusion algorithm, sensors need to transmit local estimates to a central coordinate system, and the existence of coordinate transformation uncertainty can undermine the performance of data transmission. Therefore, this paper proposes a multi-sensor distributed fusion method based on trustworthiness. Firstly, considering the presence of non-Gaussian conversion errors, a credibility-based multi-sensor fusion framework is constructed. Secondly, to address the difficulty in estimating conversion errors when measurement errors follow a non-Gaussian distribution, an optimization model is constructed based on actual measurement information to estimate the distribution of non-Gaussian conversion errors. Then, in response to the non-linear and non-Gaussian characteristics of the target optimization function, a particle swarm optimization algorithm based on trustworthiness adaptive weights is proposed to estimate the coordinate transformation errors. Finally, given the inconsistency in local estimates due to missing sensor measurements or significant errors in a non-Gaussian complex environment, a maximum correntropy consensus algorithm is proposed to avoid the trustworthiness calculation being affected by the current measurement errors, thereby improving the accuracy of the global estimation.

Euclidean direction search algorithm with maximum correntropy criterion for active noise control system

Euclidean direction search algorithm with maximum correntropy criterion for active noise control system

Applications of Maximum Correntropy Criterion to Power System Problems

Applications of Maximum Correntropy Criterion to Power System Problems

Robust Maximum Correntropy Kalman Filter

ABSTRACTThe Kalman filter provides an optimal estimation for a linear system with Gaussian noise. However, when the noises are non‐Gaussian in nature, its performance deteriorates rapidly. For non‐Gaussian noises, maximum correntropy Kalman filter (MCKF) is developed which provides a more accurate result. In a scenario, where the actual system model differs from nominal consideration, the performance of the MCKF degrades. For such cases, in this article, we have proposed a new robust filtering technique for a linear system which maximizes a cost function defined by exponential of weighted past and present errors weighted with the kernel bandwidth. During filtering, at each time step, the kernel bandwidth is selected by maximizing the correntropy function of error. Further, a convergence condition of the proposed algorithm is derived. Numerical examples are presented to show the usefulness of the proposed filtering technique.

Design a novel algorithm for enhancing UWB positioning accuracy in GPS denied environments

Accurate indoor positioning is the key to the development of the Internet of Things and intelligent devices. In view of GPS-denied indoor environments, we propose to build the indoor local positioning system by using ultra-wide band (UWB) system. In order to enhance the localization accuracy of UWB system, we propose a novel algorithm which integrates the Maximum Correntropy Criterion (MCC) and unscented Kalman filter (UKF) method to reconstruct the measurement distance by using the maximum entropy principle to reduce the influence of outliers and unknown process noise on the smooth effect. Subsequently, the least square (LS) method is implemented to attain the target node (TN) initial position coordinates, and the Taylor algorithm is then performed to further optimize the localization results of the LS method. Lastly, the experimental investigation is conducted to assess the effectiveness and applicability of the developed method via the UWB system in indoor scenarios. The experimental outcomes demonstrate that the developed MCCUKF-LS method can achieve the lowest root mean square error (RMSE), and enhance the positioning accuracy of the TN compared with the LS, KF-LS, and UKF-LS methods. The overall average RMSE of MCCUKF-LS method is reduced by 45.7% contracted with the LS algorithm. The average error of x-, y- and z-axis orientation for the LS method is reduced from 0.074 m, 0.067 m, 0.098 m to 0.036 m, 0.034 m, 0.044 m, and the achieved accuracy in the orientation of the three axes is increased by 51.4%, 49.3% and 55.1% respectively, which reveals that the designed fusion technique is capable of enhancing the positioning accuracy of the TN effectively, providing a new positioning methodology and reference for indoor positioning in GPS-denied environments.

Study on vehicle state and tire–road friction coefficient estimation based on maximum correntropy generalized high-degree cubature Kalman filter

To cope with the challenges of inadequate precision and weak robustness of tire–road friction coefficient (TRFC) and vehicle state estimation under mixed Gaussian noise circumstances, this research puts forward a method combining maximum correntropy criterion (MCC) and generalized high-degree cubature Kalman filter (GHCKF). A coupled lateral and longitudinal vehicle model and the Dugoff tire model are established. The vehicle mass is estimated using the recursive least squares approach based on a forgetting factor (FFRLS). Low-cost onboard sensors are utilized to design observers for vehicle state and TRFC. The observers’ effectiveness is tested by Carsim/Simulink co-simulation tests under the conditions of sine steering input and steering wheel angle step input. The research results indicate that in handling mixed Gaussian noise environments, maximum correntropy generalized high-degree cubature Kalman filter (MCGHCKF) method outperforms maximum correntropy cubature Kalman filter (MCCKF), GHCKF, and cubature Kalman filter (CKF) methods concerning robustness and estimation accuracy, providing stable and reliable support for systems of vehicle active safety control.

Robust sensor selection based on maximum correntropy criterion for ocean data reconstruction

Robust sensor selection based on maximum correntropy criterion for ocean data reconstruction

Dual-Triggered Distributed Maximum Correntropy Kalman Filter With Measurement Noise Variance Compensation for Sensor Networks

Dual-Triggered Distributed Maximum Correntropy Kalman Filter With Measurement Noise Variance Compensation for Sensor Networks

Maximum Correntropy Criterion With Variable Center for NLOS Mitigation in Elliptic Positioning

Maximum Correntropy Criterion With Variable Center for NLOS Mitigation in Elliptic Positioning

Diffusion Filtered-x Inverse Tangent Generalized Maximum Correntropy Criterion with Reduced Complexity for Distributed Active Noise Control

Diffusion Filtered-x Inverse Tangent Generalized Maximum Correntropy Criterion with Reduced Complexity for Distributed Active Noise Control

Improved Maximum Correntropy Criterion Kalman Filter with Adaptive Behaviors for INS/UWB Fusion Positioning Algorithm

Accurate indoor robot navigation cannot be achieved without reliable indoor positioning techniques. Ultra-wideband (UWB) is among the most dependable techniques currently available. However, the complexity of indoor environments results in signal transmissions that are susceptible to interference from obstacles, which in turn reduces positioning accuracy. Inertial Navigation System (INS) is an autonomous navigation system that is free from interference in indoor environments and unaffected by non-line-of-sight (NLOS) conditions. This paper proposes a new joint INS and UWB positioning method utilizing the Maximum Correntropy Criterion Kalman Filter (MCCKF). This approach effectively cope with the interference of measurement outliers and extend the design of the adaptive mechanisms to enhance the performance of the localization system. For UWB positioning of tag nodes, an improved Particle Swarm Optimization combined with kmeans (PSO-kmeans) method is used to reduce the impact of NLOS errors on positioning. Finally, the INS is calibrated by AMCCKF fused positioning results. The results of simulations and experiments demonstrate that the proposed AMCCKF fusion algorithm effectively suppresses the impact of anomalous measurements, enhances positioning accuracy and robustness, thereby improving its practicality in real-world environments.

Rational-quadratic kernel-based maximum correntropy Kalman filter for the non-Gaussian noises

In this paper, a rational-quadratic kernel-based maximum correntropy Kalman filter (RKMCKF) algorithm is proposed to improve the estimation accuracy for non-Gaussian noise interference. Firstly, the RKMCKF algorithm is derived to eliminate the singular matrix produced by multi-dimensional non-Gaussian noise disturbance. Secondly, the upper limit is analyzed to provide a theoretical range for kernel bandwidth, which is beneficial for the selection of proper kernel bandwidths and boosting the precision of state estimation. Furthermore, the boundness of the state estimation error is verified to manifest the RKMCKF algorithm stability. Finally, under different types of non-Gaussian noise, the proposed RKMCKF algorithm is demonstrated to promote the accuracy of state estimation compared with the conventional Kalman filter, Gaussian sum filter, Huber filter, and maximum correntropy Kalman filter through the simulations.

Data-driven decision making for manufacturing processes using pyramidal dilation attention convolutional neural network optimized with improved dwarf mongoose optimization

In this paper, data-driven decision making for manufacturing processes using Pyramidal Dilation Attention Convolutional Neural Network Optimized with Improved Dwarf Mongoose Optimization (DDMP-PDACNN-IDMO) is proposed. Initially data is taken from the SECOM dataset. Afterward the data is fed to pre-processing. In pre-processing, it removes noisy data using Variational Bayesian-based maximum Correntropy Cubature Kalman Filtering (VBMCCKF). The pre-processed data is given to feature selection. Here, representative features is selected based on the Lirebird Optimization Algorithm (LOA). Next, in order to successfully categorize the manufacturing process as successful or unsuccessful, the chosen feature is given to the Pyramidal Dilation Attention Convolutional Neural Network (PDACNN). The PDACNN’s learnable parameters are optimized using Improved Dwarf Mongoose Optimization (IDMO). Python is used for implementing the proposed method. The performance of the proposed technique was examined using performance metrics such as accuracy, and ROC. The performance analysis highlights that the proposed DDMP-PDACNN-IDMO model excels with 98.5% accuracy in the success class and 98.2% in the failure class. The proposed method outperforms DDMP-ANN, DDMP-RNN, and DDMP-BPNN with the fastest runtime (best: 9.4s) and lowest Mean Squared Error (MSE) for both training and testing (best: 0.0392 and 0.0526, respectively). It also achieves the highest R-squared (R2) values, indicating more accurate and reliable predictions. These results confirm the proposed method’s superior performance in speed and accuracy compared with other existing methods such as data-driven manufacturing process based artificial neural network (DDMP-ANN), data-driven manufacturing process based recurrent neural network (DDMP-RNN), and data-driven manufacturing process based back propagation neural network (DDMP-BPNN) respectively.

A Novel Low-Cost UWB/IMU Positioning Method With the Robust Unscented Kalman Filter Based on Maximum Correntropy

A Novel Low-Cost UWB/IMU Positioning Method With the Robust Unscented Kalman Filter Based on Maximum Correntropy

Maximum correntropy polynomial chaos Kalman filter for underwater navigation

This paper develops an underwater navigation solution that utilizes a strapdown inertial navigation system (SINS) and fuses a set of auxiliary sensors such as an acoustic positioning system, Doppler velocity log, depth meter, and magnetometer to accurately estimate an underwater vessel's position and orientation. The conventional integrated navigation system assumes Gaussian measurement noise, while in reality, the noises are non-Gaussian, particularly contaminated by heavy-tailed impulsive noises. To address this issue, and to fuse the system model with the acquired sensor measurements efficiently, we develop a square root polynomial chaos Kalman filter based on maximum correntropy criteria. The proposed method uses Hermite polynomial chaos expansion to tackle the nonlinearity, and it has the potential to estimate the states in a more accurate way in presence of a non-Gaussian measurement noise. The filter is initialized using acoustic beaconing to accurately locate the initial position of the vehicle. The computational complexity of the proposed filter is calculated in terms of flops count. The proposed method is compared with the existing maximum correntropy sigma point filters in terms of estimation accuracy and computational complexity. It is found from the simulation results that the proposed method is more accurate compared to the conventional deterministic sample point filters and Huber's M-estimator.

Iterative UKF under generalized maximum correntropy criterion for intermittent observation systems with complex non-Gaussian noise

The traditional unscented Kalman filters (UKFs) under the maximum correntropy criterion provide a powerful tool for nonlinear state estimation with heavy-tailed non-Gaussian noise. Nevertheless, the above-mentioned filters may yield biased estimates because the Gaussian kernel function can only handle certain types of non-Gaussian noise. Additionally, the use of statistical linearization methods can result in approximation errors when solving linear observation equations, while the system may also experience observation data loss. Therefore, a new iterative UKF with intermittent observations under the generalized maximum correntropy criterion is proposed for systems with complex non-Gaussian noise, called GMCC-IO-IUKF. Firstly, the connection between the UKF with and without intermittent observations is established by designing a coefficient matrix including intermittent observation variables, so as to derive the UKF with intermittent observations under the maximum correntropy criterion. Secondly, for the measurement update of GMCC-IO-IUKF, a nonlinear regression augmented model that can deal with both prediction and observation errors is established using the coefficient matrix and the nonlinear function. To better adapt to different types of non-Gaussian noise, the generalized Gaussian kernel function is substituted for the traditional Gaussian kernel function. Theoretically, GMCC-IO-IUKF can achieve better estimation performance by directly employing the nonlinear function and the latest iteration value. Finally, a classical target tracking model is used to evaluate the estimation performance and feasibility of our proposed GMCC-IO-IUKF algorithm. It appears from the experiment results that our proposed GMCC-IO-IUKF can not only promote estimation precision but also handle complex non-Gaussian noise flexibly.

Maximum Correntropy Extended Kalman Filtering with Nonlinear Regression Technique for GPS Navigation

Maximum Correntropy Extended Kalman Filtering with Nonlinear Regression Technique for GPS Navigation