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This study presents a novel navigation method designed to support a real-time, efficient, accurate indoor localisation for mobile robot system. It is applicable for inertial measurement units (IMU) consisting of gyroscopes, accelerometers, and magnetic besides stereo vision (SV). The current indoor mobile robot localisation technology adopts traditional active sensing devices such as laser, and ultrasonic method which belongs to the signal of localisation and navigation method which has low efficiency complex structure, and poor anti-interference ability. Through dual Kalman filter (DKF) algorithm, the accumulated error of gyroscope can be reduced, while combining with SV, mobile robot binocular SV orientation of inertial location can be realised under the DKF mechanism, which is introduced. First, high precision posture information of mobile robot can be obtained using fusing Kalman filter algorithm of accelerometer, gyroscope and magnetometer data. Second, inertial measurement precision can be optimised using Kalman filtering algorithm combined with machine vision localisation algorithm. The results indicate that the method achieves the levels of accuracy location comparable with that of the IMU/SV fusion algorithm; <0.0066 static RMS error, <0.0056 dynamic RMS error. The mobile robot using DKF algorithm of inertial navigation and SV indoor localisation is feasible.

The battery management system (BMS) is a crucial component of electric vehicles (EVs) owing to its sustainable operation. To ensure optimal performance of the BMS, state of charge (SOC) of the equipped battery is required to be effectively and accurately estimated. In this paper, the authors consider high‐order equivalent circuit model (ECM) to capture the dynamic characteristics of lithium‐ion batteries, which are connected in series with internal resistance by 2‐RC networks. The parameters of the RC network are determined by mathematically solving the working conditions of the two states. Moreover, the parameters of the battery can be derived by hybrid pulse power characterization (HPPC) tests. Then, based on the open‐circuit voltage, the proposed feedback‐based extended Kalman filtering (FEKF) algorithm is established. The parameters from the simulation have shown that the highest error is 0.0306 V, the optimal knowledge of which can improve the SOC estimation approach remarkably and can provide a reference value. Afterwards, the non‐linear predicting and corrective techniques are applied to the experiment in the extended calculation process. The original error is reduced by the FEKF algorithm, where the maximum and average errors are 0.0298 and 0.0240 V, respectively. Consequently, the established high‐order ECM utilizing the FEKF algorithm may provide SOC estimation with an error of 1.5% or less, resulting in superb performance from the lithium‐ion battery pack.

With the arrival of aging society and the development of modern agriculture, the use of agricultural robots for large-scale agricultural production activities will become a major trend in the future. Therefore, it is necessary to develop suitable robots and autonomous navigation technology for agricultural production. However, there is still a problem of external noise and other factors causing the failure of the navigation system. To solve this problem, we propose an agricultural scene-based multi-sensor fusion method via a loosely coupled extended Kalman filter algorithm to reduce interference from external environment. Specifically, the proposed method fuses inertial measurement unit (IMU), robot odometer (ODOM), global navigation and positioning system (GPS), and visual inertial odometry (VIO), and uses visualization tools to simulate and analyze the robot trajectory and error. In experiments, we verify the high accuracy and the robustness of the proposed algorithm when sensors fail. The experimental results show that the proposed algorithm has better accuracy and robustness on the agricultural dataset than other algorithms.

Accurate prediction of the State of Charge (SOC) of lithium-ion batteries is the foundation for the stable and efficient operation of battery management systems. This paper proposes a lithium-ion battery SOC estimation method based on the Dung Beetle Optimizer (DBO), optimizing the second-order Kalman filter algorithm (DBO-DKF). Leveraging the DBO’s fast convergence speed and strong global search capability, this method optimizes the Kalman filter algorithm in the parameter identification stage and the extended Kalman filter algorithm in the SOC estimation stage to address the issue of insufficient estimation accuracy caused by noise covariance matrices of input current and voltage measurements. Through the discharge of current tests under complex conditions, as well as comparing and analyzing credibility indicators such as MAE, RMSE, and MSE as measures of estimation accuracy, it can be verified that the proposed method effectively enhances SOC estimation accuracy.

Aiming at the problems of low estimation accuracy and narrow application range of sensorless control caused by inverter nonlinearity and motor parameter error, this paper studies a sensorless control technology of permanent magnet synchronous motor based on an interactive multi-model extended Kalman filter algorithm to realize high-precision and high-performance sensorless control of a permanent-magnet synchronous motor. Firstly, considering the influence of inverter nonlinearity, the mathematical model of PMSM including inverter disturbance voltage is established. Secondly, an interactive multi-model extended Kalman filter observer is designed based on this model to achieve high-precision sensorless control of PMSM. Thirdly, the nonlinear disturbance voltage of the inverter is fed back to the control system for dead-time compensation, thus eliminating the voltage disturbance caused by the dead-time effect. Finally, simulation experiments and dual-motor towing experiments demonstrate the efficacy of the interactive multi-model extended Kalman filter sensorless control algorithm in mitigating the effects of dead time. The results indicate that the proposed algorithm exhibits high precision in speed and angle estimation, robust anti-disturbance capabilities, and excellent overall performance.

The estimation of the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries is very important for the battery management system (BMS) and the analysis of the causes of equipment failures. Aiming at many problems such as the changes in the parameters of the lithium battery model and the accurate estimation of the SOC and SOE, this paper proposes a joint algorithm of forgetting factor recursive least square (FFRLS) and adaptive square root unscented Kalman filter (ASRUKF) based on the second-order RC equivalent circuit model. In this paper, the joint FFRLS-ASRUKF algorithm is used to perform simulation experiments under three different working conditions of HPPC, DST, and BBDST at different temperatures of 25, 15, and 5 °C. And a current ± 1 A offset is added as a disturbance to verify the robustness of ASRUKF. The results show that under HPPC working condition, the RMSE, MAE, and MAPE estimated by ASRUKF for SOC and SOE of lithium-ion batteries at three temperatures do not exceed 0.0016, 0.0012, and 0.43%, respectively. Under DST working condition, ASRUKF estimates that RMSE, MAE, and MAPE of SOC and SOE of lithium-ion batteries at three different temperatures do not exceed 0.0013, 0.0009, and 0.70% respectively. Under BBDST operating conditions, ASRUKF estimates that the RMSE, MAE, and MAPE of the SOC and SOE of lithium-ion batteries at three different temperatures do not exceed 0.0016, 0.0009, and 0.71% respectively. After adding the current offset, ASRUKF can still accurately estimate the SOC and SOE of lithium-ion batteries.

As the scale of data becomes larger and larger, clustering processing, a key step in data mining, has important practical significance. Aiming at the problems of time consumption and high clustering errors when the current clustering algorithms deal with massive and dynamic big data, an incremental clustering algorithm is proposed by taking big data as the research object. By exploring the attribute characteristics of big data, four characteristics such as scale, diversity, high speed and value are summarised. For large-scale data streams that have multiple attributes and are acquired one by one, optimise the setting method of the K-means clustering algorithm category centre point, combine the K-means clustering algorithm and the Kalman filter algorithm and measure the distance between data point pairs. Instead of Mahalanobis distance, an incremental clustering algorithm suitable for big data is constructed. Five data sets are selected to carry out example analysis. The results of the algorithm are verified by the algorithm. The proposed algorithm has obvious advantages in the incremental clustering effect of big data. At the same time, it also has efficient and stable computing performance, which meets the expected design requirements and goals.

This study addresses the high-precision positioning requirements of the BeiDou Navigation System (BDS) by focusing on the commonly adopted BDS/Inertial Navigation System integrated navigation mode. A novel Spherical Simplex Unscented Kalman Filter (SSUKF) algorithm is proposed, featuring an improved sigma-point sampling strategy that enhances filtering accuracy while reducing computational overhead. In parallel, the Time Difference of Arrival (TDOA) method is combined with the Firefly Algorithm (FA) to optimize a Radial Basis Function (RBF) neural network, further enhancing positioning precision. Evaluation is conducted using an Ultra-Wideband TDOA dataset. Results show that the SSUKF algorithm significantly reduces positioning error. Specifically, the root means square error (RMSE) achieved by SSUKF is 0.1614 m-a reduction of 62.2% compared to the Extended Kalman Filter and 52.1% compared to the Unscented Kalman Filter. When integrated with the FA-optimized RBF neural network, the hybrid SSUKF-FA-RBF model achieves an RMSE of 0.127 m under high-noise conditions, demonstrating strong robustness and accuracy. In addition to its accuracy, the SSUKF algorithm offers improved computational efficiency, making it suitable for real-time, high-precision applications. Error analysis confirms the robustness and stability of the SSUKF-FA-RBF model across various environments. Under zero standard deviation noise, the model achieves 96.4% accuracy, 95.6% precision, and a 96.1% recall ratesubstantially outperforming comparative models. This study contributes an enhanced Kalman filtering method and an optimized positioning framework, advancing both accuracy and computational efficiency for the BDS. The proposed approach offers effective technical support for a wide range of high-precision positioning applications.

This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic systems. To deal with the challenges of extensive sparsity, we resort to the compressed sensing method and propose a compressed Kalman filter (KF) algorithm. Our algorithm first compresses the original high-dimensional sparse regression vector via the sensing matrix and then obtains a KF estimate in the compressed low-dimensional space. Subsequently, the original high-dimensional sparse signals can be well recovered by a reconstruction technique. To ensure stability and establish upper bounds on the estimation errors, we introduce a compressed excitation condition without imposing independence or stationarity on the system signal, and therefore suitable for feedback systems. We further present the performance of the compressed KF algorithm. Specifically, we show that the mean square compressed tracking error matrix can be approximately calculated by a linear deterministic difference matrix equation, which can be readily evaluated, analyzed, and optimized. Finally, a numerical example demonstrates that our algorithm outperforms the standard uncompressed KF algorithm and other compressed algorithms for estimating high-dimensional sparse signals.

The core focus of the battery management system (BMS) is accurate state of charge (SOC) estimation of the lithium-ion batteries. To solve the problem of improper selection of the noise covariance matrix in the extended Kalman filter (EKF) algorithm, which in turn affects the actual operating effect and range of electric vehicles, this paper proposes the adaptive sine cosine–Levy flight–quantum particle swarm optimization (ASL-QPSO) algorithm to find the optimal noise covariance matrix. Firstly, this paper proposes the variable forgetting factor recursive least square (VFFRLS) algorithm to identify the parameters of the equivalent circuit model of the power lithium-ion batteries. Then, the obtained parameters are transmitted online by the EKF algorithm, based on which the local attraction factor is updated using the ASL-QPSO, which is used to select the appropriate noise covariance matrix. Finally, the optimized noise covariance matrix is obtained and used to achieve the accurate SOC estimation of the power lithium-ion batteries. Experimental results under different operating conditions and temperatures show that the maximum absolute error (MAX), mean absolute error (MAE), and root mean square error (RMSE) of the algorithm are less than 1.82%, 0.59%, and 0.72%, respectively. This demonstrates that the algorithm has superior convergence tuning and high robustness, presenting a novel optimization strategy for the SOC estimation of lithium-ion batteries.