Research on Invalid Target Filtering and Target Tracking Algorithm Optimization in Millimeter-Wave Radar Technology

This paper studies the relay selection in wireless cooperative communication systems. Since the high mobility of Unmanned Aerial Vehicles (UAVs) will lead to the change of link state, a relay selection algorithm based on position prediction is proposed in this paper to better study this problem. First, the Unscented Kalman Filter (UKF) algorithm is used to predict the real-time location information of each UAV, and based on the location information, it is determined whether the current UAV relay node needs to be replaced. The real-time location information of each UAV is predicted by UKF algorithm, which is the basis for determining whether the current UAV relay node needs to be replaced. Then, the outage probability of each standby UAV relay node is calculated, and the standby UAV relay node is sorted according to the link outage probability. Finally, the greedy algorithm is used to find the node with the smallest interruption probability among the backup UAV relay nodes as the next UAV relay node. The simulation results show that in terms of position prediction, the UKF algorithm is more accurate than the Kalman Filtering (KF) algorithm, and the error is smaller. In terms of interruption probability, the relay selection algorithm proposed in this paper reduces the interruption probability of the link, and the interruption probability of the link is more stable.

Traditional unscented Kalman filtering (UKF) cannot solve the filtering problem for nonlinear systems with colored measurement noises and one-step randomly delayed measurements. To fix this problem, a new UKF algorithm is proposed in this paper. First, a system model with one-step randomly delayed measurements and colored measurement noises is established, wherein a first order Markov sequence model for whitening colored noises and an independently identical distributed Bernoulli variable for modeling one-step randomly delayed measurements is introduced. Second, an UKF is proposed for the above established models through unscented transformation by calculating the nonlinear states posterior mean and covariance based on the Bayesian filter framework. Specially, the proportional symmetric sampling method is used in the new UKF algorithm. Finally, the effectiveness and superiority of the proposed method is verified via simulation.

The accurate estimation of battery state of charge (SOC) enables the reliable and safe operation of lithium‐ion batteries. Data‐driven SOC estimation is considered an emerging and effective solution. However, existing data‐driven SOC estimation methods typically involve direct estimation and lack effective feedback correction. Moreover, battery degradation poses additional challenges to accurate SOC estimation. Therefore, this study proposes an adaptive combined method for battery SOC estimation based on a long short‐term memory (LSTM) network and unscented Kalman filter (UKF) algorithm considering battery aging status. First, an LSTM model is constructed to characterize the battery's dynamic performance instead of traditional battery models. Then, the UKF algorithm is employed to perform SOC estimation through the feedback of terminal voltage prediction. To enhance estimation accuracy under different aging statuses, a proportional‐integral‐derivative controller is employed to correct the capacity fading during the SOC estimation process. Validation results indicate that the terminal voltage prediction model demonstrates exceptional robustness against interference from current and voltage noise. Compared to the traditional estimation method combining the deep learning model and Kalman filter algorithm, the proposed method demonstrates superior estimation accuracy under various complex operating conditions. Furthermore, the proposed method outperforms the traditional method in estimation performance during battery aging.

GPS disciplined crystal oscillator technology has been widely used to obtain high-precision and low-cost frequency standards. But there are two factors that would affect the accuracy of the frequency correction. The first is the random error and outliers contained in the one pulse per second (PPS) outputted by the GPS. The second factor is the frequency nonlinear drift of crystal oscillator because of the aging and the temperature. To solve these problems, a mathematical model of the frequency difference between IPPS signal and crystal oscillator signal is established in this paper. And then a novel high-precision method based on the sequence weighted adaptive Unscented Kalman filter (UKF) algorithm is proposed on the basis of the mathematical model. To be more specific, the UKF is used to address the problem of random error and frequency nonlinear drift. And the effect of outliers is eliminated by weighing sequence in the UKF algorithm. The adaptive filtering technology can reduce the impact of the variable and random system noise. Numerical simulation and verification are described. The results show that the proposed method can make frequency standards higher stability and precision.

Numerical simulation of a new early gas kick detection method using UKF estimation and GLRT

State-of-charge (SOC) estimation of lithium-ion batteries (LIBs) is the basis of other state estimations. However, its accuracy can be affected by many factors, such as temperature and ageing. To handle this bottleneck issue, we here propose a joint SOC-SOH estimation method considering the influence of the temperature. It combines the Forgetting Factor Recursive Least Squares (FFRLS) algorithm, Total Least Squares (TLS) algorithm, and Unscented Kalman Filter (UKF) algorithm. First, the FFRLS algorithm is used to identify and update the parameters of the equivalent circuit model in real time under different battery ageing degrees. Then, the TLS algorithm is used to estimate the battery SOH to improve the prior estimation accuracy of SOC. Next, the SOC is calculated by the UKF algorithm, and finally, a more accurate SOH can be obtained according to the UKF-based SOC trajectory. The battery-in-the-loop experiments are utilized to verify the proposed algorithm. For the cases of temperature change up to 35 °C and capacity decay up to 10%, our joint estimator can achieve ultra-low errors, bounded by 2%, respectively, for SOH and SOC. The proposed method paves the way for the advancement of battery use in applications, such as electric vehicles and microgrid applications.

Accurate remaining useful life (RUL) prediction under the noisy environment is a big challenge for the health management of modern industrial systems since the extraction of the accurate data structure from heavily corrupted data is difficult. In recent years, the kernel adaptive filter (KAF) has been widely adopted to solve the robust regression problem due to its low-complexity and high-approximation capability and robustness while the applications in battery RUL prediction are still few and far between. Thus, this paper is concerned with long-term RUL prediction using the KAF method. At first, concretely speaking, a robust KAF algorithm is derived based on the double-Gaussian-mixture (DGM) cost function, which is used to learn the capacity degradation mechanism from contaminated capacity data and so as to build the long-term prediction model. Second, a robust unscented Kalman filter (UKF) algorithm employing the DGM-based cost function is developed, which is then combined with the KAF-based prediction model to realize a more accurate and reliable prediction. Under the hybrid prognostic framework, the proposed UKF algorithm is applied to filter the noisy observations. When the observation data are inaccessible, the predicted data from the off-line trained KAF-based prediction model are adopted as the approximated value of the real observations for the UKF algorithm to optimize the prediction results and to provide the uncertainty representation. The experimental results reveal that the proposed method has great robustness when the measurements contain noise and large outliers, which makes it possible to get satisfactory prediction performance without preprocessing the data manually.

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

State of charge (SOC) of the lithium-ion battery is an important parameter of the battery management system (BMS), which plays an important role in the safe operation of electric vehicles. When existing unknown or inaccurate noise statistics of the system, the traditional unscented Kalman filter (UKF) may fail to estimate SOC due to the non-positive error covariance of the state vector, and the SOC estimation accuracy is not high. Therefore, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed to solve this problem. The IAUKF is composed of the improved unscented Kalman filter (IUKF) that is able to suppress the non-positive definiteness of error covariance and Sage–Husa adaptive filter. The IAUKF can improve the SOC estimation stability and can improve the SOC estimation accuracy by estimating and correcting the system noise statistics adaptively. The IAUKF is verified under the federal urban driving schedule test, and the SOC estimation results are compared with IUKF and UKF. The experimental results show that the IAUKF has higher estimation accuracy and stability, which verifies the effectiveness of the proposed method.

In this paper, a state estimation method of distributed electric drive articulated vehicle dynamics parameters based on the forgetting factor unscented Kalman filter with singular value decomposition (SVD-UKF) is proposed. The 7DOF nonlinear dynamics model of a distributed electric drive articulated vehicle is established. The unscented Kalman filter algorithm is the foundation, with singular value decomposition replacing the Cholesky decomposition. A forgetting factor is introduced to dynamically adapt the observation noise covariance matrix in real time, resulting in a centralized parameter state estimator for the articulated vehicle. The proposed parameter state estimation method based on the forgetting factor SVD-UKF is simulated and compared with the unscented Kalman filter (UKF) estimation method. Key dynamic parameters are estimated, such as the lateral and longitudinal velocities and accelerations, angular velocity, articulated angle, wheel speeds, and longitudinal and lateral tire forces of both the front and rear vehicle bodies. The results show that the proposed forgetting factor SVD-UKF method outperforms the traditional UKF method. Furthermore, a prototype vehicle test is conducted to compare the estimated values of various dynamic parameters with the actual values, demonstrating minimal errors. This verifies the effectiveness of the proposed dynamic parameter estimation method for articulated vehicles.

The state of charge (SOC) of the battery is an index set to measure the leftover capacity of the battery. Its scientific estimation plays a vital role in ensuring the secure operation and efficient control of the battery. To enhance the certainty of battery SOC estimation, this study proposes a SOC estimation approach that blends the unscented Kalman filter (UKF) and the crisscross optimization algorithm (CSO) based on a second-order RC network equal battery model. Building upon hybrid pulse power characteristic (HPPC) testing and parameter estimation using the least squares method, the battery SOC is estimated using the urban dynamometer driving schedule (UDDS) test data and the UKF algorithm. Different from the extended Kalman filter (EKF), the UKF algorithm avoids the loss of multi-system nonlinearity during the linearization process through the Gaussian transformation based on sigma points. The CSO algorithm is utilized to calculate the noise in the battery model estimation by considering the battery charge-discharge voltage, current, and estimation results. By excluding the estimation error from the original estimation statistics, more accurate estimation statistics are obtained. Experimental analysis is conducted by comparing the proposed CSO-UKF algorithm with the UKF algorithm and the PSO-UKF algorithm, using battery charge-discharge data collected under the UDDS operating condition with a battery charge-discharge test instrument. The experimental statistics endorse the effectiveness and trustworthiness of the proposed algorithm.

In view of abnormal situations, such as multimodal noise, bad measurement data, and sudden load changes in a ship integrated electric propulsion system, this article proposes an unscented Kalman filter with enhanced mixture minimum error entropy (EnMMEE-UKF) to estimate the dynamic state of a ship integrated electric propulsion system. The new algorithm mixture includes two Gaussian functions, defines them as kernel functions, and uses quantization operators to lighten the weight of the mixture minimum error entropy (MMEE). This not only reduces the calculation burden of the MMEE but also effectively improves the system’s ability to handle strongly nonlinear models. In addition, in the lightweight MMEE (LMMEE)-unscented Kalman filter (UKF) algorithm, a fading factor is introduced, based on the strong tracking filter (STF) theory, to modify the error covariance matrix. The enhanced MMEE replaces the mean square error loss of the traditional UKF algorithm. This enhances the robustness of the algorithm estimation, in which a statistical linearization method is adopted to merge the state and measurement errors into the cost function of the enhanced MMEE. Finally, a fixed-point iteration method is applied to recursively update the posterior estimate and covariance matrix. To verify the effectiveness of the proposed method, numerical simulation experiments of various scenarios are conducted on the simulation system and the prototype system. The results demonstrate that the proposed algorithm not only has high estimation accuracy under various abnormal conditions but also effectively reduces the computational complexity of the algorithm, making its application in engineering possible.

Constrained nonlinear state estimation based on the UKF approach

State estimation of induction machines may be a difficult problem, due to the non-linear behavior of theirs. For non-linear state estimation, the unscented Kalman filter (UKF) is a well-known extension of the linear Kalman filter. Operation of the UKF algorithm strongly depends on the process and measurement noise covariance parameters of the estimator. Determination of these parameters is not straightforward and can be difficult, especially if the number of state variables and hence the system complexity is relatively high. In this paper, the UKF algorithm is applied for speed sensorless state estimation of induction machines in such a way that seven state variables are estimated from the measured stator currents and from the known excitation voltages. In order to tune the noise parameters of the UKF, a new, optimization-based method is presented. This tuning method provides adequate behavior for the observer beside difficult operating conditions as it has been shown by simulation experiment.

This paper describes a new nonlinear estimation procedure used to estimate and track the parameters of a nonlinear aircraft. The Unscented Kalman Filter (UKF) is developed and compared to the more traditional Extended Kalman Filter (EKF). State and parameters are estimated on the F-15 for both a complex maneuver and a maneuver with failure. The algorithms have access to the nonlinear dynamic equations, but not the aircraft engine models, aerodynamic models, or atmospheric models. Parameters describing these unknown dynamics are estimated in the EKF and UKF algorithms. Results show the UKF to be more accurate than the EKF, and track all parameters very well at all times, even after a 50% failure of the stabilator. The aerodynamic forces and moments, while difficult to track immediately after the failure because of the discontinuous nonlinearity, did recover quickly and stay within the predicted bounds.