Strong Tracking Kalman Filter Research Articles

An Improved Model Predictive Control to Enhance Voltage Performance for $LC$ Filtered Three-Level Inverters With Voltage Feedback Only

This article proposes a double vector model predictive control (MPC) method for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$LC$</tex-math></inline-formula> filtered three-level T-type inverter (3LT <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> I) to gain superior voltage performance, which is current sensorless and computationally efficient. In the proposed scheme, inverter voltage increment is induced in the multi-objectives cost function, and the desired voltage is derived based on the simplified discrete model, which optimizes the output voltage control performance. The prepositive prediction calculation is shifted out from the evaluation circle, and the computation is lessened simultaneously. Then, aiming to estimate the capacitor current of the simplified discrete model with voltage feedback only, the strong-tracking Kalman filter (STKF)-based observer is designed, which gets rid of the dependence on current sensors and lowers hardware cost. Furthermore, to improve the control accuracy of tracking the desired voltage, a two-layer decision algorithm based on the search tree is developed to find the optimal double vector easily, which obtains a low-complexity selection process using the univariate cost function. Finally, the simulation and experimental results are given to demonstrate the effectiveness of the theoretical analyses and the proposed method.

Hybrid Nonsingleton Fuzzy Strong Tracking Kalman Filtering for High Precision Photoelectric Tracking System

This article presents a hybrid nonsingleton fuzzy strong tracking Kalman filter (H-NFSTKF) for the high-precision photoelectric tracking system (PTS) to improve state estimation performance in the complex case of measurement noise change and velocity state mutation. The proposed H-NFSTKF is composed of three parts: A strong tracking Kalman filter (STKF) and two fuzzy logic systems (FLSs) including a singleton FLS (SFLS) and a nonsingleton FLS (NFLS). To compare different nonsingleton firing strength approaches, standard method (Sta-NS), centroid-based method (Cen-NS), similarity-based method (Sim-NS), and subsethood-based method (Sub-NS) are discussed. In addition, two traditional control strategies, i.e, dual closed-loop and feed-forward control, as well as four filtering methods including Kalman filter (KF), STKF, fuzzy STKF (FSTKF), and single nonsingleton FSTKF (S-NFSTKF) are also designed to show the superiority of the proposed H-NFSTKF. Finally, by means of comparative simulation analyses and experimental results, the excellence of the proposed H-NFSTKF is verified.

A novel interactive robust filter algorithm for GNSS/SINS integrated navigation

To solve the problem of Kalman filter (KF) performance degradation in unmanned aerial vehicle (UAV) applications, a novel interactive robust filter algorithm for GNSS/SINS integrated navigation is proposed in this paper. The strong tracking Kalman filter (STKF) is robust to uncertain system noise but is ineffective to abnormal measurement information. Based on the same performance index function with STKF, a measurement noise covariance matrix adaptive Kalman filter algorithm (MAKF) is presented, but it is ineffective under uncertain system noise. Furthermore, the interactive robust filter algorithm based on STKF and MAKF (IF-STKF-MAKF) is proposed, given the complementary characteristics of the above two filter algorithms. The STKF and MAKF operate in parallel based on the same system model. The filter probability of each filter is updated according to the likelihood function to perform output fusion and input interaction. The simulation and experiment results demonstrate that the IF-STKF-MAKF is effective and can achieve high estimation accuracy under both system noise anomalies and measurement information anomalies. In the vehicle experiment, the position accuracy of the proposed IF-STKF-MAKF method has been improved by more than 30% compared with KF, STKF, and MAKF. This method can also be extended to land vehicles, mobile robots, etc.

A modified adaptive Kalman filtering method for maneuvering target tracking of unmanned surface vehicles

The filtering methods are crucial for an unmanned surface vehicle (USV) to realize target tracking. Due to the poor observation caused by the strong vibration during the navigation of the USV, the target tracking accuracy of the traditional filtering method has been significantly degraded. A modified strong tracking-based expended Sage-Husa adaptive robust Kalman filter (MST-ESHARKF) algorithm is proposed to overcome this problem in this paper. In the proposed algorithm, a modified fading factor for the strong tracking Kalman filter is introduced to eliminate disturbance-induced filter divergence. In addition, the adaptive factor of robust Kalman filtering is designed to balance the predicted and observed states dedicated to improving the robustness of the algorithm. Finally, the biased and unbiased estimators for measurement and process noise covariances are merged, and the measurement noise covariance matrix’s interval is constrained, resulting in a simultaneous evaluation of measurement and process noise covariance matrices with improved dependability of the proposed algorithm. The simulation and experiment results show that the proposed MST-ESHARKF outperforms the existing filters in target tracking.

An improved strong tracking Kalman filter algorithm for real-time vehicle tracking

This paper deals with the improved strong tracking Kalman filter algorithm for vehicle tracking analysis. The proposed solution has an advantage in two ways, which are used for a location to be identified with the GPS (Global positioning system) and basic GSM with message setting. The structure two modules, the Sender side and Receiver side. The performance of improved strong tracking Kalman filter algorithm is analyzed for fast lane moving and obstacle detection with history notification. This paper also analyzed the real-time measurements of the GPS vehicle tracking system using the hardware set up using Arduino MEGA with SIM 808A. The vehicle position monitoring is also tested and the performance has been analyzed with Matlab simulation.

An Indoor Positioning and Tracking Algorithm Based on Angle-of-Arrival Using a Dual-Channel Array Antenna

The angular position measurement of an array antenna based on a wireless signal has high accuracy in an indoor no-occlusion environment. However, due to the high complexity of indoor environments, signal occlusion, multipath, and other interfering factors are inevitable when users move randomly, which can greatly reduce the positioning accuracy. In addition, different directions of the positioning source signal can also affect the positioning result. The switching wheels of the dual-polarization antenna array are collected in channel 1, the fast Fourier transform (FFT) is applied to the data of channel 2 to estimate the frequency offset, and the phase of the data is compensated. Using the FFT frequency offset estimation, the high-precision positioning of a single base station is realized using the dual-channel switch and dual-polarization antenna array in turn. Aiming at analyzing the affecting factors of the positioning system accuracy, the strong tracking kalman filter algorithm is studied. At the same time, the singular value decomposition of the covariance matrix is performed to improve the robustness of the strong tracking kalman filter, and the adaptive factor is introduced to improve the filtering accuracy. The proposed positioning algorithm can achieve the positioning accuracy within 1 m in the coverage area in a line-of-sight (LOS) environment, while the dynamic positioning accuracy within 1 m cannot be guaranteed in the coverage area in a non-line-of-sight (NLOS) environment. On this basis, the analysis of the static, rotational, and dynamic positioning accuracies of the source in the LOS and NLOS environments shows that the proposed singular value decomposition strong tracking kalman filter (SVD-STKF) algorithm can improve the overall positioning accuracy of the system by 0.03 m, and the maximum error in the LOS environment can be reduced by 0.08 m. The proposed SVD-STKF algorithm can correct the Hausdorff distance of dynamic positioning by up to 0.513 m in the NLOS environment where the system’s positioning accuracy decreases sharply due to the signal shielding. Also, it can make the positioning results smoother and achieve a good correction effect for the points far away from the true trajectory.

Contactless interactive control technology with a switching system based on Butterworth filter and modified strong tracking Kalman filter

In the human–computer interaction field, a contactless interaction with large screens through gestures is very representative, and the recognition and filtering of gesture images are very important tasks. Aiming at solving the problems of interference and positioning drift of three-dimensional lidar sensors, this article proposes a contactless interactive control system based on switching filtering algorithm, which selects the Butterworth filtering and the modified strong tracking Kalman filter to be used in the filtering process. The proposed interactive system extracts and optimizes user gestures, maps the gestures to the screen, simulates mouse operations, and enables operations such as selection, sliding, zooming in and out, and others. This switching filtering algorithm effectively solves the accuracy problem of a single filtering algorithm and the rapidity of complex filtering algorithms in the signal processing step, and greatly improves the interaction accuracy without sacrificing too much processing time. The experimental results show that by applying the proposed switching filtering algorithm to a contactless human–computer interaction system, the system can achieve smooth gesture interaction. The proposed system can perform real-time interaction with multiple people, which fully verifies the effectiveness and superiority of the proposed algorithm.

Data-driven identification and control based on optic tracking feedback for robotic systems

This paper presents the control of a robotic system based on a data-driven model. The components of the robotic system are a redundant robot and a motion capture system, both considered as a class of nonlinear discrete Multi-Input and Multi-Output system. The strong tracking Kalman filter algorithm approximates the Jacobian matrix of an equivalent model for the robotic system considering only the input and output on-line data. Moreover, a type of proportional controller based on the estimated Jacobian matrix for the robot’s end-effector is designed. The Lyapunov stability analysis guarantees the convergence of the equivalent model and the control law. The estimation and control approach are validated with thorough experimental results.

Mobile target localization and tracking techniques in harsh environment utilizing adaptive multi‐modal data fusion

Multi-source cooperative positioning systems relying on federated filtering have become attractive development directions of navigation strategy in real-time localization and tracking under challenging urban environment. However, local Kalman filters of traditional federated filtering may result in divergence when the system model or the measurements is inaccurate, and the fixed information distribution coefficient in federated filter cannot adaptively reflect the performance of each local filter. To improve the precision and robustness of the integrated navigation system, a novel adaptive federated strong tracking Kalman filter with dynamic fading factor mechanism for multi-sensor information fusion is proposed. Through iterative computation of the fading factor and updating the adaptive weight coefficients, strong tracking filter becomes robust to the uncertainty of system model. Meanwhile, an effective adaptive information distribution estimation algorithm based on the predicted residuals is constructed to balance the contributions of the kinematic model information and measurements on the state estimates. To ensure the stability of filtering, a simplified fusion strategy is established to solve the singular problem of the global covariance matrix of estimation error. Theoretical analysis and simulation results demonstrate the validity of the proposed approach in improving the accuracy and robustness of the integrated navigation and positioning systems. The proposed integrated multi-modal cooperative navigation and positioning algorithm will be of great significance to the implementation of real-time mobile target localization and tracking in harsh environment or dead zone.

Multi-Fault Diagnosis of Interacting Multiple Model Batteries Based on Low Inertia Noise Reduction

In view of the problems of declined estimation and diagnostic accuracy, as well as diagnosis delay caused by the fixed model transformation probability of the interacting multiple model (IMM) fault diagnosis algorithm, an IMM algorithm based on low inertia noise reduction (LN-IMM) was presented in this paper. The proposed algorithm realized the multi-fault diagnosis of lithium-ion batteries in combination with strong tracking Kalman filter (STKF). In the non-model transformation stage, a transition probability correction function was constructed using the difference of the n-th order based on model probability to suppress the effect of noise on the estimation accuracy of the algorithm. In the model transformation stage, a model jump threshold was introduced in order to reduce inertia when the matched model was switched and realized quick model transformation. Accordingly, the final experiment proved that the LN-IMM-STKF algorithm efficiently completed the state estimation of lithium-ion batteries and improved the diagnostic accuracy of faults while reducing diagnosis delay and attaining accurate detection as well as rapid separation of battery fault information.

A Carrier Tracking Loop Using Adaptive Strong Tracking Kalman Filter in GNSS Receivers

The Kalman filter (KF) has been widely used in the carrier track to improve the tracking performance of receivers under challenging environments. The KF-based tracking assumes that the noise statistics are exactly known in advance and kept fixed during the whole iteration process. However, the noise statistics are difficult to know accurately and the fixed noise statistics cannot reflect the practical situations under time-varying environments. To further enhance the performance of carrier tracking, the adaptive strong tracking Kalman filter (STKF) is proposed. The adaptive fading factors are employed in the state prediction covariance to adjust the Kalman gain. Moreover, to improve the accuracy of fading factors in STKF tracking, the measurement noise covariance is adjusted based on the $C/N_{0}$ estimations. In addition, the working state is checked, and fading factors are used only when the system is not steady. The proposed algorithm has been implemented in the software receiver. The test results demonstrate that the proposed method has more superior tracking performance under challenging environments than other tracking methods.

C/N0 Estimator Based on the Adaptive Strong Tracking Kalman Filter for GNSS Vector Receivers.

The carrier-to-noise ratio (C/N0) is an important indicator of the signal quality of global navigation satellite system receivers. In a vector receiver, estimating C/N0 using a signal amplitude Kalman filter is a typical method. However, the classical Kalman filter (CKF) has a significant estimation delay if the signal power levels change suddenly. In a weak signal environment, it is difficult to estimate the measurement noise for CKF correctly. This article proposes the use of the adaptive strong tracking Kalman filter (ASTKF) to estimate C/N0. The estimator was evaluated via simulation experiments and a static field test. The results demonstrate that the ASTKF C/N0 estimator can track abrupt variations in C/N0 and the method can estimate the weak signal C/N0 correctly. When C/N0 jumps, the ASTKF estimation method shows a significant advantage over the adaptive Kalman filter (AKF) method in terms of the time delay. Compared with the popular C/N0 algorithms, the narrow-to-wideband power ratio (NWPR) method, and the variance summing method (VSM), the ASTKF C/N0 estimator can adopt a shorter averaging time, which reduces the hysteresis of the estimation results.

An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer

The strap‐down inertial navigation system (SINS) is a commonly used sensor for autonomous underground navigation, which can be used for shearer positioning under a coal mine. During the process of initial alignment, inaccurate or time‐varying noise covariance matrices will significantly degrade the accuracy of the initial alignment of the shearer. To overcome the performance degradation of the existing initial alignment algorithm under complex underground environment, a novel adaptive filtering algorithm is proposed by the integration of the strong tracking Kalman filter and the sequential filter for the initial alignment of the shearer with complex underground environment. Compared with the traditional multiple fading factor strong tracking Kalman filter (MSTKF) method, the proposed MSTSKF algorithm integrates the advantage of strong tracking Kalman filter and sequential filter, and multiple fading factor and forgetting factor for east and north velocity measurement are designed in the algorithm, respectively, which can effectively weaken the coupling relationship between the different states and increase strong robustness against process uncertainties. The simulation and experiment results show that the proposed MSTSKF method has better initial alignment accuracy and robustness than existing strong tracking Kalman filter algorithm.

Robust GPS/INS/DVL Navigation and Positioning Method Using Adaptive Federated Strong Tracking Filter Based on Weighted Least Square Principle

Multi-sensor integrated positioning technique that combines complementary features of the global positioning system (GPS) and inertial navigation system (INS) for navigation in challenging urban environments has been a hot research area. A variety of algorithms have been proposed over the past two decades for this well-studied field. However, with the increasing demands of seamless positioning, traditional GPS/INS integrated technique faces rigorous challenges, especially in GPS-denied environment, where traditional techniques cannot be applied directly. To improve the precision and robustness of the navigation system, a novel hybrid GPS/INS/Doppler velocity log (DVL) positioning method is proposed, which introduces DVL as the reference information to assist the GPS module to correct the divergence error of INS. A new robust adaptive federated strong tracking Kalman filter (RAFSTKF) algorithm is also presented for data fusion, which has the advantage of robustness with respect to the uncertainty of the system model. Meanwhile, we introduce the least square principle and adaptively adjust information sharing factors to obtain the optimal estimation, which can improve the reliability of the overall system. The theoretical analysis and simulation results demonstrate the effectiveness of the proposed hybrid GPS/INS/DVL positioning method based on RAFSTKF. In addition, the tracking performance of the proposed method outperforms that of traditional federated Kalman filter.

An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems.

The cubature Kalman filter (CKF) is widely used in the application of GPS/INS integrated navigation systems. However, its performance may decline in accuracy and even diverge in the presence of process uncertainties. To solve the problem, a new algorithm named improved strong tracking seventh-degree spherical simplex-radial cubature Kalman filter (IST-7thSSRCKF) is proposed in this paper. In the proposed algorithm, the effect of process uncertainty is mitigated by using the improved strong tracking Kalman filter technique, in which the hypothesis testing method is adopted to identify the process uncertainty and the prior state estimate covariance in the CKF is further modified online according to the change in vehicle dynamics. In addition, a new seventh-degree spherical simplex-radial rule is employed to further improve the estimation accuracy of the strong tracking cubature Kalman filter. In this way, the proposed comprehensive algorithm integrates the advantage of 7thSSRCKF’s high accuracy and strong tracking filter’s strong robustness against process uncertainties. The GPS/INS integrated navigation problem with significant dynamic model errors is utilized to validate the performance of proposed IST-7thSSRCKF. Results demonstrate that the improved strong tracking cubature Kalman filter can achieve higher accuracy than the existing CKF and ST-CKF, and is more robust for the GPS/INS integrated navigation system.

Dynamic Phasor Estimation Through DSTKF Under Transient Conditions

This paper presents a double suboptimal-scaling-factor adaptive strong tracking Kalman filter (DSTKF)-based phasor measurement unit algorithm which can meet the accuracy requirement of the IEEE standard C37.118.1 under the dynamic condition. This method uses a $k$ th Taylor polynomial to linearize the complex exponential of the signal model, and estimates the dynamic phasor using DSTKF. The antialias filter unit for DSTKF algorithm is included to suppress high-order harmonics which will augment computing burden. By applying double suboptimal-scaling factors, DSTKF adjusts the process noise covariance matrix and the prediction covariance matrix in real time. Thereafter, amplitude and frequency measurement performances under various dynamic conditions are assessed in detail. Theoretical analysis and comprehensive experimental results validate the effectiveness of the proposed method.

An adaptive strong tracking Kalman filter for position and orientation system

A position and orientation systems (POS) plays an important role in aerial mapping applications. It integrates the inertial navigation system and global positioning system to provide high-precision position, velocity and attitude for various aerial mapping sensors. However, in severe environment of temperature, magnetic field and vibration in the application of aerial mapping, the precision of gyroscopes and accelerometers may degrade. The traditional Kalman filter may perform poorly when the model of gyroscope and accelerometer errors is uncertain. This paper highlights the use of multiple fading factors for a strong tracking Kalman filter (STKF) to accommodate the model uncertainty of gyroscope and accelerometer errors. Through utilizing the information of the sensitivity matrix of a two-stage Kalman filter, the multiple fading factors are obtained adaptively. Therefore, a more accurate covariance matrix is obtained in the proposed algorithm, and a better state tracking ability is achieved than with the Kalman filter and the STKF. Finally, a flight experiment is demonstrated to validate the effectiveness of the proposed algorithm. It is shown from the experimental results that the proposed algorithm can more accurately estimate the time-varying errors of gyroscopes and accelerometers than Kalman filters or the STKF; the accuracy of position, velocity and attitude of POS is also improved correspondingly.

Position Estimation and Smooth Tracking With a Fuzzy-Logic-Based Adaptive Strong Tracking Kalman Filter for Capacitive Touch Panels

This paper presents a novel 7-in capacitive touch panel (CTP) system with a smooth tracking algorithm that accurately estimates the position where the panel is touched and tracks the trajectory of touch. The proposed CTP system consists of a microcontroller unit, a sensor IC, and an interface board. When a user draws at different speeds, the measurement noise caused by the sensor IC induces an error in the touched position and zigzag trajectory, especially when the motion is slow. The fuzzy-logic-based adaptive strong tracking Kalman filter method is implemented in a CTP system to mitigate the effect of measurement noise and provide a smooth tracking trajectory at different speeds. Moreover, the approach effectively measures and quantifies the “smoothness” of the touched trajectory. Experimental results indicate that the proposed method reduces the measurement noise and decreases the mean tracking error by 85.4% over that achieved using the moving average filter.

An adaptive compensation algorithm for temperature drift of micro-electro-mechanical systems gyroscopes using a strong tracking Kalman filter.

We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.

多旋翼姿态解算中的改进自适应扩展Kalman算法

An improved Sage-Husa adaptive extended Kalman filter algorithm is proposed to ensure the precision and stability of calculating attitude angles of a multi-rotor Unmanned Aerial Vehicle (UAV) under the actual flight conditions, such as unknown and time-varied noise statistical properties, main disturbance source in vibration and attitude angles high dynamically changed. The algorithm uses attitude angle variance estimated by a gyroscope in real time to estimate system noise variance and only adopts an adaptive filter algorithm to estimate measurement noise variance on-line to ensure the precision and stability of filtering. Meanwhile, it introduces the criterion of filter convergence to restrain the divergence of Kalman filter through combining with a strong tracking Kalman filter algorithm. A flight experiment and corresponding analysis show that the root-mean-square errors of the pinch and roll angles estimated by the improved algorithm are 1.722 and 1.182, obviously better than that of the conventional Sage-Husa adaptive Kalman filter algorithm. It concludes that the improved algorithm has strong adaptive ability, good real-time performance, high precision and reliable operation. It meets the need of multi-rotor UAV autonomous flight and can be applied to other navigation information measuring systems with high dynamic performance requirements if the parameters are modified appropriately.