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Vehicle State Estimation by Integrating the Recursive Least Squares Method with a variable forgetting factor with an Adaptive Iterative Extended Kalman Filter

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WORLD ELECTRIC VEHICLE JOURNAL 2024年第9期15卷 399页

作者： Chen, Yong Huang, Yanmin Song, Zeyu Beijing Informat Sci & Technol Univ Coll Mech & Elect Engn Beijing 100192 Peoples R China Collaborat Innovat Ctr Elect Vehicles Beijing Beijing Peoples R China Beijing Lab New Energy Vehicles Beijing Peoples R China

The sideslip angle and the yaw rate are the key state parameters for vehicle handling and stability control. To improve the accuracy of the input parameters and the time-varying characteristics of noise covariance in state estimation, a combined method of recursive least squares with a variable forgetting factor and adaptive iterative extended Kalman filtering is proposed for estimation. Based on the established three-degrees-of-freedom nonlinear model of the vehicle, the variable forgetting factor recursive least squares method is used to identify the tire cornering stiffness and serves as an input for vehicle state estimation. An innovative algorithm is used to optimise the uncertain noise covariance in the iterative extended Kalman filter (IEKF) process. Finally, with the help of the joint simulation of CarSim2019 and Matlab/Simulink R2022a, a distributed drive electric vehicle state parameter estimation model is established, and a simulation analysis of typical working conditions is carried out. Furthermore, an experiment is conducted with the pix moving vehicle and the integrated navigation system. The simulation and experimental results show that, compared to the traditional extended Kalman filter algorithm, the proposed algorithm improves the estimation accuracy of the yaw rate, sideslip angle, and longitudinal speed by 58.17%, 57.2%, and 76.47%, respectively, which shows that the algorithm has a higher estimation accuracy and a stronger applicability to provide accurate state information for vehicle handling and stability control.

关键词： distributed drive electric vehicle state estimation iterated extended Kalman filter variable forgetting factor recursive least squares adaptive control

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Adaptive Turbo Equalization with Sparse Homotopy DCD-RLS Algorithm with variable forgetting factor for Underwater Acoustic Communication

Adaptive Turbo Equalization with Sparse Homotopy DCD-RLS Alg...

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IEEE/OES China Ocean Acoustics (COA)

作者： Zhang Youwen Liu Lu Sun Dajun Harbin Engn Univ Coll Underwater Acoust Engn Acoust Sci & Technol Lab Harbin Peoples R China

ISBN: (纸本)9781467399784

Underwater acoustic channels have the features of sparse and time varying. Adaptive filter has become a common and useful tool for underwater acoustic communications over the sparse and time-varying channels. In this paper, we propose a novel homotopy dichotomous coordinate descent (DCD) recursive least squares (RLS) adaptive filtering algorithm, which incorporates the variable forgetting factor (VFF). Compared with the conventional RLS-type algorithms, the proposed algorithm can obtain its performance via two aspects: 1) making a remarkable improvement in a sparse fast time varying underwater acoustic channel and 2) reducing the computational complexity by using Homotopy DCD iterations. Numerical and lake experimental results show that the proposed algorithm has better tracking ability and a lower steady-state normalized mean square error (NMSE).

关键词： RLS algorithm Homotopy dichotomous coordinate descent algorithm variable forgetting factor adaptive filter

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Frequency Hopping Acquisition Based on variable forgetting factor Recursive Least Squares Algorithm 12

Frequency Hopping Acquisition Based on Variable Forgetting F...

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IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Chen, Zengmao Zhao, Xianwu Lu, Li Sun, Rongchen Sun, Zhiguo Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

ISBN: (纸本)9781665483032

The main drawback of frequency hopping pattern synchronization capture algorithms is time-consuming. To address this problem, variable forgetting factor recursive least square (VFFRLS) frequency hopping pattern acquisition algorithm is proposed in this paper. First, the coefficients of the adaptive spectrum estimation model are calculated iteratively used the VFFRLS algorithm. Then, instantaneous frequency of the frequency hopping signal is estimated, and the result is converted into a binary sequence. The most significant bit of the binary sequence is loaded into the linear feedback shift register. Finally, the correct frequency point could be obtained. The theoretical expressions of bit error rate, capture probability and average capture time are derived under the Additive White Gaussian Noise environment. Moreover, the capture performance is verified through numerical simulation.

关键词： frequency hopping acquisition spectrum estimation variable forgetting factor

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A variable forgetting factor QRD-based RLS Algorithm with Bias Compensation for System Identification with Input Noise

A Variable Forgetting Factor QRD-based RLS Algorithm with Bi...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Tan, H. J. Chan, S. C. Zhang, L. Univ Hong Kong Dept Elect & Elect Engn Pokfulam Rd Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479953417

This paper proposes a variable forgetting factor QRD-based recursive least squares algorithm with bias compensation (VFF-QR-RLS-BC) for system identification with input noise. The new algorithm is based on the least square estimation with bias compensation framework and it employs a variable forgetting factor to improve the tracking speed and a QRD-based implementation for recursively solving the LS problem with bias compensation. Simulation results show that the proposed method can obtain improved convergence rate in sudden system change environment and satisfactory performance under stationary environment.

关键词： System identification in input noise bias-compensation recursive least squares algorithm variable forgetting factor QR decomposition

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Linearly Constrained RLS Algorithm with variable forgetting factor for DS-CDMA System

Linearly Constrained RLS Algorithm with Variable Forgetting ...

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International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Chern, Shiunn-Jang Sun, Chun-Huang Tamkang Univ Dept Elect Engn New Taipei Taiwan Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung Taiwan

ISBN: (纸本)9781479961207

In this paper we present a novel linearly constrained (LC) RLS algorithm with variable forgetting factor (VFF), and apply to the DS-CDMA system for suddenly joined narrowband interference suppression. Under Rayleigh fading channel environment, the VFF could be employed to improve the tracking capability compared with the conventional LC-RLS algorithm with fixed value of forgetting factor (FF). From computer simulation results, we verify that the proposed scheme outperforms the conventional optimal FF LC-RLS algorithm in terms of rapid tracking capability and strong narrow-band suppression.

关键词： linearly constrained RLS variable forgetting factor DS-CDMA narrowband interference suppression SINR

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An Improved Harmonic Current Detection Method Based on variable forgetting factor RLS Algorithm

An Improved Harmonic Current Detection Method Based on Varia...

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International Conference on Sustainable Energy and Environmental Engineering (ICSEEE 2012)

作者： Vu, Minh Guang Han, Wei Wang, Dazhi Li, Yunlu Northeastern Univ Inst Elect Power Syst & Motor Drives Shenyang Peoples R China

ISBN: (纸本)9783037856345

The effects of compensating and restraining the power system harmonics using active power filter are determined by the detection precision and its dynamic response characters. To improve both of them, a harmonic current detection algorithm based on variable forgetting factor Recursive Least-Squares algorithm is presented. The occurrence of the dynamic process is identified firstly by the judgment condition which is given by the algorithm, and then the forgetting factor is assigned dynamically, so that the convergent speed is significantly improved. The algorithm overcomes the impact of low-pass filter of traditional p-q or ip-iq algorithm, and releases the contradiction cased by the conflicting requirements of forgetting factor value between steady process and dynamic process. So it has better dynamic performance. Simulation and experiments prove the validity and feasibility of the approaches

关键词： Recursive Least-Squares algorithm harmonic current detection variable forgetting factor active power filter

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Gradient based variable forgetting factor nonlinear RLS algorithm using correlation function with nonzero lags

Gradient based variable forgetting factor nonlinear RLS algo...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Leung, SH So, CF City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)0780374029

In environment with impulsive noise, most learning algorithms are encountered difficulty in distinguishing the nature of large error signal, whether caused by the impulse noise or large model error. Consequently, they suffer from slow convergence or large misadjustment. A new gradient based variable forgetting factor nonlinear RLS algorithm uses correlation function of error signal with nonzero lags (GCVFF) is introduced. The correlation of nonzero lags maintains the sensitivity of the algorithm responding to the model error and becomes sluggish to the impulse noise. Simulation results show that it achieves fast convergence speed and small misadjustment and outperforms other variable forgetting factor (VFF) RLS algorithms.

关键词： gradient methods nonlinear estimation nonlinear filters recursive filters least squares approximations correlation methods convergence of numerical methods adaptive filters impulse noise recursive estimation gradient based algorithm nonlinear RLS algorithm variable forgetting factor correlation function model error error signal nonzero lags convergence speed recursive least squares filtering adaptive filtering impulsive noise learning algorithms

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Improved variable forgetting factor Recursive Least Square Algorithm

Improved Variable Forgetting Factor Recursive Least Square A...

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12th International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Albu, Felix Valahia Univ Targoviste Dept Elect & Telecommun Targoviste Romania

ISBN: (纸本)9781467318723;9781467318716

In this paper an improved variable forgetting factor recursive least square (IVFF-RLS) algorithm is proposed. The forgetting factor is adjusted according to the square of a time-averaging estimate of the autocorrelation of a priori and a posteriori errors. The proposed algorithm has fast convergence, and robustness against variable background noise, near-end signal variations and echo path change. The simulation results indicate the superior performances of IVFF-RLS when compared to the RLS and VFF-RLS algorithms.

关键词： recursive least squares variable forgetting factor system identification adaptive control echo cancellation

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Low-Complexity variable forgetting factor Constant Modulus RLS-based Algorithm for Blind Adaptive Beamforming

Low-Complexity Variable Forgetting Factor Constant Modulus R...

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47th Asilomar Conference on Signals, Systems, and Computers

作者： Qin, Boya Cai, Yunlong Champagne, Benoit Zhao, Minjian Yousefi, Siamak Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 2A7 Canada

ISBN: (纸本)9781479923908

In this paper, we propose a novel low-complexity variable forgetting factor (VFF) mechanism to enhance the performance of recursive least squares (RLS) algorithms for adaptive blind beamforming. The beamformer is designed according to the constrained constant modulus (CCM) criterion, and the proposed algorithm operates in the generalized sidelobe canceler (GSC) structure for implementation. The proposed variable forgetting factor mechanism employs a new component updated by the time average of the constant modulus (CM) cost function, to adjust the forgetting factor. A complexity comparison is provided to show its advantages over existing methods. The study of the steady-state analysis is carried out. Simulation results which are presented for a nonstationary scenario illustrate that the proposed variable forgetting factor mechanism achieves a superior performance compared to existing algorithms.

关键词： Adaptive blind beamforming constrained constant modulus variable forgetting factor

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On the Performance of variable forgetting factor Recursive Least-Squares Algorithms 8

On the Performance of Variable Forgetting Factor Recursive L...

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8th International Conference on Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies (ATOM-N)

作者： Elisei-Iliescu, Camelia Paleologu, Constantin Tamas, Razvan Univ Politehn Bucuresti Bucharest Romania Maritime Univ Constanta Constanta Romania

ISBN: (数字)9781510604254

ISBN: (纸本)9781510604247;9781510604254

The recursive least-squares (RLS) is a very popular adaptive algorithm, which is widely used in many system identification problems. The parameter that crucially influences the performance of the RLS algorithm is the forgetting factor. The value of this parameter leads to a compromise between tracking, misadjustment, and stability. In this paper, we present some insights on the performance of variable forgetting factor RLS (VFF-RLS) algorithms, in the context of system identification. Besides the classical RLS algorithm, we mainly focus on two recently proposed VFF-RLS algorithms. The novelty of the experimental setup is that we use real-world signals provided by Romanian Air Traffic Services Administration, i.e., voice and noise signals corresponding to real communication channels. In this context, the Air Traffic Control (ATC) communication represents a challenging task, usually involving non-stationary environments and stability issues.

关键词： adaptive filters recursive least-squares (RLS) algorithm variable forgetting factor echo cancellation

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