Impacts of the measurement equation modification of the adaptive Kalman filter on joint polarization and laser phase noise tracking

Hu, Shaohua; Zhang, Jing; Li, Qun; Bai, Linchangchun; Yi, Xingwen; Xu, Bo; Qiu, Kun

doi:10.3788/col202220.020603

“…Trajectory prediction research in China started earlier, and the traditional methods mainly focus on the trajectory prediction methods based on techniques such as rules, statistics and machine learning. For example, trajectory prediction methods based on mathematical models such as Kalman filter and particle filter are widely used in the fields of target tracking and traffic management [13][14][15][16][17] . In recent years, domestic scholars began to adopt deep learning methods for trajectory prediction research.…”

Section: Introductionmentioning

confidence: 99%

Application of Optimized Kalman Filtering in Target Tracking Based on Improved Gray Wolf Algorithm

Pang,

Wang,

Yang

2023

Preprint

1

0

View full text Add to dashboard Cite

High precision is a very important index in target tracking. In order to improve the prediction accuracy of target tracking, an optimized Kalman filter approach based on improved Gray Wolf algorithm (IGWO-OKF) is proposed in this paper. Since the convergence speed of traditional Gray Wolf algorithm is slow, meanwhile, the number of gray wolves and the choice of the maximum number of iterations has a great influence on the algorithm, a nonlinear control parameter combination adjustment strategy is proposed. An improved Grey Wolf Optimization algorithm (IGWO) is formed by determining the best combination of adjustment parameters through the fastest iteration speed of the algorithm. The improved Grey Wolf Optimization algorithm (IGWO) is formed, and the process noise covariance matrix and observation noise covariance matrix in Kalman filter are optimized by IGWO. The proposed approach is applied into. The experiment results show that the proposed IGWO-OKF approach has low error, high accuracy and good prediction effect.

show abstract

“…Although some results have been achieved, they still face challenges such as complex scenarios, multi-target interactions, and uncertainty, which require further research and improvement.Trajectory prediction research in China started earlier, and the traditional methods mainly focus on the trajectory prediction methods based on techniques such as rules, statistics and machine learning. For example, trajectory prediction methods based on mathematical models such as Kalman filter and particle filter are widely used in the fields of target tracking and traffic management [13][14][15] . In recent years, domestic scholars began to adopt deep learning methods for trajectory prediction research.…”

mentioning

confidence: 99%

Application of optimized Kalman filtering in target tracking based on improved Gray Wolf algorithm

Pang,

Wang,

Yang

2024

Sci Rep

0

View full text Add to dashboard Cite

High precision is a very important index in target tracking. In order to improve the prediction accuracy of target tracking, an optimized Kalman filter approach based on improved Gray Wolf algorithm (IGWO-OKF) is proposed in this paper. Since the convergence speed of traditional Gray Wolf algorithm is slow, meanwhile, the number of gray wolves and the choice of the maximum number of iterations has a great influence on the algorithm, a nonlinear control parameter combination adjustment strategy is proposed. An improved Grey Wolf Optimization algorithm (IGWO) is formed by determining the best combination of adjustment parameters through the fastest iteration speed of the algorithm. The improved Grey Wolf Optimization algorithm (IGWO) is formed, and the process noise covariance matrix and observation noise covariance matrix in Kalman filter are optimized by IGWO. The proposed approach is applied into. The experiment results show that the proposed IGWO-OKF approach has low error, high accuracy and good prediction effect.

show abstract

Partially decoupled polarization demultiplexing and phase noise equalizer for Alamouti code-based simplified coherent systems

Jin,

Ling,

Zhang

et al. 2023

Opt. Express

1

0

View full text Add to dashboard Cite

Alamouti space-time block code (STBC) combined with a simple heterodyne coherent receiver can realize polarization-insensitive phase-diversity detection to reduce the cost. In the receiver, a joint equalizer has been used for STBC’s polarization demultiplexing and phase tracking. However, the joint equalizer requires two different step size parameters to update the tap weight coefficients for polarization demultiplexing and the phase noise estimation. This leads to the search process being complex so requiring more iterations for convergence. In this paper, we propose a partially decoupled equalizer that consists of a polarization and phase decoupled equalizer (PPDE) and a pilot-aided blind phase search (P-BPS) algorithm to accelerate the convergence and improve the phase noise tolerance. By theoretically calculating the phase noise, the PPDE can achieve polarization demultiplexing with only one single step size parameter, thus suppressing the searching space and greatly reducing the iterations required for convergence. In the carrier phase recovery stage, the P-BPS algorithm can effectively improve the phase noise tolerance and solve the cyclic slip problem of BPS. We conduct numerical simulations and an experiment to transmit a quadrature phase-shift keying (QPSK) signal. The results demonstrate that the number of iterations required for PPDE convergence is only half of that of the joint equalizer while maintaining polarization-insensitive characteristics in large phase noise. Meanwhile, the achievable linewidth tolerance of P-BPS is increased by three times compared with DD-LMS.

show abstract

Impacts of the measurement equation modification of the adaptive Kalman filter on joint polarization and laser phase noise tracking

Cited by 3 publications

References 19 publications

Application of Optimized Kalman Filtering in Target Tracking Based on Improved Gray Wolf Algorithm

Application of Optimized Kalman Filtering in Target Tracking Based on Improved Gray Wolf Algorithm

Application of optimized Kalman filtering in target tracking based on improved Gray Wolf algorithm

Partially decoupled polarization demultiplexing and phase noise equalizer for Alamouti code-based simplified coherent systems

Contact Info

Product

Resources

About