We propose a two-stage equalization based on a simplified Kalman filter, which is used to solve the rapid rotation of the state of polarization (RSOP) that is caused by lightning strikes on optical cables and the extra inter symbol interference (ISI) introduced in the system. By analyzing the special expression of matrix coefficient in the Kalman filter under polarization demultiplexing, the simplified idea of a Kalman filter is provided, and its updating process is transformed into a kind of multiple-input-multiple-output (MIMO) structure algorithm. At the same time, the second stage finite impulse response filter is used to solve the ISI that is difficult to be solved by a Kalman filter. The performance of the proposed algorithm was tested in a coherent system of 28Gbaud PDM-QPSK/16QAM. The results confirm that on the basis of lower complexity than a Kalman filter, the proposed algorithm reduces its complexity by more than 30% compared to traditional MIMO equalization algorithm under the premise of linear operation, and which also can handle RSOP of 20 Mrad/s. When the system suffers from the extra ISI due to the limited device bandwidth, the optical signal to noise ratio of the proposed algorithm is about 4 dB lower than the Kalman filter at the same bit error rate.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1364/OE.502176 | DOI Listing |
Accurately estimating phase is crucial in continuous-variable quantum key distribution systems, directly impacting the final secret key rate. In previous systems that utilize the local local oscillator, phase estimation is closely tied to the amplitude and signal-to-noise ratio (SNR) of the pilot signal. As SNR decreases, so does the accuracy of phase estimation, leading to increased excess noise and a potential loss of the system's secret key rate.
View Article and Find Full Text PDFComput Stat
September 2024
Department of Statistics, Purdue University, West Lafayette, IN 47907.
State estimation for large-scale non-Gaussian dynamic systems remains an unresolved issue, given nonscalability of the existing particle filter algorithms. To address this issue, this paper extends the Langevinized ensemble Kalman filter (LEnKF) algorithm to non-Gaussian dynamic systems by introducing a latent Gaussian measurement variable to the dynamic system. The extended LEnKF algorithm can converge to the right filtering distribution as the number of stages become large, while inheriting the scalability of the LEnKF algorithm with respect to the sample size and state dimension.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Mechatronics Engineering, North University of China, Taiyuan 030051, China.
To enhance the positioning accuracy of autonomous underwater vehicles (AUVs), a new adaptive filtering algorithm (RHAUKF) is proposed. The most widely used filtering algorithm is the traditional Unscented Kalman Filter or the Adaptive Robust UKF (ARUKF). Excessive noise interference may cause a decrease in filtering accuracy and is highly likely to result in divergence by means of the traditional Unscented Kalman Filter, resulting in an increase in uncertainty factors during submersible mission execution.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Engineering Design, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden.
Topography estimation is essential for autonomous off-road navigation. Common methods rely on point cloud data from, e.g.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Smart Diagnostic and Online Monitoring, Leipzig University of Applied Sciences, Wächterstraße 13, 04107 Leipzig, Germany.
This paper presents a comparative study of different AI models for indoor positioning systems, emphasizing improvements in localization accuracy and processing time. This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman filter using a real Received Signal Strength Indicator (RSSI) and 9-axis ICM-20948 sensor. An in-depth analysis is provided in this paper for data cleaning and feature selection to reduce errors for all the models.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!