scholarly journals A Recursive Least Square Adaptive Filter for Nonuniformity Correction of Infrared Image Sequences

2005 ◽  
pp. 540-546 ◽  
Author(s):  
Flavio Torres ◽  
Sergio N. Torres ◽  
César San Martín
Keyword(s):  
Adaptive Filter ◽  
Infrared Image ◽  
Image Sequences ◽  
Least Square ◽  
Recursive Least Square ◽  
Nonuniformity Correction

An improvement for scene-based nonuniformity correction of infrared image sequences

2013 ◽  
Author(s):  
Lixiang Geng ◽  
Qian Chen ◽  
Feng Shi ◽  
Changjiang Wang ◽  
Xuelian Yu
Keyword(s):  
Infrared Image ◽  
Image Sequences ◽  
Nonuniformity Correction

scholarly journals Adaptive Filtering for Full-Duplex UWA Systems with Time-Varying Self-Interference Channel

2020 ◽  
Author(s):  
Lu Shen ◽  
Yuriy Zakharov ◽  
Benjamin Henson ◽  
Nils Morozs ◽  
Paul Mitchell
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Mean Squared Error ◽  
Least Square ◽  
Full Duplex ◽  
Time Varying ◽  
Recursive Least Square ◽  
Exponential Window ◽  
Evaluation Metric ◽  
High Level

<div>Abstract:</div><div><br></div><div>To enable full-duplex (FD) in underwater acoustic (UWA) systems, a high level of self-interference (SI) cancellation (SIC) is required. For digital SIC, adaptive filters are used. In time-invariant channels, the SI can be effectively cancelled by classical recursive least-square (RLS) adaptive filters, such as the sliding-window RLS (SRLS) or exponential-window RLS, but their SIC performance degrades in time-varying channels, e.g., in channels with a moving sea surface. Their performance can be improved by delaying the filter inputs. This delay, however, makes the mean squared error (MSE) unsuitable for measuring the SIC performance. In this paper, we propose a new evaluation metric, the SIC factor (SICF), which gives better indication of the SIC performance compared to MSE. The SICF can be used in experiments and in real FD systems. A new SRLS adaptive filter based on parabolic approximation of the channel variation in time, named SRLS-P, is also proposed. The SIC performance of the SRLS-P adaptive filter and classical RLS algorithms (with and without the delay) is evaluated by simulation and in lake experiments. The results show that the SRLS-P adaptive filter significantly improves the SIC performance, compared to the classical RLS adaptive filters.</div>

A mass and road slope integrated estimation strategy based on the joint iteration of least square method and Sage-Husa adaptive filter for autonomous logistics vehicle

2021 ◽  
pp. 095440702110417
Author(s):  
Weida Wang ◽  
Yuanbo Zhang ◽  
Ke Chen ◽  
Hua Zhang ◽  
Xiantao Wang ◽  
...  
Keyword(s):  
Adaptive Filter ◽  
Autonomous Vehicles ◽  
Least Square Method ◽  
Estimation Algorithm ◽  
Least Square ◽  
Recursive Least Square ◽  
Estimation Strategy ◽  
Vehicle Mass ◽  
Integrated Estimation ◽  
Road Slope

Autonomous logistics vehicles are characterised by large changes in mass and their performances are greatly influenced by slope. In addition, sensors on autonomous vehicles are expensive and difficult to be installed considering application environment. To address these problems, a novel integrated estimation strategy for vehicle mass and road slope, which is based on the joint iteration of multi-model recursive least square (MMRLS) and Sage-Husa adaptive filter with the strong tracking filter (SH-STF), is proposed by utilising information involving speed, nominal engine torque and inherent parameters of vehicles. Firstly, due to the separate slowly-changing and time-dependent characteristics, the vehicle mass and road slope are estimated by using MMRLS and SH-STF separately. Secondly, the longitudinal dynamics gain and the steering dynamics gain are calculated separately based on each model’s residual probability distribution. Then, the two estimations module are combined by employing an iterative algorithm. Finally, the proposed strategy is verified by simulation and real vehicle tests. The tests result reveals that the estimation algorithm can effective estimate vehicle mass and road slope in real-time under straight going and steering conditions.

Pengurangan Noise Sepeda Motor dan Mesin Diesel dari Sinyal Bicara dengan Algoritma Recursive Least Square

2013 ◽  
Vol 5 (1) ◽  
pp. 18-25
Author(s):  
Hugeng Hugeng ◽  
Endah Setyaningsih ◽  
Meirista Wulandari
Keyword(s):  
Speech Recognition ◽  
Mean Square Error ◽  
Adaptive Filter ◽  
Noise Cancellation ◽  
Least Square ◽  
Mean Square ◽  
Adaptive Noise Cancellation ◽  
Recursive Least Square ◽  
Adaptive Noise

Adanya bunyi kendaraan bermotor yang tercampur dengan suara seseorang yang sedang berbicara dapat mengganggu suatu sistem contohnya pada sistem speech recognition sehingga perintah terhadap sistem tersebut tak dapat dikerjakan. Ada beberapa cara yang dapat digunakan untuk mengatasi masalah gangguan noise yaitu salah satunya menggunakan  filter adaptif dengan metode Adaptive Noise Cancellation (ANC). ANC menghilangkan noise yang tercampur dengan suatu sinyal berdasarkan noise referensi. ANC ini terdiri dari 2 bagian yaitu filter digital dan algoritma adaptif. Filter digital FIR dan algoritma adaptif RLS digunakan pada sistem ini. Pemfilteran menggunakan perangkat lunak Matlab secara simulasi dan hasil filter berupa sinyal estimasi. Keberhasilan sistem pengurangan noise ini dapat dilihat berdasarkan parameter Mean Square Error (MSE). Hasil parameter yang didapat menunjukkan bahwa sistem ini bisa mengurangi noise sepeda motor dan mesin diesel yang tercampur dengan sinyal bicara walau pun  nilai MSE yang dihasilkan cukup besar. Keywords - Adaptive, Filter, ANC, RLS

scholarly journals Adaptive Filtering for Full-Duplex UWA Systems with Time-Varying Self-Interference Channel

2020 ◽  
Author(s):  
Lu Shen ◽  
Yuriy Zakharov ◽  
Benjamin Henson ◽  
Nils Morozs ◽  
Paul Mitchell
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Mean Squared Error ◽  
Least Square ◽  
Full Duplex ◽  
Time Varying ◽  
Recursive Least Square ◽  
Exponential Window ◽  
Evaluation Metric ◽  
High Level

<div>Abstract:</div><div><br></div><div>To enable full-duplex (FD) in underwater acoustic (UWA) systems, a high level of self-interference (SI) cancellation (SIC) is required. For digital SIC, adaptive filters are used. In time-invariant channels, the SI can be effectively cancelled by classical recursive least-square (RLS) adaptive filters, such as the sliding-window RLS (SRLS) or exponential-window RLS, but their SIC performance degrades in time-varying channels, e.g., in channels with a moving sea surface. Their performance can be improved by delaying the filter inputs. This delay, however, makes the mean squared error (MSE) unsuitable for measuring the SIC performance. In this paper, we propose a new evaluation metric, the SIC factor (SICF), which gives better indication of the SIC performance compared to MSE. The SICF can be used in experiments and in real FD systems. A new SRLS adaptive filter based on parabolic approximation of the channel variation in time, named SRLS-P, is also proposed. The SIC performance of the SRLS-P adaptive filter and classical RLS algorithms (with and without the delay) is evaluated by simulation and in lake experiments. The results show that the SRLS-P adaptive filter significantly improves the SIC performance, compared to the classical RLS adaptive filters.</div>

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