Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals
Source abstract
Abstract
Accurately extracting weak signals is extremely important for overall performance and application in optoelectronic imaging and optical communication systems. While weak signals are susceptible to noise, adaptive filtering is a commonly used noise removal method. Still, its convergence speed is slow, the steady-state error is large, and the anti-interference ability is weak. To solve the above problems, this paper proposes a new type of variable-step-length adaptive filtering algorithm (DSLMS) based on the minutiae function, which effectively reduces the noise component in error through its combination with the pair cancelation system, utilizing the low correlation property of the noise signal, to improve the anti-noise interference ability of the adaptive filter. Using FPGA and Matlab (2018b) for experimental verification, the results show that this algorithm shows significant advantages in noise suppression, accelerated algorithm convergence, and low steady-state error, and it has effectiveness and application potential for the optoelectronic detection of weak signal processing.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrared Target Detection Methodologies · Impact of Light on Environment and Health · Advanced Measurement and Detection Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Control Science and Engineering, Tiangong University, Tianjin 300387, China
School of Information Engineering, Ningxia University, Yinchuan 750021, China
References (39)
Show source-provided references
1. Adaptive time delay estimation based on signal preprocessing and fourth-order cumulant10.1007/s00034-023-02390-7 ↗
2. On the tracking performance of adaptive filters and their combinations10.1109/TSP.2021.3081045 ↗
3. On the invention of active noise control by paul lueg10.1121/1.399195 ↗
4. A new normalized lmat algorithm and its performance analysis10.1016/j.sigpro.2014.05.018 ↗
5. Fast stable normalised least-mean fourth algorithm10.1049/el.2015.0421 ↗
6. Fast block lms and rlsbased parameter estimation and two-dimensional imaging in monostatic mimo radar systems with multiple mobile targets10.1109/TSP.2018.2795571 ↗
7. Implementation of a low power and high speed adaptive noise canceller using lms algorithm10.1016/j.matpr.2021.06.112 ↗
8. Yu, C., Gu, R., and Wang, Y. (2020, January 12–14). The application of improved variable step-size lms algorithm in sonar signal processing. Proceedings of the 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China.10.1109/ITNEC48623.2020.9084959 ↗
9. Efficient directionof-arrival estimation method based on variable-step-size lms algorithm10.1109/LAWP.2019.2923700 ↗
10. Senapati, A., Sharma, R., Roy, J.S., and Singh, P. (2018, January 22–24). Beamforming in smart antenna with multiple interferers using leaky lms and variable step size leaky lms. Proceedings of the 2018 International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC), Bhubaneswar, India.10.1109/AESPC44649.2018.9033362 ↗
11. Chergui, L., and Bouguezel, S. (2019, January 19–22). Variable step size pre-whitening transform domain lms adaptive noise canceller. Proceedings of the 2019 International Conference on Advanced Systems and Emergent Technologies (IC ASET), Hammamet, Tunisia.10.1109/ASET.2019.8871012 ↗
12. Adaptive filtering denoising method based on variational mode decomposition
13. Tobar, F.A., Kuh, A., and Mandic, D.P. (2012, January 17–20). A novel augmented complex valued kernel lms. Proceedings of the 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM), Hoboken, NJ, USA.10.1109/SAM.2012.6250542 ↗
14. The q-gradient lms spline adaptive filtering algorithm and its variable step-size variant10.1016/j.ins.2023.119983 ↗
15. Improved least mean square algorithm based in satellite multi-beamforming
16. Lu, B., Feng, C., and Long, G. (2013, January 16–17). A new variable step-size lms adaptive algorithm based on marr function. Proceedings of the 2013 International Conference on Information Technology and Applications, Chengdu, China.10.1109/ITA.2013.56 ↗
17. He, D., Wang, M., Han, Y., and Hui, S. (2019, January 28–30). Variable step size lms adaptive algorithm based on exponential function. Proceedings of the 2019 IEEE 2nd International Conference on Information Communication and Signal Processing (ICICSP), Weihai, China.10.1109/ICICSP48821.2019.8958492 ↗
18. Complexity, convergence and computational efficiency for system identification algorithms
19. Comparison and simulation of system identification algorithms based on correlation analysis
20. Combinations of adaptive filters: Performance and convergence properties
21. Affine-projection-like adaptive-filtering algorithms using gradient-based step size10.1109/TCSI.2014.2304665 ↗
22. Low observable target motion analysis using amplitude information10.1109/7.543858 ↗
23. Robust spline adaptive filtering based on accelerated gradient learning: Design and performance analysis10.1016/j.sigpro.2021.107965 ↗
24. The stability of variable step-size lms algorithms10.1109/78.806072 ↗
25. Lim, J.S. (1990). Two-Dimensional Signal and Image Processing, Prentice-Hall, Inc.
26. Improved variable step size least mean square algorithm for pipeline noise
27. A Globally Convergent Stochastic Pairwise Conjugate Gradient-Based Algorithm for Adaptive Filtering10.1109/LSP.2008.2005437 ↗
28. An adaptive-noise Augmented Kalman Filter approach for input-state estimation in structural dynamics10.1016/j.ymssp.2022.109654 ↗
29. A novel lms algorithm applied to adaptive noise cancellation10.1109/LSP.2008.2008584 ↗
30. A new class of self-normalising lms algorithms10.1049/ell2.12498 ↗
31. Fpga based high speed data acquisition system with ethernet interface
32. Wang, X., Zeng, T., Yin, S., Wang, Y., and Hu, Z. (2018, January 10–12). Design and implementation of automatic acquisition and processing system for balance based on arm and fpga. Proceedings of the 2018 5th International Conference on Systems and Informatics (ICSAI), Nanjing, China.10.1109/ICSAI.2018.8599303 ↗
33. Rai, A., Roy, A., Qamar, S., Saif, A.G.F., Hamoda, M.M., Azeem, A., and Mohammed, S.A. (2024). Modeling and simulation of fir filter using distributed arithmetic algorithm on fpga. Multimed. Tools Appl., 1–14.10.1007/s11042-024-18637-7 ↗
34. Design and fpga implementation of an adaptive narrowband interference suppression filter
35. Efficient very large-scale integration architecture design of proportionate-type least mean square adaptive filters
36. Fast and accurate measurement in botda fibre sensor through the application of filtering techniques in frequency and time domains10.1109/JSEN.2023.3347307 ↗
37. Mao, J., Wang, Z., Liu, J., and Song, D. (2024). A forward- backward splitting equivalent source method based on s- difference. Appl. Sci., 14.10.3390/app14031086 ↗
38. Salzenstein, P., and Pavlyuchenko, E. (2021). Uncertainty Evaluation on a 10.52 GHz (5 dBm) Optoelectronic Oscillator Phase Noise Performance. Micromachines, 12.10.3390/mi12050474 ↗
39. Meng, X., Yuan, H., and Wang, Y. (2021, January 27–28). Research on the construction method of photoelectric detection preamplifier circuit combined with single chip microcomputer technology. Proceedings of the 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China.10.1109/AEECA52519.2021.9574144 ↗