{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:04:05Z","timestamp":1769551445898,"version":"3.49.0"},"reference-count":0,"publisher":"Engineering and Technology Publishing","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["jcm"],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:p>To solve the problem that the weak signal is difficult to detect under a strong background noise, a detection method based on lifting wavelet threshold denoising and multi-layer autocorrelation method is proposed. Firstly, the original signal is denoised by lifting wavelet threshold to improve the signal-to-noise ratio. Secondly, the multi-layer autocorrelation function of the noise-reconstructed signal is calculated, and its time-frequency signature are analyzed. Finally, the combined algorithm is used on weak signals with low signal-to-noise ratio to extract weak signal features. Simulation and experimental results demonstrate that the proposed method can detect weak signal features buried in the heavy noise effectively. The proposed method is compared with the traditional noise reduction method, which reflects its effectiveness and superiority.<\/jats:p>","DOI":"10.12720\/jcm.17.11.890-899","type":"journal-article","created":{"date-parts":[[2022,12,4]],"date-time":"2022-12-04T21:43:27Z","timestamp":1670190207000},"page":"890-899","source":"Crossref","is-referenced-by-count":18,"title":["Weak Signal Detection Based on Lifting Wavelet Threshold Denoising and Multi-Layer Autocorrelation Method"],"prefix":"10.12720","author":[{"name":"College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China","sequence":"first","affiliation":[]},{"given":"Yuzhe","family":"Hou","sequence":"first","affiliation":[]},{"given":"Shunming","family":"Li","sequence":"additional","affiliation":[]},{"given":"Huijie","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Siqi","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Tianyi","family":"Yu","sequence":"additional","affiliation":[]}],"member":"4977","published-online":{"date-parts":[[2022]]},"container-title":["Journal of Communications"],"original-title":[],"link":[{"URL":"http:\/\/www.jocm.us\/uploadfile\/2022\/1031\/20221031034404126.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,4]],"date-time":"2022-12-04T21:43:32Z","timestamp":1670190212000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.jocm.us\/show-278-1834-1.html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":0,"URL":"https:\/\/doi.org\/10.12720\/jcm.17.11.890-899","relation":{},"ISSN":["2374-4367"],"issn-type":[{"value":"2374-4367","type":"print"}],"subject":[],"published":{"date-parts":[[2022]]}}}