{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:27:16Z","timestamp":1740202036950,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>Cognitive radio needs high sampling rate to monitor wide frequency band. In this work, we introduce a coprime sampling approach to address this problem. The proposed model consists of two analog-to-digital converters (ADCs), which are clocked at sub-Nyquist coprime rates. In the proposed sampling model, the two ADCs are not required to be synchronously clocked. The two channels simultaneously sample a common signal. The obtained samples are grouped into multiple uniform low speed sequences. We construct the relation between the known autocorrelation functions and unknown average PSD. It is proved that PSD estimation needs to solve an over-determined problem, and the least square algorithm could be used. Simulation results are reported to evaluate the proposed PSD algorithm, and it indicates that the proposed algorithm is feasible.<\/jats:p>","DOI":"10.3233\/978-1-61499-927-0-802","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:27:05Z","timestamp":1740133625000},"source":"Crossref","is-referenced-by-count":0,"title":["Power Spectral Density Estimation from Coprime Samples"],"prefix":"10.3233","author":[{"family":"Zhao Yijiu","sequence":"additional","affiliation":[]},{"family":"Chen Yu","sequence":"additional","affiliation":[]},{"family":"Yuan Xibin","sequence":"additional","affiliation":[]},{"family":"Zhang Shanshan","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining IV"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:06:58Z","timestamp":1740136018000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-926-3&spage=802&doi=10.3233\/978-1-61499-927-0-802"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-927-0-802","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2018]]}}}