{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:58:52Z","timestamp":1760230732582,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T00:00:00Z","timestamp":1659744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation Of China","doi-asserted-by":"publisher","award":["61771361"],"award-info":[{"award-number":["61771361"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the shortage of independent and identically distributed (i.i.d.) training samples, space\u2212time adaptive processing (STAP) often suffers remarkable performance degradation in the heterogeneous clutter environment. Sparse recovery (SR) techniques have been introduced into STAP for the benefit of the drastically reduced training requirement, but they are incompletely robust for involving the tricky selection of hyper\u2212parameters or the undesirable point estimation for parameters. Given this issue, we incorporate the Multiple\u2212measurement Complex\u2212valued Variational relevance vector machines (MCV) to model the space\u2212time echoes and provide a Gibbs\u2212sampling\u2212based method to estimate posterior distributions of parameters accurately. However, the Gibbs sampler require quantities of iterations, as unattractive as traditional Bayesian type SR\u2212STAP algorithms when the real\u2212time processing is desired. To address this problem, we further develop the Bayesian Autoencoding MCV for STAP (BAMCV\u2212STAP), which builds the generative model according to MCV and approximates posterior distributions of parameters with an inference network pre\u2212trained off\u2212line, to realize fast reconstruction of measurements. Experimental results on simulated and measured data demonstrate that BAMCV\u2212STAP can achieve suboptimal clutter suppression in terms of the output signal to interference plus noise ratio (SINR) loss, as well as the attractive real\u2212time processing property in terms of the convergence rate and computational loads.<\/jats:p>","DOI":"10.3390\/rs14153800","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"3800","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2472-1860","authenticated-orcid":false,"given":"Chenxi","family":"Zhang","sequence":"first","affiliation":[{"name":"National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiliang","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenchao","family":"Chen","sequence":"additional","affiliation":[{"name":"National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Chen","sequence":"additional","affiliation":[{"name":"National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Penghui","family":"Wang","sequence":"additional","affiliation":[{"name":"National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changrui","family":"Jia","sequence":"additional","affiliation":[{"name":"The 38th Research Institute of China Electronics Technology Corporation, Hefei 230088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, X., Cheng, Y., Wu, H., and Wang, H. 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