{"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2110.03218","open_access":"1"}],"publisher":"Institute of Electrical and Electronics Engineers","publication":"31st International Workshop on Machine Learning for Signal Processing","day":"01","OA_place":"repository","oa_version":"Preprint","doi":"10.1109/mlsp52302.2021.9596168","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","extern":"1","date_created":"2024-10-08T13:03:09Z","intvolume":"         4","type":"conference","OA_type":"green","oa":1,"conference":{"location":"Gold Coast, Australia","name":"MLSP: Machine Learning for Signal Processing","start_date":"2021-10-25","end_date":"2021-10-28"},"volume":4,"publication_identifier":{"eisbn":["9781728163383"]},"quality_controlled":"1","author":[{"first_name":"Tomer","last_name":"Weiss","full_name":"Weiss, Tomer"},{"first_name":"Nissim","last_name":"Peretz","full_name":"Peretz, Nissim"},{"full_name":"Vedula, Sanketh","first_name":"Sanketh","last_name":"Vedula"},{"full_name":"Feuer, Arie","first_name":"Arie","last_name":"Feuer"},{"orcid":"0000-0001-9699-8730","last_name":"Bronstein","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander"}],"language":[{"iso":"eng"}],"date_published":"2021-10-01T00:00:00Z","citation":{"ama":"Weiss T, Peretz N, Vedula S, Feuer A, Bronstein AM. Joint optimization of system design and reconstruction in MIMO radar imaging. In: <i>31st International Workshop on Machine Learning for Signal Processing</i>. Vol 4. Institute of Electrical and Electronics Engineers; 2021. doi:<a href=\"https://doi.org/10.1109/mlsp52302.2021.9596168\">10.1109/mlsp52302.2021.9596168</a>","short":"T. Weiss, N. Peretz, S. Vedula, A. Feuer, A.M. Bronstein, in:, 31st International Workshop on Machine Learning for Signal Processing, Institute of Electrical and Electronics Engineers, 2021.","chicago":"Weiss, Tomer, Nissim Peretz, Sanketh Vedula, Arie Feuer, and Alex M. Bronstein. “Joint Optimization of System Design and Reconstruction in MIMO Radar Imaging.” In <i>31st International Workshop on Machine Learning for Signal Processing</i>, Vol. 4. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/mlsp52302.2021.9596168\">https://doi.org/10.1109/mlsp52302.2021.9596168</a>.","ista":"Weiss T, Peretz N, Vedula S, Feuer A, Bronstein AM. 2021. Joint optimization of system design and reconstruction in MIMO radar imaging. 31st International Workshop on Machine Learning for Signal Processing. MLSP: Machine Learning for Signal Processing vol. 4.","ieee":"T. Weiss, N. Peretz, S. Vedula, A. Feuer, and A. M. Bronstein, “Joint optimization of system design and reconstruction in MIMO radar imaging,” in <i>31st International Workshop on Machine Learning for Signal Processing</i>, Gold Coast, Australia, 2021, vol. 4.","apa":"Weiss, T., Peretz, N., Vedula, S., Feuer, A., &#38; Bronstein, A. M. (2021). Joint optimization of system design and reconstruction in MIMO radar imaging. In <i>31st International Workshop on Machine Learning for Signal Processing</i> (Vol. 4). Gold Coast, Australia: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/mlsp52302.2021.9596168\">https://doi.org/10.1109/mlsp52302.2021.9596168</a>","mla":"Weiss, Tomer, et al. “Joint Optimization of System Design and Reconstruction in MIMO Radar Imaging.” <i>31st International Workshop on Machine Learning for Signal Processing</i>, vol. 4, Institute of Electrical and Electronics Engineers, 2021, doi:<a href=\"https://doi.org/10.1109/mlsp52302.2021.9596168\">10.1109/mlsp52302.2021.9596168</a>."},"status":"public","publication_status":"published","month":"10","_id":"18241","article_processing_charge":"No","date_updated":"2024-10-16T09:41:11Z","title":"Joint optimization of system design and reconstruction in MIMO radar imaging","external_id":{"arxiv":["2110.03218"]},"abstract":[{"text":"Multiple-input multiple-output (MIMO) radar is one of the leading depth sensing modalities. However, the usage of multiple receive channels lead to relative high costs and prevent the penetration of MIMOs in many areas such as the automotive industry. Over the last years, few studies concentrated on designing reduced measurement schemes and image reconstruction schemes for MIMO radars, however these problems have been so far addressed separately. On the other hand, recent works in optical computational imaging have demonstrated growing success of simultaneous learning-based design of the acquisition and reconstruction schemes, manifesting significant improvement in the reconstruction quality. Inspired by these successes, in this work, we propose to learn MIMO acquisition parameters in the form of receive (Rx) antenna elements locations jointly with an image neural-network based reconstruction. To this end, we propose an algorithm for training the combined acquisition-reconstruction pipeline end-to-end in a differentiable way. We demonstrate the significance of using our learned acquisition parameters with and without the neural-network reconstruction. Code and datasets will be released upon publication.","lang":"eng"}],"scopus_import":"1","year":"2021"}