Sparsity-aided radarwaveform synthesis

Sparsity-aided radarwaveform synthesis. Hu, H., Soltanalian, M., Stoica, P., & Zhu, X. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 2270-2274, Sep., 2014.
abstract bibtex

Owing to the inherent sparsity of the target scene, compressed sensing (CS) has been successfully employed in radar applications. It is known that the performance of target scene recovery in CS scenarios depends highly on the coherence of the sensing matrix (CSM), which is determined by the radar transmit waveform. In this paper, we present a cyclic optimization algorithm to effectively reduce the CSM via a judicious design of the radar waveform. The proposed method provides a reduction in the size of the Gram matrix associated with the sensing matrix, and moreover, relies on the fast Fourier transform (FFT) operations to improve the computation speed. As a result, the suggested algorithm can be used for large dimension designs (with ≲ 100 variables) even on an ordinary PC. The effectiveness of the proposed algorithm is illustrated through numerical examples.

@InProceedings{6952834,
  author = {H. Hu and M. Soltanalian and P. Stoica and X. Zhu},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Sparsity-aided radarwaveform synthesis},
  year = {2014},
  pages = {2270-2274},
  abstract = {Owing to the inherent sparsity of the target scene, compressed sensing (CS) has been successfully employed in radar applications. It is known that the performance of target scene recovery in CS scenarios depends highly on the coherence of the sensing matrix (CSM), which is determined by the radar transmit waveform. In this paper, we present a cyclic optimization algorithm to effectively reduce the CSM via a judicious design of the radar waveform. The proposed method provides a reduction in the size of the Gram matrix associated with the sensing matrix, and moreover, relies on the fast Fourier transform (FFT) operations to improve the computation speed. As a result, the suggested algorithm can be used for large dimension designs (with ≲ 100 variables) even on an ordinary PC. The effectiveness of the proposed algorithm is illustrated through numerical examples.},
  keywords = {compressed sensing;fast Fourier transforms;optimisation;radar signal processing;sparse matrices;sensing matrix coherence;sparsity-aided radar waveform synthesis;radar transmit waveform;fast Fourier transform;Gram matrix;cyclic optimization algorithm;compressed sensing;Coherence;Sensors;Compressed sensing;Vectors;Algorithm design and analysis;MIMO radar;compressed sensing;mutual coherence;radar;sensing matrix;sparsity;waveform synthesis},
  issn = {2076-1465},
  month = {Sep.},
}

Downloads: 0

{"_id":"bunxd6Fsgv8zLQtoA","bibbaseid":"hu-soltanalian-stoica-zhu-sparsityaidedradarwaveformsynthesis-2014","authorIDs":[],"author_short":["Hu, H.","Soltanalian, M.","Stoica, P.","Zhu, X."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["H."],"propositions":[],"lastnames":["Hu"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Soltanalian"],"suffixes":[]},{"firstnames":["P."],"propositions":[],"lastnames":["Stoica"],"suffixes":[]},{"firstnames":["X."],"propositions":[],"lastnames":["Zhu"],"suffixes":[]}],"booktitle":"2014 22nd European Signal Processing Conference (EUSIPCO)","title":"Sparsity-aided radarwaveform synthesis","year":"2014","pages":"2270-2274","abstract":"Owing to the inherent sparsity of the target scene, compressed sensing (CS) has been successfully employed in radar applications. It is known that the performance of target scene recovery in CS scenarios depends highly on the coherence of the sensing matrix (CSM), which is determined by the radar transmit waveform. In this paper, we present a cyclic optimization algorithm to effectively reduce the CSM via a judicious design of the radar waveform. The proposed method provides a reduction in the size of the Gram matrix associated with the sensing matrix, and moreover, relies on the fast Fourier transform (FFT) operations to improve the computation speed. As a result, the suggested algorithm can be used for large dimension designs (with ≲ 100 variables) even on an ordinary PC. The effectiveness of the proposed algorithm is illustrated through numerical examples.","keywords":"compressed sensing;fast Fourier transforms;optimisation;radar signal processing;sparse matrices;sensing matrix coherence;sparsity-aided radar waveform synthesis;radar transmit waveform;fast Fourier transform;Gram matrix;cyclic optimization algorithm;compressed sensing;Coherence;Sensors;Compressed sensing;Vectors;Algorithm design and analysis;MIMO radar;compressed sensing;mutual coherence;radar;sensing matrix;sparsity;waveform synthesis","issn":"2076-1465","month":"Sep.","bibtex":"@InProceedings{6952834,\n author = {H. Hu and M. Soltanalian and P. Stoica and X. Zhu},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Sparsity-aided radarwaveform synthesis},\n year = {2014},\n pages = {2270-2274},\n abstract = {Owing to the inherent sparsity of the target scene, compressed sensing (CS) has been successfully employed in radar applications. It is known that the performance of target scene recovery in CS scenarios depends highly on the coherence of the sensing matrix (CSM), which is determined by the radar transmit waveform. In this paper, we present a cyclic optimization algorithm to effectively reduce the CSM via a judicious design of the radar waveform. The proposed method provides a reduction in the size of the Gram matrix associated with the sensing matrix, and moreover, relies on the fast Fourier transform (FFT) operations to improve the computation speed. As a result, the suggested algorithm can be used for large dimension designs (with ≲ 100 variables) even on an ordinary PC. The effectiveness of the proposed algorithm is illustrated through numerical examples.},\n keywords = {compressed sensing;fast Fourier transforms;optimisation;radar signal processing;sparse matrices;sensing matrix coherence;sparsity-aided radar waveform synthesis;radar transmit waveform;fast Fourier transform;Gram matrix;cyclic optimization algorithm;compressed sensing;Coherence;Sensors;Compressed sensing;Vectors;Algorithm design and analysis;MIMO radar;compressed sensing;mutual coherence;radar;sensing matrix;sparsity;waveform synthesis},\n issn = {2076-1465},\n month = {Sep.},\n}\n\n","author_short":["Hu, H.","Soltanalian, M.","Stoica, P.","Zhu, X."],"key":"6952834","id":"6952834","bibbaseid":"hu-soltanalian-stoica-zhu-sparsityaidedradarwaveformsynthesis-2014","role":"author","urls":{},"keyword":["compressed sensing;fast Fourier transforms;optimisation;radar signal processing;sparse matrices;sensing matrix coherence;sparsity-aided radar waveform synthesis;radar transmit waveform;fast Fourier transform;Gram matrix;cyclic optimization algorithm;compressed sensing;Coherence;Sensors;Compressed sensing;Vectors;Algorithm design and analysis;MIMO radar;compressed sensing;mutual coherence;radar;sensing matrix;sparsity;waveform synthesis"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2014url.bib","creationDate":"2021-02-13T17:43:41.780Z","downloads":0,"keywords":["compressed sensing;fast fourier transforms;optimisation;radar signal processing;sparse matrices;sensing matrix coherence;sparsity-aided radar waveform synthesis;radar transmit waveform;fast fourier transform;gram matrix;cyclic optimization algorithm;compressed sensing;coherence;sensors;compressed sensing;vectors;algorithm design and analysis;mimo radar;compressed sensing;mutual coherence;radar;sensing matrix;sparsity;waveform synthesis"],"search_terms":["sparsity","aided","radarwaveform","synthesis","hu","soltanalian","stoica","zhu"],"title":"Sparsity-aided radarwaveform synthesis","year":2014,"dataSources":["A2ezyFL6GG6na7bbs","oZFG3eQZPXnykPgnE"]}