Loading...

Sparsity-Based MIMO radars

Abtahi, A ; Sharif University of Technology | 2021

0 Viewed
  1. Type of Document: Article
  2. DOI: 10.1007/978-3-030-69637-5_17
  3. Publisher: Birkhauser , 2021
  4. Abstract:
  5. With advances in technology, Multiple-Input Multiple-Output (MIMO) radars have attracted a lot of attention in different modern military and civilian applications. As there are multiple receivers in a MIMO radar system, the cost can significantly be reduced if we can reduce the sampling rate and send fewer samples to the common processing center. Sometimes, the problem is not even the cost. It is the technology issues of high sampling rates such as the necessity of high-rate Analog-to-Digital (A/D) converters. The reduction in sampling rate can be achieved using Compressive Sensing (CS) or, in a much simpler form, Random Sampling (RS). In CS, we take a number of linear combinations of sparse signal samples which is smaller than what is necessary according to Shannon–Nyquist sampling theory. The sparse signal can be recovered from these linear combinations exploiting sparse recovery methods. By using sparse recovery methods, not only the sampling rate can be reduced but also the performance of the radars in detection and estimation procedures can be improved. In this chapter, we discuss the use of CS, RS, and sparse recovery methods in a MIMO radar system, the main challenges we face in this concept, and the solutions to these challenges proposed up to now. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG
  6. Keywords:
  8. Source: Applied and Numerical Harmonic Analysis ; 2021 , Pages 323-342 ; 22965009 (ISSN)
  9. URL: https://link.springer.com/chapter/10.1007/978-3-030-69637-5_17