Computer Science – Information Theory
Scientific paper
2009-07-27
Proc. IEEE International Conf. Acoust.Speech Signal Process, pp. 3017 - 3020, Apr. 2008
Computer Science
Information Theory
Scientific paper
Multiple-input multiple-output (MIMO) radar systems have been shown to achieve superior resolution as compared to traditional radar systems with the same number of transmit and receive antennas. This paper considers a distributed MIMO radar scenario, in which each transmit element is a node in a wireless network, and investigates the use of compressive sampling for direction-of-arrival (DOA) estimation. According to the theory of compressive sampling, a signal that is sparse in some domain can be recovered based on far fewer samples than required by the Nyquist sampling theorem. The DOA of targets form a sparse vector in the angle space, and therefore, compressive sampling can be applied for DOA estimation. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than other approaches. This is particularly useful in a distributed scenario, in which the results at each receive node need to be transmitted to a fusion center for further processing.
Petropulu Athina P.
Poor Harold Vincent
Yu Yao
No associations
LandOfFree
Compressive Sensing for MIMO Radar does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Compressive Sensing for MIMO Radar, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Compressive Sensing for MIMO Radar will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-246504