Paper Info
Title
Heuristic noise driven compressed sensing for DOA estimation in phased array radar system
Abstract
For the classic model in compressed sensing, y= Φx + e, the proposed noise driven compressed sensing method tries to build an equivalent system of the classic model as, y = (Φ+p)x = ΦEx, provided Px = e. This results in an equivalent sensing matrix ΦE. Inspired by the idea that low coherence guarantees the reconstruction of the sparse vector with large probability, the equivalent sensing matrix ΦE is updated iteratively in a Markov Chain Monte Carlo (MCMC) based framework to reduce the large coherence between a set of specific columns. At the same time, the proposed method tries to preserve most of the information of the original sensing matrix Φ via adjusting the noise related matrix P. The proposed method is utilized successfully in a phased array radar system based solely on the existing hardware. Numerical simulations show that the proposed method obtains more precise estimation of direction of arrival (DOA) using one snapshot compared with the traditional estimation methods such as Capon, APES and GLRT based on hundreds of snapshots.
Year
Venue
Keywords
2012
Fusion
mcmc based framework,doa estimation,markov chain monte carlo based framework,direction of arrival,phased array radar,sparse vector,phased array radar system,compressed sensing,direction-of-arrival estimation,heuristic noise driven compressed sensing,radar,coherence,sensors,noise
Field
DocType
ISBN
Markov chain Monte Carlo,Direction of arrival,Computer science,Matrix (mathematics),Phased array,Artificial intelligence,Compressed sensing,Computer vision,Heuristic,Algorithm,Coherence (physics),Speech recognition,Capon
Conference
978-0-9824438-4-2
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Liu Jing112.41
Chongzhao Han244671.68
Hu Yu301.01