Asset Details
Do you wish to reserve the book?
Compressed sensing parallel factor analysis-based joint angle and Doppler frequency estimation for monostatic multiple-input–multiple-output radar
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Compressed sensing parallel factor analysis-based joint angle and Doppler frequency estimation for monostatic multiple-input–multiple-output radar
Do you wish to request the book?
Compressed sensing parallel factor analysis-based joint angle and Doppler frequency estimation for monostatic multiple-input–multiple-output radar
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Compressed sensing parallel factor analysis-based joint angle and Doppler frequency estimation for monostatic multiple-input–multiple-output radar
2014
Overview
In this study, the authors discuss the topic of joint angle and Doppler frequency estimation in a monostatic multiple-input–multiple-output radar and a compressed sensing parallel factor (CS-PARAFAC) analysis-based algorithm is proposed. In this algorithm, the joint estimation problem is firstly linked to the compressed sensing trilinear model, then the estimated compressed matrix can be derived through trilinear alternating least square method and the angle and Doppler frequency are jointly estimated with sparsity from the compressed matrices. The proposed CS-PARAFAC algorithm, which can obtain automatically paired angle and Doppler frequency estimation, has very close estimation performance to the conventional parallel factor analysis algorithm. When compared to the conventional subspace-based algorithm, such as estimation of signal parameters via rotational invariance techniques, it can achieve much better joint angle and Doppler frequency estimation performance. As the compression, the proposed algorithm has much lower computational complexity and smaller memory capacity meanwhile. Numerical simulations verify the efficiency and illustrate performance improvement of the proposed algorithm.
Publisher
The Institution of Engineering and Technology,The Institution of Engineering & Technology
