Weighted least squares algorithm for target localization in distributed MIMO radar
(2015) In Signal Processing 115. p.144-150- Abstract
- In this paper, we address the problem of locating a target using multiple-input multiple-output (MIMO) radar with widely separated antennas. Through linearizing the bistatic range measurements, which correspond to the sum of transmitter-to-target and target-to-receiver distances, a quadratically constrained quadratic program (QCQP) for target localization is formulated. The solution of the QCQP is proved to be an unbiased position estimate whose variance equals the Cramer-Rao lower bound. A weighted least squares algorithm is developed to realize the QCQP. Simulation results are included to demonstrate the high accuracy of the proposed MIMO radar positioning approach.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7601978
- author
- Einemo, Martin LU and So, Hing Cheung
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Multiple-input multiple-output (MIMO) radar, Target localization, Bistatic range, Weighted least squares
- in
- Signal Processing
- volume
- 115
- pages
- 144 - 150
- publisher
- Elsevier
- external identifiers
-
- wos:000356126500013
- scopus:84928811160
- ISSN
- 0165-1684
- DOI
- 10.1016/j.sigpro.2015.04.004
- language
- English
- LU publication?
- yes
- id
- ff801280-9466-41ca-bc2c-7be68c97565a (old id 7601978)
- date added to LUP
- 2016-04-01 13:27:52
- date last changed
- 2022-04-21 21:47:17
@article{ff801280-9466-41ca-bc2c-7be68c97565a, abstract = {{In this paper, we address the problem of locating a target using multiple-input multiple-output (MIMO) radar with widely separated antennas. Through linearizing the bistatic range measurements, which correspond to the sum of transmitter-to-target and target-to-receiver distances, a quadratically constrained quadratic program (QCQP) for target localization is formulated. The solution of the QCQP is proved to be an unbiased position estimate whose variance equals the Cramer-Rao lower bound. A weighted least squares algorithm is developed to realize the QCQP. Simulation results are included to demonstrate the high accuracy of the proposed MIMO radar positioning approach.}}, author = {{Einemo, Martin and So, Hing Cheung}}, issn = {{0165-1684}}, keywords = {{Multiple-input multiple-output (MIMO) radar; Target localization; Bistatic range; Weighted least squares}}, language = {{eng}}, pages = {{144--150}}, publisher = {{Elsevier}}, series = {{Signal Processing}}, title = {{Weighted least squares algorithm for target localization in distributed MIMO radar}}, url = {{http://dx.doi.org/10.1016/j.sigpro.2015.04.004}}, doi = {{10.1016/j.sigpro.2015.04.004}}, volume = {{115}}, year = {{2015}}, }