Next Article in Journal
Closed-Form Algorithm for 3-D Near-Field OFDM Signal Localization under Uniform Circular Array
Previous Article in Journal
Design and Testing of a Flexible Inclinometer Probe for Model Tests of Landslide Deep Displacement Measurement
Previous Article in Special Issue
Direction-Of-Arrival Estimation and Tracking Based on a Sequential Implementation of C-SPICE with an Off-Grid Model
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(1), 219; https://doi.org/10.3390/s18010219

Improved Coarray Interpolation Algorithms with Additional Orthogonal Constraint for Cyclostationary Signals

1
College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China
2
School of Electrical Engineering & Automation, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150006, China
*
Author to whom correspondence should be addressed.
Received: 12 December 2017 / Revised: 10 January 2018 / Accepted: 11 January 2018 / Published: 14 January 2018
Full-Text   |   PDF [2563 KB, uploaded 15 January 2018]   |  

Abstract

Many modulated signals exhibit a cyclostationarity property, which can be exploited in direction-of-arrival (DOA) estimation to effectively eliminate interference and noise. In this paper, our aim is to integrate the cyclostationarity with the spatial domain and enable the algorithm to estimate more sources than sensors. However, DOA estimation with a sparse array is performed in the coarray domain and the holes within the coarray limit the usage of the complete coarray information. In order to use the complete coarray information to increase the degrees-of-freedom (DOFs), sparsity-aware-based methods and the difference coarray interpolation methods have been proposed. In this paper, the coarray interpolation technique is further explored with cyclostationary signals. Besides the difference coarray model and its corresponding Toeplitz completion formulation, we build up a sum coarray model and formulate a Hankel completion problem. In order to further improve the performance of the structured matrix completion, we define the spatial spectrum sampling operations and the derivative (conjugate) correlation subspaces, which can be exploited to construct orthogonal constraints for the autocorrelation vectors in the coarray interpolation problem. Prior knowledge of the source interval can also be incorporated into the problem. Simulation results demonstrate that the additional constraints contribute to a remarkable performance improvement. View Full-Text
Keywords: coarray interpolation; cyclostationarity; (conjugate) correlation subspaces; coprime array; orthogonal constraint; Toeplitz completion; Hankel completion coarray interpolation; cyclostationarity; (conjugate) correlation subspaces; coprime array; orthogonal constraint; Toeplitz completion; Hankel completion
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Song, J.; Shen, F. Improved Coarray Interpolation Algorithms with Additional Orthogonal Constraint for Cyclostationary Signals. Sensors 2018, 18, 219.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top