Phased Array Radar-Based Channel Modeling and Sparse Channel Estimation for an Integrated Radar and Communication System | Zendy

Ling Huang | Zendy; Yu Zhang | Zendy; Qingyu Li | Zendy; Jian Song | Zendy

Open Access

Phased Array Radar-Based Channel Modeling and Sparse Channel Estimation for an Integrated Radar and Communication System

Author(s) -

Ling Huang,

Yu Zhang,

Qingyu Li,

Jian Song

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2731398

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

An integrated radar and communication system can cooperatively form a radar-communication network with significantly enhanced efficiency and considerably less occupied hardware resources. Such a system exhibits great advantages compared with traditional individual radar and communication modes. Numerous papers have proposed achieving the integrated functions based on phased array radar, whereas the channel modeling and channel estimation problems concerning communications are seldom discussed in the literature. In this paper, we propose a ray-cluster-based spatial channel model and a sounding channel estimation scheme realized on the existing hardware of phased array radar. Considering the correlation, we model the channel by comparing the difference between adjacent antennas, and we present the response vector of the antenna array. We analyze the number of beams that are needed to cover the space, and we present the sequence of directional beamforming vectors when sounding the channel. Redundant dictionary matrices are utilized to present the channel as a sparse signal recovery problem, in which the spatial sparsity is leveraged for performance enhancement. A sparsity adaptive matching pursuit (SAMP)-based compressed sensing tool is exploited for the sparse recovery problem and compared with the conventional least squares (LS) algorithm. The experimental results demonstrate that our proposed scheme can effectively solve the channel estimation problem in the integrated radar and communication system with reduced complexity, and it outperforms LS by 25% when considering mean square error (MSE) performance in general.

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