79 research outputs found
Mutual Interference Mitigation in PMCW Automotive Radar
This paper addresses the challenge of mutual interference in phase-modulated
continuous wave (PMCW) millimeter-wave (mmWave) automotive radar systems. The
increasing demand for advanced driver assistance systems (ADAS) has led to a
proliferation of vehicles equipped with mmWave radar systems that operate in
the same frequency band, resulting in mutual interference that can degrade
radar performance creating safety hazards. We consider scenarios involving two
similar PMCW radar systems and propose an effective technique for a cooperative
design of transmit waveforms such that the mutual interference between them is
minimized. The proposed approach is numerically evaluated via simulations of a
mmWave automotive radar system. The results demonstrate that the proposed
technique notably reduces mutual interference and enhances radar detection
performance while imposing very little computational cost and a negligible
impact on existing infrastructure in practical automotive radar system
Joint Optimization of Waveform Covariance Matrix and Antenna Selection for MIMO Radar
In this paper, we investigate the problem of jointly optimizing the waveform
covariance matrix and the antenna position vector for
multiple-input-multiple-output (MIMO) radar systems to approximate a desired
transmit beampattern as well as to minimize the cross-correlation of the
received signals reflected back from the targets. We formulate the problem as a
non-convex program and then propose a cyclic optimization approach to
efficiently tackle the problem. We further propose a novel local optimization
framework in order to efficiently design the corresponding antenna positions.
Our numerical investigations demonstrate a good performance both in terms of
accuracy and computational complexity, making the proposed framework a good
candidate for real-time radar signal processing applications.Comment: This paper is accepted for publication in the 2019 IEEE Asilomar
Conference on Signals, Systems, and Computers (Asilomar 2019
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