Low probability of intercept-based adaptive radar waveform optimization in signal-dependent clutter for joint radar and cellular communication systems

Shi, Chenguang; Salous, Sana; Wang, Fei; Zhou, Jianjiang

Abstract

In this paper, we investigate the problem of low probability of intercept (LPI)-based adaptive radar waveform optimization in signal-dependent clutter for joint radar and cellular communication systems, where the radar system optimizes the transmitted waveform such that the interference caused to the cellular communication systems is strictly controlled. Assuming that the precise knowledge of the target spectra, the power spectral densities (PSDs) of signal-dependent clutters, the propagation losses of corresponding channels and the communication signals is known by the radar, three different LPI based criteria for radar waveform optimization are proposed to minimize the total transmitted power of the radar system by optimizing the multicarrier radar waveform with a predefined signal-to-interference-plus-noise ratio (SINR) constraint and a minimum required capacity for the cellular communication systems. These criteria differ in the way the communication signals scattered off the target are considered in the radar waveform design: (1) as useful energy, (2) as interference or (3) ignored altogether. The resulting problems are solved analytically and their solutions represent the optimum power allocation for each subcarrier in the multicarrier radar waveform. We show with numerical results that the LPI performance of the radar system can be significantly improved by exploiting the scattered echoes off the target due to cellular communication signals received at the radar receiver.

Citation

Shi, C., Salous, S., Wang, F., & Zhou, J. (2016). Low probability of intercept-based adaptive radar waveform optimization in signal-dependent clutter for joint radar and cellular communication systems. EURASIP Journal on Advances in Signal Processing, 2016(1), Article 111. https://doi.org/10.1186/s13634-016-0411-6

• Journal Article Type: Article
• Acceptance Date: Oct 7, 2016
• Online Publication Date: Oct 28, 2016
• Publication Date: Oct 28, 2016
• Deposit Date: May 4, 2017
• Publicly Available Date: May 5, 2017
• Journal: EURASIP Journal on Advances in Signal Processing
• Print ISSN: 1687-6172
• Electronic ISSN: 1687-6180
• Publisher: SpringerOpen
• Peer Reviewed: Peer Reviewed
• Volume: 2016
• Issue: 1
• Article Number: 111
• DOI: https://doi.org/10.1186/s13634-016-0411-6
• Public URL: https://durham-repository.worktribe.com/output/1388168