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A Novel Waveform Optimization Method for Orthogonal-Frequency Multiple-Input Multiple-Output Radar Based on

Meng Xia¹, Wenrong Gong¹, Lichao Yang²

¹School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)

|September 14, 2024

Summary

This study addresses high sidelobe levels in orthogonal frequency-division multiplexing (OFDM) with linear frequency modulation (LFM) radar waveforms. A deep neural network approach optimizes OFDM-LFM signals, significantly improving radar detection performance.

Keywords:

MIMO radar OFDM-LFM dual-channel neural networks orthogonal frequency waveform design

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Area of Science:

Radar Systems Engineering
Signal Processing
Artificial Intelligence in Engineering

Background:

Orthogonal frequency-division multiplexing (OFDM) with linear frequency modulation (LFM) is common in multiple-input multiple-output (MIMO) radar.
High sidelobe levels in OFDM-LFM waveforms after pulse compression degrade target detection capabilities.

Purpose of the Study:

Investigate the causes of high sidelobe levels in OFDM-LFM waveforms.
Propose a novel waveform optimization design method using deep neural networks.
Enhance radar target detection performance by optimizing OFDM-LFM waveforms.

Main Methods:

Theoretical analysis to identify sources of high sidelobe levels.
Deep neural network design utilizing the ResNeXt architecture for dual-channel networks.
Development of a new loss function for optimizing OFDM-LFM waveform phase and bandwidth.
Application of an optimization factor to manage peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs).

Main Results:

Numerical results confirm the theoretical analysis of sidelobe level causes.
The proposed deep neural network method effectively optimizes OFDM-LFM waveforms.
Designed waveforms demonstrate superior pulse compression characteristics.
Significant improvement in radar detection performance was observed.

Conclusions:

The theoretical analysis accurately explains high sidelobe levels in OFDM-LFM waveforms.
Deep neural network-based optimization offers an effective solution for waveform design.
Optimized OFDM-LFM waveforms enhance radar system capabilities, particularly in target detection.