The Research on Interference Suppression Algorithms for Millimeter-Wave Radar in Multi-Interference Environments

TAN Haonan; DONG Mei; CHEN Boxiao

doi:10.11999/JEIT250617

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TAN Haonan, DONG Mei, CHEN Boxiao. The Research on Interference Suppression Algorithms for Millimeter-Wave Radar in Multi-Interference Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250617

Citation:

TAN Haonan, DONG Mei, CHEN Boxiao. The Research on Interference Suppression Algorithms for Millimeter-Wave Radar in Multi-Interference Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250617

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The Research on Interference Suppression Algorithms for Millimeter-Wave Radar in Multi-Interference Environments

doi: 10.11999/JEIT250617 cstr: 32379.14.JEIT250617

National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: National Natural Science Foundation of China (62271367)

Received Date: 2025-07-01
Rev Recd Date: 2025-09-17

Available Online: 2025-09-23

Abstract

Abstract

Objective With the widespread application of millimeter-wave radar in intelligent driving, mutual interference among radars has become increasingly prominent. Interference signals appear as sharp pulses in the time domain and elevated background noise in the frequency domain, severely degrading target information acquisition and threatening road traffic safety. To address this challenge, this paper proposes a joint envelope recovery–based signal reconstruction algorithm that exploits the time-domain characteristics of signals to enhance target detection performance in multi-interference environments. Methods The proposed algorithm consists of two core steps. Step 1: Interference region detection. A dual-criterion mechanism, combining interference envelope detection with transition point detection within the envelope, is employed. This approach substantially improves the accuracy of detecting both interference regions and useful signal segments in multi-interference environments. Step 2: Signal reconstruction. The detected useful signal segments and interference-free portions are used to reconstruct the interference regions. To ensure continuity and improve reconstruction accuracy, the Hilbert transform is applied to perform normalized envelope amplitude coordination on the reconstructed signal. Results and Discussions The algorithm first detects interference regions and useful signal segments with high precision through the dual-criterion mechanism, and then reconstructs the interference regions using the detected segments. Simulation results show that the algorithm achieves an interference detection accuracy of 93.7% and a useful signal segment detection accuracy of 97.2%, exceeding comparative algorithms (Table 3). The reconstructed signal effectively eliminates sharp interference pulses in the time domain, smooths the signal amplitude, and markedly improves the Signal-to-Interference-plus-Noise Ratio (SINR) in the frequency domain (Fig. 11). Compared with other interference suppression algorithms, the proposed method exhibits superior suppression performance (Fig. 12), achieving an SINR improvement of more than 3 dB in the frequency domain and maintaining better suppression effects across different SINR conditions (Fig. 13). In real-road tests, the algorithm successfully detects multiple interference regions and useful signal segments (Fig. 14) and significantly enhances the SINR after reconstruction (Fig. 15). Conclusions This paper proposes a joint envelope recovery–based signal reconstruction algorithm to address inaccurate target detection in multi-interference environments for millimeter-wave radar. The algorithm employs a dual-criterion mechanism to accurately detect interference regions and valid signal segments, and reconstructs the interference regions using the detected useful segments. The Hilbert transform is further applied to achieve collaborative normalization of the signal envelope. Experimental results demonstrate that the algorithm effectively identifies interference signals and reconstructs interference regions in multi-interference scenarios, significantly improving the signal-to-noise ratio, suppressing interference, and enabling accurate target information acquisition. These findings provide an effective anti-jamming solution for intelligent driving systems operating in multi-interference environments.
- Millimeter-wave radar,
- Frequency-modulated continuous wave,
- Multi-interference,
- Interference region detection,
- Signal reconstruction

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References(21)

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