Signal Reconstruction Using Bi-LSTM for Automotive Radar Interference Mitigation

• Automotive radar has emerged as an important sensor for environmental perception in modern vehicles. A rapid increase in the number of radars present in traffic operating at unregulated frequencies has given rise to a mutual interference problem. In order for radar-based systems to function reliably, such interference must be mitigated. In this paper, this problem is addressed with a bidirectional long short-term memory (Bi-LSTM) network as a deep learning approach. Using the Bi-LSTM network, we reconstruct the intermediate frequency (IF) signal and recover samples lost to interference. The proposed signal reconstruction method is evaluated via real measurement data. The proposed Bi-LSTM network provides a better performance than an autoregressive model-based signal reconstruction method. © 2022 European Microwave Association (EuMA).

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Nyckelord

FMCW radar

Interference suppression

Radar signal processing

Recurrent neural networks

Signal reconstruction

Automotive radar

Continuous wave radar

Frequency modulation

Long short-term memory

Radar equipment

Radar interference

Environmental perceptions

Interference mitigation

Interference problems

Learning approach

Memory network

Mutual interference

Reconstruction method

Signals reconstruction

Publikations- och innehållstyp

ref (ämneskategori)

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