Super-Resolution Range-Velocity Estimate of Multiple Targets for OFDM-Based 5G Radar Based on Unitary Parallel Factor Method

In this paper, we consider the problem of joint range-velocity estimate of multiple targets in orthogonal frequency division multiplex (OFDM) transmit waveform-based 5G radar. A unitary parallel factor (PARAFAC) algorithm is proposed to achieve super-resolution estimate and outstanding performance,...

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Vydáno v:IEEE transactions on vehicular technology Ročník 74; číslo 3; s. 4048 - 4063
Hlavní autoři: Cao, Chenghu, Huang, Haisheng, Zhao, Yongbo
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.03.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
5G mobile communication
Accuracy
Algorithms
Complexity
Decomposition
Forward-backward averaging scheme
OFDM
OFDM-based 5G radar
Orthogonal Frequency Division Multiplexing
Radar
Radar applications
Radar signal processing
real-valued tensor signal model
regularized alterative least squares
Sensors
Signal processing
Signal processing algorithms
Symbols
Tensors
unitary parallel factor
Waveforms
ISSN:0018-9545, 1939-9359
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Shrnutí:In this paper, we consider the problem of joint range-velocity estimate of multiple targets in orthogonal frequency division multiplex (OFDM) transmit waveform-based 5G radar. A unitary parallel factor (PARAFAC) algorithm is proposed to achieve super-resolution estimate and outstanding performance, using forward-backward averaging scheme. The forward-backward averaging scheme is adopted to construct real-valued tensor signal model instead of the complex-valued one, yielding the better accuracy at modest complexity. The proposed unitary PARAFAC algorithm is performed by decomposing the real-valued tensor without signal subspace estimate. Due to the inherent smoothing processing of the proposed unitary PARAFAC algorithm, it can effectively deal with high correlated target. Additionally, the proposed unitary PARAFAC algorithm can automatically obtain pair parameters including range and velocity of the same target without additional pair-matching operation. More importantly the regularized alternative least squares (RALS) algorithm is used to improve the decomposition performance of the real-valued tensor while maintaining iteration stability. The numerical results are presented to demonstrate the superior performance, especially for high correlated and closely spaced targets in low-SNR scenario.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3491092