A Support Set Stabilization Method for MIMO Radar Sparse Imaging in Displacement Measurement

JIN Liangnian; HUANG Bosun

doi:10.11999/JEIT260470

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JIN Liangnian, HUANG Bosun. A Support Set Stabilization Method for MIMO Radar Sparse Imaging in Displacement Measurement[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260470

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JIN Liangnian, HUANG Bosun. A Support Set Stabilization Method for MIMO Radar Sparse Imaging in Displacement Measurement[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260470

JIN Liangnian, HUANG Bosun. A Support Set Stabilization Method for MIMO Radar Sparse Imaging in Displacement Measurement[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260470

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A Support Set Stabilization Method for MIMO Radar Sparse Imaging in Displacement Measurement

doi: 10.11999/JEIT260470 cstr: 32379.14.JEIT260470

JIN Liangnian^{1, 2
,
,},
HUANG Bosun¹

1.
Institute of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
2.
XXXXXXXXXX, Guilin 541004, China

Funds: The National Natural Science Foundation of China(62661017), Guangxi Key Research and Development Program (Guike FN2600640261, Guike AB23075161), Supported by the Project of Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory(GXKL06240110)

Accepted Date: 2026-10-16
Rev Recd Date: 2026-10-16

Available Online: 2025-10-25

Abstract

Abstract

Objective FMCW-MIMO radar provides a non-contact approach for full-field displacement measurement by combining sparse imaging with inter-frame phase-difference estimation. Compared with conventional point-based measurement methods, radar imaging enables displacement information to be obtained over multiple spatial targets simultaneously. However, the accuracy of phase-based displacement estimation strongly depends on the consistency of the reconstructed target locations between consecutive frames. Existing sparse imaging methods may produce fluctuations in the reconstructed support set because of sidelobe leakage, target omission, and mainlobe drift, particularly under low-SNR conditions. Such support-set instability introduces phase errors and causes discontinuities or fluctuations in the estimated displacement. Therefore, a sparse imaging method capable of maintaining a stable support set while suppressing sidelobe interference is developed for high-precision MIMO radar displacement measurement.Methods A UNet-enhanced ADMM (UNetE-ADMM) algorithm is proposed by embedding a UNet gain module into the proximal operator of ADMM. Spatially adaptive normalized weights are generated from imaging features to distinguish target mainlobes from sidelobes. The mainlobes are preserved while sidelobes are progressively suppressed, allowing the support set to be stabilized during iterative reconstruction. The reconstructed complex scattering coefficients are subsequently used for inter-frame phase-difference estimation and radial displacement measurement. A support-set stability criterion and displacement-error model are further established to analyze the effects of mainlobe drift, grid mismatch, and inter-Tx motion.Results and Discussions Simulations are conducted using a cascaded AWR2243 MIMO radar configuration with a 77-GHz carrier frequency, 3.65-GHz bandwidth, and 50-Hz frame rate (Table 2). For distributed point targets under SNRs from −10 to 10 dB, the proposed method produces more stable target locations and lower sidelobe levels than L1-ADMM and PnP-UNet (Fig. 9). At −10 dB, the mainlobe drift probability is reduced from 37.5% for L1-ADMM to 6.3%, corresponding to an 83.2% reduction, while the displacement RMSE decreases from 44.62 to 1.54 μm (Table 3). In the vibration experiment with two corner reflectors, the proposed method achieves displacement RMSEs of 185 and 18 μm for the moving and stationary targets, respectively, compared with 2247 and 122 μm for L1-ADMM (Table 4). In the human vital-sign experiment, the reconstructed chest displacement is smoother (Fig. 16), and the heartbeat spectral peak is more distinguishable (Fig. 17), while the measured heart rates remain consistent with the reference measurements (Table 5). The convergence and target-background-ratio results further demonstrate effective sidelobe suppression and stable iterative reconstruction (Fig. 18).Conclusions The proposed UNetE-ADMM method integrates learned target structural priors into ADMM sparse imaging and improves support-set stability through adaptive mainlobe preservation and sidelobe suppression. Experimental results demonstrate reduced mainlobe drift and improved displacement accuracy compared with conventional L1-ADMM and PnP-UNet, particularly under low-SNR conditions. The method is also applicable to both discrete targets and continuous structures. Grid mismatch, computational cost associated with dense imaging grids, and generalization to more complex scenes remain to be further investigated.
- Displacement measurement,
- MIMO radar,
- Sparse imaging,
- ADMM,
- UNet

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References(25)

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