Deterministic compressed sensing LFM radar for range-Doppler estimation of multiple moving targets

Hanumanthu, Sudha; P, Rajesh Kumar

doi:10.1016/j.measurement.2021.110315

Cited by 3 publications

(2 citation statements)

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“…Figure 5 shows the procedure of the optimization algorithm. To retrieve the target information, an optimization algorithm was used to optimize sparse signals, such as Basis Pursuit Denoising (BPDN) and Least Absolute Shrinkage and Selection Operator (LASSO) provided by the Sparse LAB package [ 24 ]. They are equivalent methods but were developed by different research communities.…”

Section: Methodsmentioning

confidence: 99%

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A High-Resolution Imaging Method for Multiple-Input Multiple-Output Sonar Based on Deterministic Compressed Sensing

Gao,

Xu,

Yang

2024

Sensors

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Differences between conventional sonar and Multiple-Input Multiple-Output (MIMO) sonar systems arise in achieving high angular and range resolution. MIMO sonar uses Matched Filtering (MF) with well-correlated transmitted signals to enhance spatial resolution by obtaining virtual arrays. However, imperfect correlation characteristics yield high sidelobe values, which hinder accurate target localization in underwater imagery. To address this, a Compressed Sensing (CS) method is proposed by reconstructing echo signals to suppress correlation noise between orthogonal waveforms. A shifted dictionary matrix and a deterministic Discrete Fourier Transform (DFT) measurement matrix are used to multiply received echo signals to yield compressed measurements. A sparse recovery algorithm is applied to optimize signal reconstruction before joint transmit–receive beamforming forms a 2D sonar image in the angle-range domain. Numerical simulations and lake experimental results confirm the effectiveness of the proposed method, by obtaining a lower sidelobe sonar image under sub-Nyquist sampling rates as compared with other approaches.

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Section: Methodsmentioning

confidence: 99%

“…In the presence of noise, it is hypothesized that CS can also mitigate the inter-waveform cross-correlation levels. This enables the detection of two closely spaced targets with higher resolution compared with conventional MF [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%