Compressive Sensing for Tomographic Imaging of a Target with a Narrowband Bistatic Radar
Abstract
:1. Introduction
- A new formulation for radar tomography based on sparse inversion is introduced. The main idea is to construct a dictionary of signal prototypes by discretizing the illuminated scene of interest into a grid of discrete points. In this formulation, the received radar signal vector becomes linear to the unknown reflection vector to be estimated. This effectively casts the radar tomography problem under consideration to a sparse linear inverse problem, given that the illuminated scene of interest only contains a small number of dominant scatterers, as is often the case in practice. Such a formulation allows a high-quality image of the target to be obtained under the compressive sensing framework.
- A technical challenge for the tomography formulation based on sparse inversion is the dictionary mismatch problem, resulting from the fact that the true scatterers almost always do not coincide exactly with the dictionary grid. This dictionary mismatch problem has been known in the literature to significantly degrade the performance of conventional sparse reconstruction techniques [27,28]. To overcome this problem, we tried exploiting the use of the parameter-refined orthogonal matching pursuit (PROMP) algorithm [29] to solve the sparsity-based tomographic formulation. Compared to other conventional sparse reconstruction techniques, like the orthogonal matching pursuit (OMP) and convex optimization (see e.g., [30,31], and the references therein), PROMP has the advantage of being capable of dealing with the dictionary mismatch arising from off-grid scatterers by perturbing the dictionary atoms and allowing them to go off the grid. PROMP belongs to the greedy pursuit family which identifies the support of the solution in an iterative manner based on the level of correlation between the input data and the dictionary atoms. As a result, PROMP is computationally efficient and thus suitable for real-time operation.
- Performance evaluation studies involving both simulated and real data are presented to demonstrate the superior performance of the proposed sparsity-based tomography method over the conventional k-space tomography technique.
2. Signal Model
3. Sparse Inversion Formulation of Bistatic Radar Tomography
4. Parameter-Refined Orthogonal Matching Pursuit
5. Results
5.1. Results with Simulated Data
5.2. Results with Real Data
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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INPUT: . |
|
end for. |
OUTPUT: |
Algorithm | k-Space | OMP | PROMP |
---|---|---|---|
Averaged runtime (second) | 0.1561 | 0.0331 | 0.0799 |
Averaged number of iterations | − | 27.65 | 8.96 |
Averaged per-iteration runtime (second) | − | 0.0012 | 0.0088 |
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Nguyen, N.H.; Berry, P.; Tran, H.-T. Compressive Sensing for Tomographic Imaging of a Target with a Narrowband Bistatic Radar. Sensors 2019, 19, 5515. https://doi.org/10.3390/s19245515
Nguyen NH, Berry P, Tran H-T. Compressive Sensing for Tomographic Imaging of a Target with a Narrowband Bistatic Radar. Sensors. 2019; 19(24):5515. https://doi.org/10.3390/s19245515
Chicago/Turabian StyleNguyen, Ngoc Hung, Paul Berry, and Hai-Tan Tran. 2019. "Compressive Sensing for Tomographic Imaging of a Target with a Narrowband Bistatic Radar" Sensors 19, no. 24: 5515. https://doi.org/10.3390/s19245515
APA StyleNguyen, N. H., Berry, P., & Tran, H.-T. (2019). Compressive Sensing for Tomographic Imaging of a Target with a Narrowband Bistatic Radar. Sensors, 19(24), 5515. https://doi.org/10.3390/s19245515