Abstract
Compressive Sensing (CS) provides a new perspective for addressing radar applications requiring large amount of measurements and long data acquisition time; both issues are inherent in through-the-wall radar imaging (TWRI). Most CS techniques applied to TWRI consider stepped-frequency radar platforms. In this paper, the impulse radar two-dimensional (2D) TWRI problem is cast within the framework of CS and solved by the sparse constraint optimization performed on time-domain samples. Instead of the direct sampling of the time domain signal at the Nyquist rate, the Random Modulation Preintegration architecture is employed for the CS projection measurement, which significantly reduces the amount of measurement data for TWRI. Numerical results for point-like and spatially extended targets show that high-quality reliable TWRI based on the CS imaging approach can be achieved with a number of data points with an order of magnitude less than that required by conventional beamforming using the entire data volume.
Highlights
Through-the-wall radar imaging (TWRI) is a topic of current interest due to the wide range of public safety and defense applications
Instead of the direct sampling of the time domain signal at the Nyquist rate, the Random Modulation Preintegration architecture is employed for the Compressive Sensing (CS) projection measurement, which significantly reduces the amount of measurement data for TWRI
The downrange resolution is restricted to c/2B, where c is the speed of light and B is the bandwidth of the signal, whereas the crossrange resolution is restricted to the diffraction tomography limit, which is related to the antenna array aperture size [10]
Summary
Through-the-wall radar imaging (TWRI) is a topic of current interest due to the wide range of public safety and defense applications. Gurbuz et al proposed a compressive sensing data acquisition and imaging method for step frequency continuous wave (SFCW) ground penetrating radar (GPR) [20], where the sparsity property and limited number of buried objects are successfully utilized for improved target detection and resolution. With the informative CS measurement data, the TWRI is solved as sparse constraint optimization problem which well-exploits the sparsity of the targets space and enables high-resolution, less-cluttered y lmq, air d 0 lmq, air φmq lmq, wall θmq rtm Figure 1: TWRI geometry.
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