| Title | Compressive Sampling Stepped Frequency Ground Penetrating Radar Using Group Sparsity and Markov Chain Sparsity Model |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Kafedziski, Venceslav |
| Conference Name | 2019 14th International Conference on Advanced Technologies, Systems and Services in Telecommunications (℡SIKS) |
| Date Published | oct |
| Keywords | A-scan, Antenna measurements, B-scan, buried object detection, composability, compressed sensing, compressive sampling, compressive sampling stepped frequency ground penetrating radar, Cyber-physical systems, Frequency measurement, Ground penetrating radar, Group Sparse, group sparsity, land mine detection, Markov chain, Markov Chain model, Markov chain sparsity model, Markov processes, Position measurement, privacy, pubcrawl, radar antennas, radar data, radar imaging, radar resolution, random antenna positions, random frequencies, Resiliency, sparse delays, sparse image, stepped frequency radar |
| Abstract | We investigate an implementation of a compressive sampling (CS) stepped frequency ground penetrating radar. Due to the small number of targets, the B-scan is represented as a sparse image. Due to the nature of stepped frequency radar, smaller number of random frequencies can be used to obtain each A-scan (sparse delays). Also, the measurements obtained from different antenna positions can be reduced to a smaller number of random antenna positions. We also use the structure in the B-scan, i.e. the shape of the targets, which can be known, for instance, when detecting land mines. We demonstrate our method using radar data available from the Web from the land mine targets buried in the ground. We use group sparsity, i.e. we assume that the targets have some non-zero (and presumably known) dimension in the cross-range coordinate of the B-scan. For such targets, we also use the Markov chain model for the targets, where we simultaneously estimate the model parameters using the EMturboGAMP algorithm. Both approaches result in improved performance. |
| DOI | 10.1109/℡SIKS46999.2019.9002342 |
| Citation Key | kafedziski_compressive_2019 |
Compressive Sampling Stepped Frequency Ground Penetrating Radar Using Group Sparsity and Markov Chain Sparsity Model