scholarly journals Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 665
Author(s):  
Paul Berry ◽  
Ngoc Hung Nguyen ◽  
Hai-Tan Tran
Keyword(s):  
Compressive Sensing ◽  
Matching Pursuit ◽  
Target Recognition ◽  
Radar Data ◽  
Effective Bandwidth ◽  
Sparse Reconstruction ◽  
Range Resolution ◽  
Combining Data ◽  
Temporal And Spatial ◽  
High Range

The problem of obtaining high range resolution (HRR) profiles for non-cooperative target recognition by coherently combining data from narrowband radars was investigated using sparse reconstruction techniques. If the radars concerned operate within different frequency bands, then this process increases the overall effective bandwidth and consequently enhances resolution. The case of unknown range offsets occurring between the radars’ range profiles due to incorrect temporal and spatial synchronisation between the radars was considered, and the use of both pruned orthogonal matching pursuit and refined l 1 -norm regularisation solvers was explored to estimate the offsets between the radars’ channels so as to attain the necessary coherence for combining their data. The proposed techniques were demonstrated and compared using simulated radar data.

Radar Target Recognition Based on Sparse Representation of Dictionary Optimized

2014 ◽  
Vol 701-702 ◽  
pp. 433-436
Author(s):  
Pei Pei Duan ◽  
Hui Li ◽  
Qi Li
Keyword(s):  
Matching Pursuit ◽  
Target Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Radar Target ◽  
Redundant Dictionary ◽  
Range Resolution ◽  
Radar Target Recognition ◽  
Training Samples ◽  
High Range

The high range resolution profile samples are numerous and sparse. But less radar target recognition algorithms based on high range resolution profiles (HRRP) employed the sparseness of HRRP samples. A new radar target recognition algorithm using a fast sparse decomposition method is presented here. This algorithm was to be carried out in three major steps. First, the Gabor redundant dictionary was partitioned according to its atom characteristics to decrease the atoms storage. Then, the matching pursuit algorithm was improved by the genetic algorithm and the fast cross-correlations calculation to accelerate training samples decomposition and generate the taxonomic dictionaries. Finally, the reconstruction errors of testing samples were used to recognize different radar targets. The simulations show that this method can resist noise disturbs and its recognition rate is high.

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