A Weak Signal Detection Method Based on Artificial Fish Swarm Optimized Matching Pursuit

Abstract

To detect weak signals is difficult in signal processing and is very important in many areas such as non-destructive evaluation (NDE), radar etc. Sparse signal decomposition from overcomplete dictionaries are the most recent technique in the signal processing community. In this paper, this technique is utilized to cope with ultrasonic weak flaw detection problem. But its calculation is huge (NP problem). A new improved matching pursuit algorithm is proposed. The mathematical model of searching algorithms based on artificial fish swarm is established; the artificial fish swarm with the advantages of distributed parallel searching ability, strong robustness, good global astringency, and insensitive preferences are employed to search the best matching atoms. It can reduce complexity of sparse decomposition and space of memory. Experimental results shows that the amplitude, frequency and initial phase parameters of ultrasonic signal blurred by strong noise can be estimated according to the proposed algorithm, and the expected weak signal can be then reconstructed. When this method is used in the ultrasonic flaw detection, compared with the wavelet entropy and wavelet transform, the results show that the signal quality and performance parameters are improved obviously. 2009 World Congress on Computer Science and Information Engineering 978-0-7695-3507-4/08 $25.00

Current progress on weak signal detection

“…Scholars found a solution for calculation problem. QI et al [17] applied the MP algorithm of artificial fish optimization to the detection of ultrasonic weak fault signals, which solved the low efficiency problem of the original MP algorithm. It can reduce the complexity of the sparse decomposition and save storage space of over-complete dictionary.…”

A Review of Artificial Fish Swarm Optimization Methods and Applications

“…When this method is used in the ultrasonic flaw detection, compared with the wavelet entropy and wavelet transform, the results show that the signal quality and performance parameters are improved obviously [64].…”

“…Experimental results shows that the amplitude, frequency and initial phase parameters of ultrasonic signal blurred by strong noise can be estimated according to the proposed algorithm and the expected weak signal can be then reconstructed. When this method is used in the ultrasonic flaw detection, compared with the wavelet entropy and wavelet transform, the results show that the signal quality and performance parameters are improved obviously [64].…”