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Erschienen in: Soft Computing 14/2020

05.12.2019 | Methodologies and Application

A passive detection algorithm for low-altitude small target based on a wavelet neural network

verfasst von: Conghui Cao, Qun Hou, Thomas Aaron Gulliver, Qiang Lan

Erschienen in: Soft Computing | Ausgabe 14/2020

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Abstract

A passive detection algorithm is presented for multiple low-altitude small targets, which employs a wavelet neural network (WNN). The slope, kurtosis, and skewness are employed as the features for low-altitude small target detection, and an algorithm is given to determine the number of targets. A WNN is used to establish a relationship between signal classes and the signal characteristics using training signals. Then, signals are classified as either target present or target not present using the WNN. Indoor data from a research laboratory and outdoor data from a bridge in the Jimo District, Qingdao, were used for training and evaluation. The performance results show that the error rate with the proposed WNN-based algorithm is better than those based on the slope, skewness, and kurtosis of signal. Furthermore, the proposed algorithm is better than those based on other neural networks such as BPNN, RBFNN, SOMNN, and SVM. At a distance of 3 km, the recognition rate is greater than 84%, which is better than other techniques such as visual recognition, acoustic, and active radar.

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Metadaten
Titel
A passive detection algorithm for low-altitude small target based on a wavelet neural network
verfasst von
Conghui Cao
Qun Hou
Thomas Aaron Gulliver
Qiang Lan
Publikationsdatum
05.12.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 14/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-019-04574-3

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