ArticleSensors (Basel, Switzerland)2013
Seismic target classification using a wavelet packet manifold in unattended ground sensors systems.
Article in Sensors (Basel, Switzerland), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Seismic Signal Analysis Based on Variational Mode Decomposition and Hilbert Transform for Ground Intrusion Activity Classification.Sensors (Basel, Switzerland) · 2023Article
- Vehicle Counting and Moving Direction Identification Based on Small-Aperture Microphone Array.Sensors (Basel, Switzerland) · 2017Article
- A Low-Cost Energy-Efficient Cableless Geophone Unit for Passive Surface Wave Surveys.Sensors (Basel, Switzerland) · 2015Article
- A compact methodology to understand, evaluate, and predict the performance of automatic target recognition.Sensors (Basel, Switzerland) · 2014Article
- Design of small MEMS microphone array systems for direction finding of outdoors moving vehicles.Sensors (Basel, Switzerland) · 2014Article
- Dual roadside seismic sensor for moving road vehicle detection and characterization.Sensors (Basel, Switzerland) · 2014Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
One of the most challenging problems in target classification is the extraction of a robust feature, which can effectively represent a specific type of targets. The use of seismic signals in unattended ground sensor (UGS) systems makes this problem more complicated, because the seismic target signal is non-stationary, geology-dependent and with high-dimensional feature space. This paper proposes a new feature extraction algorithm, called wavelet packet manifold (WPM), by addressing the neighborhood preserving embedding (NPE) algorithm of manifold learning on the wavelet packet node energy (WPNE) of seismic signals. By combining non-stationary information and low-dimensional manifold information, WPM provides a more robust representation for seismic target classification. By using a K nearest neighbors classifier on the WPM signature, the algorithm of wavelet packet manifold classification (WPMC) is proposed. Experimental results show that the proposed WPMC can not only reduce feature dimensionality, but also improve the classification accuracy up to 95.03%. Moreover, compared with state-of-the-art methods, WPMC is more suitable for UGS in terms of recognition ratio and computational complexity.
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Registered trials
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