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.
Highlights
Unattended ground sensor (UGS) systems consist of a lot of sensor nodes and are usually employed for battlefield situation awareness through detection of seismic, acoustic and infrared signals emitted by moving targets [1]
This paper has presented a new wavelet packet manifold (WPM) signature by combining the wavelet packet node energy and neighborhood preserving embedding (NPE) algorithm of manifold learning for a better representation of moving seismic targets
WPM-based classification method enables seismic sensor nodes to carry out precise classification, it does not require the target to be at a certain range from the sensor nodes or a very homogenous underlying geology conditions
Summary
Unattended ground sensor (UGS) systems consist of a lot of sensor nodes and are usually employed for battlefield situation awareness through detection of seismic, acoustic and infrared signals emitted by moving targets [1]. Laplacian Eigenmap [20], locally linear embedding (LLE) [21], and ISOMAP [22], etc These methods yield impressive results on some benchmark artificial data sets, besides some real applications, their nonlinear properties make them computationally expensive [23]. Wavelet packet manifold classification (WPMC) is developed for seismic target recognition. The WPMC is produced by the three following steps: first, wavelet packet transforms are performed on seismic signals and WPNE is obtained. Since through the combination of manifold learning and wavelet packet transform, distinctive features are obtained, the classifier is less important and implemented.
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