<title>Feature extraction and fusion of acoustic and seismic sensors for target identification</title>

Abstract

The U.S. Army Research Laboratory (ARL) is developing an acoustic target classifier using a backpropagation neural network (BPNN) algorithm. Various techniques for extracting features have been evaluated to improve the confidence level and probability of correct identification. Some techniques used in the past include simple power spectral estimates (PSEs), split-window peak-picking, harmonic line association (HLA), principal component analysis (PCA), wavelet packet analysis,1'2'3'4 and others. In addition, improved results have been obtained when data are combined from other sensors co-located with the acoustic sensor. A three-axis seismic sensor has been configured as part of an acoustic sensor array that ARL uses on typical field experiments, with data collected and sampled simultaneously.The PSE, HLA, and shape statistic feature data are extracted from a group of vehicles and then split into testing and training files. The training file typically consists of 75 percent of the data set, and the performance of the trained neural network is evaluated with the remaining test data. Cross-validation is performed with vehicle data collected at different times of day and under various conditions. Results of the neural network from a few of the feature extraction algorithms under evaluation and from the fusion of the acoustic and seismic sensor data are presented.

Using sound to classify vehicle-terrain interactions in outdoor environments

“…al. [11] classified engine sounds by looking for harmonics in motor oscillations. This involves having a periodic sound source, which will manifest patterns over time.…”

“…al. [11] for equations.) We also compute the spectral rolloff, which is the frequency value under which a certain percentage of the total power lies.…”

“…al. [11] looks specifically at the distribution moments of the spectrum, and in following these methods, we create a 4D feature vector consisting of the spectral centroid, standard deviation, skewness and kurtosis.…”

A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots

“…The gianna and shape feature vector [11] is a combination of the gianna feature [20] and shape feature [21]. The gianna feature includes three features extracted from the time domain (ZCR, STE, and entropy) and three features from the frequency domain (spectral centroid, spectral roll-off, and spectral flux).…”

Classification of Vehicles Based on Audio Signals using Quadratic Discriminant Analysis and High Energy Feature Vectors

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