Enhanced Target Identification Using Higher Order Shape Statistics
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 (ID). 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, and others. In addition, improved classification results have been obtained when shape statistic features derived from HLA feature sets or seismic PSE features have been incorporated in BPNN training, testing, and cross-validation. The combined acoustic/seismic data from collocated acoustic and seismic sensors are gathered by a three-axis seismic sensor. This is configured as part of an acoustic sensor array that ARL uses on typical field experiments. The PSE, HLA, and shape statistic feature (SSF) data are extracted from a set of vehicles and then split into a testing and training file. The training file typically consists of 75 percent of the whole data set, and the performance of the trained neural network is evaluated using the remaining test data, and further cross-validation is performed with vehicle data collected at different times of day and various operating conditions. Results of the neural network from a few of the feature extraction algorithms currently under evaluation and from the acoustic/seismic sensor fusion are presented in this report.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 1999
- Accession Number
- ADA361281
Entities
People
- Mark Wellman
- Nassy Srour
Organizations
- United States Army Research Laboratory