Enhanced Target Identification Using Higher Order Shape Statistics
Interim rept. Jan-May 1997
ARMY RESEARCH LAB ADELPHI MD SENSORS AND ELECTRON DEVICES DIRECTORATE
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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 acousticseismic 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 acousticseismic sensor fusion are presented in this report.
- Acoustic Detection and Detectors
- Seismic Detection and Detectors