Advanced Signal Processing Techniques for BAT-Class MMW Seekers

Among the most demanding problems for millimeter wave (MMW) radar guided missiles such as that being developed for the Brilliant Antitank (BAT) program is the detection and classification of stationary ground targets in clutter rich environments for autonomous acquisition and terminal homing. This mssion must be met with a high probability of successful classification and a minimal false target density (FTD) to best utilize interceptor resources. The threat for these missions consists of the modern tank formation, assembly areas, choke points, and high-value targets. In addition, it is desirable to commit resources against tanks rather than lower-priority vehicles such as trucks. More sophisticated signal processing techniques are required to detect and classify stationary targets with high probability in a clutter-rich environment (including snow) and to perform aimpoint selection and biasing. Nontraditional techniques include the use of wavelet transformations, fractal-based feature extraction, and neural network processing. Specifically, a MMW seeker will be designed and simulated that will include a wavelet transform processor, a detection and segmentation subsystem, a feature extractor that incorporates a fractal dimensionality feature, and a neural network classifier. Collected data (such as collected under the MLRS/TGW program) will be used to test the system.