Target Identification by Radar Sensor Networks with Variable-Interval Sampling and Linear Interpolation

Linearly interpolated target features are used for target identification, including amplitude and complex features generated from multifrequency radar target returns in the resonance region. Based on the inverse Fast Fourier Transform, an efficient method for estimating the distance between a complex feature and an interpolated one is developed. Using a variable-interval re-sampling scheme, an algorithm is developed for condensing the reference sets for nearest-neighbor target identification. Two algorithms are developed for target identification by radar sensor networks using data fusing rules and the linearly interpolated features. Computer simulation results are presented that demonstrate the effectiveness of the proposed methods.