PERFORMANCE ENHANCEMENT OF TARGET RECOGNITION USING FEATURE VECTOR FUSION OF MONOSTATIC AND BISTATIC RADAR

This paper proposes a fusion technique of feature vectors that improves the performance of radar target recognition. The proposed method utilizes more information than simple monostatic or bistatic (single receiver) algorithms by combining extracted feature vectors from multiple (two or three) receivers. In order to verify the performance of the proposed method, we use the calculated monostatic and bistatic RCS of three full-scale aircraft and the measured monotatic and bistatic RCS of four scale-model targets. The scattering centers are extracted using one-dimensional FFT-based CLEAN and then used as feature vectors for a neural network classifier. The results show that our method has better performance than algorithms that solely use monostatic or bistatic data.