Data explosion and information redundancy are the main characteristics of the era of big data. Digging out valuable information from mass data is the premise of efficient information processing, which is a key technology in the area of object recognition with mass feature database. In the area of large scale image processing, both of the massive image data and the image features of high-dimension take great challenges to object recognition and information retrieval. Similar with big data, the large scale image feature database, which contains extensive quantity of information redundancy, can also be quantitatively represented by finite clustering models without degrading recognition performance. Inspired by the ideas of product quantization and high dimensional feature division, a data compression method based on recursive self-organizing mapping (RSOM) algorithm is proposed in this paper.
The micro-motion of ballistic missile targets induces micro-Doppler modulation on the radar return signal, which is a unique feature for the warhead discrimination during flight. In order to extract the micro-Doppler feature of ballistic missile targets, time-frequency analysis is employed to process the micro-Doppler modulated time-varying radar signal. The images of time-frequency distribution (TFD) reveal the micro-Doppler modulation characteristic very well. However, there are many existing time-frequency analysis methods to generate the time-frequency distribution images, including the short-time Fourier transform (STFT), Wigner distribution (WD) and Cohen class distribution, etc. Under the background of ballistic missile defence, the paper aims at working out an effective time-frequency analysis method for ballistic missile warhead discrimination from the decoys.