Research on Bionic Processing for Auto-adaptive Radar Waveform
|Course||Nuclear Science and Technology|
|Keywords||adaptive hybrid waveform bionic signal processing auditory system model spectrogram correlation spectrogram transformation|
High-resolution range profiles are used to extract target features for recognition and classification. The traditional method for imaging is using matched filter for LFM (Linear Frequency Modulated) pulse compression. The performance of compression is affected by distortion and Doppler frequency shift of the echo. The method is not robust. In the thesis, a bionic waveform design and signal processing method is introduces into imaging radar signal detection and the performance is further improved against traditional methods.A velocity selected imaging method for multi-scale high-resolution range profile is proposed based on bats’echolocation system. Adaptive hybrid waveform and intelligent bionic signal processing are two important characteristics of the system. The emissions are adaptive hybrid waveform containing CF (Constant Frequency) and FM (Frequency Modulated) signals in a single pulse. The CF component is used to get speed information of the target while FM is for fine detection and imaging. The Doppler frequency shift getting from CF processing is used for compensation of FM signal processing and also a speed gate for imaging. The waveform design is adaptive because in searching phase, the pulse period is long with most CF component and gets shorter and shorter with more FM component in tracking phase for real-time imaging; The intelligent bionic signal processing model based on auditory nervous contains two major blocks called spectrogram correlation and spectrogram transformation. These two blocks are used to get low and high resolution range profiles respectively for multi-scale detecting. In the spectrogram correlation block, a cochlear filter bank is adopted for encoding the bats’transmission and multiple echoes, in which the input is divided into several parallel pathways. Each frequency channel consists of an inner hair for half-wave rectification, followed by a stellate cell for low-pass filtering and peak-detection, and then a coincidence cell used as cross-correlator. The outputs of all frequency channels are fused in the auditory nervous centre to get the absolute range of the targets. The spectrogram transformation takes place across all frequency channels to improve range resolution and reconstruct fine range structure of the target.Bionic modeling and signal processing method for each block are studied in this thesis. The model is then simulated with MATLAB to check the correctness of the model.and the simulation results imply that the bionic method is more robust and flexible compared with traditional methods with higher resolution. At last the model is realized in FPGA (Field Programmable Gate Arrays). The FPGA hardware description code for this model is developed in Simulink@MATLAB based on System Generator for DSP toolbox and then downloaded to Xilinx Virtex-5XCV5LX110 FPGA. The experiment results improve that it is hardware realizable for the bionic model in a single FPGA chip and real-time processing can be achieved.