Research On Wideband High Speed Signal Detection And Frequency Domain Measurement Technology

Electronic support is a special kind of military reconnaissance means to obtain the enemy’s military intelligence, which plays an extremely important role in the field of radar counter-measure. With the rapid development of radar technology, the number of radars continues to grow. In the modern electronic warfare environment, the radar signal becomes more and more intensive, and the signal form becomes more and more complex. Signal bandwidth is wider and wider, and the rate is higher and higher, which brings great pressure to the signal detection and parameter estimation on the basis of traditional Nyquist sampling theory. With the development of the signal to the broadband and high speed, the traditional signal processing methods will not be able to meet the needs of the future battlefield due to the electronic bottleneck of the sampling device.In order to avoid the high speed sampling of wideband high speed signals, domestic and foreign scholars have done some research in recent years. Among them, the application of reducing the system sampling rate based on compressed sensing theory and microwave photonics has attracted wide attention. Under compressed sensing theory, one can sample a signal at a rate far below the Nyquist rate and fulfill the task of signal detection and parameter estimation with the few data based the sparsity of the signal. Microwave photonic technology combined with photonic technology and microwave technology, can implement wide bandwidth, low loss, electromagnetic interference immunity microwave signal processing system. The relevant analysis and research is done in this paper to improve the performance of wideband linear frequency modulated signal detection and parameter estimation in complex electromagnetic environment under the framework of compressed sensing and to explore the application of optical sampling technology in the instantaneous frequency measurement of high frequency microwave signals. Some solutions are proposed in this paper.The main contributions and research content of this paper is as follows:1. Aiming at the problem that the detection performance of LFM signal detection algorithm based on waveform matched dictionary under compressed sensing framework is greatly influenced by white Gauss noise, a LFM signal detection algorithm based on fractional Fourier transform (FrFT) dictionary is proposed. The algorithm is based on the energy accumulation characteristics of LFM signal in the FrFT domain. First, the FrFT matrix is constructed as the sparse dictionary of the signal. Then the coefficient vector of the signal in the FrFT dictionary is partially reconstructed by using the orthogonal matching pursuit algorithm. Finally, the purpose of signal detection is achieved through the detection decision of the coefficient vector. The experimental results show that compared with the algorithm based on waveform matched dictionary, the proposed algorithm can obtain a higher success probability of detection with less compression sampling points and lower signal-to-noise ratio (SNR) conditions.2. Aiming at the problem that the performance of LFM signal detection algorithm based on FrFT dictionary under the compressed sensing framework is greatly influenced by the narrow band interference signal, a LFM signal detection algorithm based on cascade dictionary is proposed by introducing the morphological component analysis method. First, a redundant dictionary is constructed by the cascade of the FrFT dictionary and the FFT dictionary, then the coefficient vector of the signal in the redundant dictionary is partially reconstructed by using the orthogonal matching pursuit algorithm. Ultimately, the signal and interference are separated. The experimental results demonstrate that the algorithm can effectively suppress the narrow band interference signal, and compared with the algorithm based on FrFT dictionary, the algorithm can obtain higher signal success probability of detection under the condition of the presence of narrow band interference signal.3. Aiming at the problem that the parameter estimation performance of LFM signal parameter estimation algorithm based on waveform matched dictionary under compressed sensing framework is greatly influenced by white Gauss noise and heavy narrow band interference signal, a two-step search strategy algorithm is proposed based on FrFT dictionary. The algorithm obtains the optimal fractional Fourier transform order of the signal through a coarse search and a fine search, and then the chirp rate and the initial frequency of the LFM signal are obtained. The experimental results show that compared with the algorithm based on waveform matched dictionary, the proposed algorithm can obtain a higher success probability of parameter estimation with less compression sampling points and lower SNR conditions.4. Aiming at the problem that the running time of the coarse search step of the two-step search strategy parameter estimation algorithm based on FrFT dictionary is relatively long, an improved two-step search strategy algorithm is proposed. The algorithm is based on the sparsity of LFM signal both in the time-frequency domain and in the FrFT domain. Firstly, the transform order is coarsely searched in the time-frequency domain. Then, the transform order is finely searched in the FrFT domain according to the coarse value of the order. Finally, the chirp rate and initial frequency are obtained. The experimental results show that compared with the algorithm based on FrFT dictionary, the running time of the coarse search step of the proposed algorithm is significantly reduced, and compared with the algorithm based on waveform matched dictionary, the proposed algorithm can also obtain a higher success probability of parameter estimation under the same compression sampling points and SNR conditions.5. Study on the instantaneous frequency measurement algorithm for high frequency microwave signals based on microwave photonics technique. A new instantaneous frequency measurement algorithm for high frequency microwave signals based on optical sampling is proposed. The algorithm modulates the high frequency microwave signal onto the low repetition rate optical sampling pulse using an optical intensity modulator, and then achieve the purpose of high frequency microwave signal optical sampling. The frequency of the under-sampled signal is accurately estimated through the combination of fast Fourier transform and chirp Z transform. The experimental results show that the proposed algorithm can accurately estimate the signal frequency in the 39 GHz bandwidth.