Research On Radar Signal Sorting And Recognition Techniques In Complicated Environments

Radar signal sorting and recognition algorithm applied to passive radar seeker (PRS) is investigated in this dissertation, which is in order to improve the signal sorting and tracking capability of PRS in the dense, complicated and variational environments.The limitations of traditional signal sorting model are analyzed according to the design and implementation of radar signal sorting and tracking device in both software and hardware. And then an improved radar signal sorting model is developed. The functional modules of the improved model are discussed in detail, including the separation of simultaneous arriving signals, the extraction of pre-sorting parameters and the extraction of intra-pulse modulated feature of radar signals. Correspondingly, some solutions are provided. Meanwhile, simulations prove the efficiency and feasibility of these methods.Some new ideas and some novel methods based on blind signal process (BSP) are shown to dispose the simultaneous arriving signals. Firstly, a radar signal sorting algorithm based on the Fast Independent component analysis (Fast ICA) is given in the dissertation. This sorting algorithm can separate different modulated radar signals efficiently. Secondly, combined with global optimal blind source separation (BSS) algorithm, a new BSS algorithm is presented based on maximum pseudo-Signal Noise Ratio (SNR). The pseudo-SNR function, built as the objective function, is constructed by the covariance matrix of source signals and noise. This idea is formed based on the theory that SNR is maximal when source signals of statistical independence are completely separated. Then unmixing matrix could be obtained without any iteration, when the optimization of the objective function is transformed into solving Generalized Eigenvalue (GE) problem. This new algorithm is global optimal with low computational complexity, and the statistically independence of source signals can guarantee a feasible solution. Thirdly, a quasi switching algorithm of blind source separation is proposed based on switching algorithm. It uses the signal kurtosis as the judgement function, which is utilized to choose and weight the corresponding activation function adaptively. Compared with the original algorithm, this novel algorithm can be more effective in unknown multi-source separation of linear mixed signals.A new dynamic clustering pre-sorting algorithm is illuminated based on phase-only vector, considering that a single phase is trustless under the influence of noise and microwave antennas are multi-baseline or tri-dimensional-baseline, Firstly, it uses multiple channels of phases measured by broadband digital channelization receivers to construct the phase-only vector or AOA-only vector, which acts as clustering object. Afterwards, quasi-C-means dynamic clustering and sequence searching are adopted to complete the pre-sorting work to various radar signals. Simulation experiments show that, the dynamic clustering pre-sorting algorithm based on phase-only vector can achieve better performance than single phase method in low SNR.A spur track design of signal sorting is conceived based on polarization interferometer constructed by dual-polarized sinuous antenna (DPSA), due to polarization characteristic can be regarded as the attribute of radar signals, And an extraction of polarization parameters in frequency domain is constructed based on the digital channelization technology. The concept of polarization sorting is introduced into signal sorting of passive radar seeker firstly. Polarization sorting provides a brand-new and reliable approach to signal sorting in complicated environment, especially in sorting the signals with the same time arriving, signals with the same frequency and signals with the same phase.There are two feature extraction methods of intra-pulse modulated radar signal presenting in this dissertation, due to classical parameters can't describe the intra-pulse modulated radar signal accurately. One is the feature extraction method based on the filial generation of Instantaneous Frequency (IF); the other is the feature extraction method to envelope function of rotation angle a domain of radar signals based on Fractional Fourier Transform (FRFT). The first method pretreats the IF sequence of different modulated signals, and then extracts standard deviations, correlation coefficients with sample sequence and the extremums number of auto-correlation function to construct filial generation vector. Afterwards, it constructs an auto-sorting decision-making tree to verify the sorting performance of filial generation vector. The second method searches the envelope function of rotation angle domain based on FRFT firstly, extracts the rotation angle value a of envelope function's peak, the peak value and the kurtosis of the envelope function to construct a new feature vector. At the same time, a cluster sorting method based on the new feature vector is used to complete the radar signals sorting work. Computer simulation results verify the feasibility and effectiveness of these new feature vectors as the complement to classical parameters.Finally, combined with the problems in debugging digital channelization receiver and sorting processor, a new sorting model and method based on digital channelization technology is provided.