Study On Blind Detection Technologies Of Communication Signals

The blind detection of the communication signals is a very important function of the intelligent receiver based on Software Defined Radio (SDR), especially in the case of non-cooperative communications where no a priori information can be used for surveillance and interception.This dissertation is devoted to a study of key technologies of the blind detection of the communication signals, including the presence detection, blind parameter estimation and recognition of the specific signals. The achievements presented in this paper are a part of a large scale army project of research undertaken by the lab the author works with. The contributions obtained in this thesis can be summarized in the following five aspects.1. In the aspect of presence detection, the emphasis is laid on the blind detection of signals with short burst and/or with poor quality.A novel presence detection algorithm based on the fluctuation of the correlation function of the received signals in time domain is proposed. The algorithm, employing the first-order fluctuation function of the correlation function as the detection function and combining with the SDM-based adaptive threshold estimation method proposed in this paper, provides an effective solution to the detection problems of short burst or short burst-interval signals.A robust detection algorithm based on the spectral entropy of the short-time Fourier Transform (STFT) of the received signals, which is originally used in the speech signal processing, is proposed for the detection of signals under low Signal-to-Noise Ratio (SNR).2. In the aspect of blind SNR estimation, some algorithms independent of the parameters of the signals such as modulation type, carrier frequency and symbol rate etc. are addressed and discussed.A modified Generalized Split-Symbol Moment Estimator (GSSME) algorithm is proposed for the blind SNR estimation of MPSK signals, and the standard derivation of the estimated SNR is suggested as a measurement for indicating the end of an optimal iteration, which is a great difficulty in the original algorithm.The analytical expressions for the lower bounds of the mean and variance of the estimation are derived in detail for the subspace-based blind SNR estimator. In addition, simulations are performed for MQAM signals besides MPSK and MFSK signals, especially under negative SNR circumstance, expanding the applicable range of the original algorithm.A blind SNR estimator based on the modified Projection Approximation Subspace Tracking deflation (PASTd) algorithm is proposed. The orthogonality of the estimated eigenvectors is guaranteed by introducing the Gram-Schmidt orthogonization process into the original PASTd method. Compared with the eigenvalue decomposition-based method, the proposed algorithm can achieve a more accurate estimation with lower computational complexity. Furthermore, the algorithm needs neither the parameters of the received signals, such as the carrier frequency, symbol rate and modulation scheme, nor the synchronization of the system.3. In the aspect of blind symbol rate estimation, the investigation is targeted at the algorithms for signals with unknown modulation type and nonzero carrier frequency.A novel blind symbol rate estimation algorithm for CPFSK signals is proposed based on combination of Discrete Wavelet Transform (DWT) with Continuous Wavelet Transform (CWT). CWT is used for the extraction of detailed information of the signal obtained from the DWT, and greatly reduces the influence of the noise. Thus the proposed algorithm can get an accurate estimation in low SNR.A blind symbol rate estimator based on the wavelet multi-resolution analysis is proposed. The wavelet orthogonal multi-resolution decomposition is performed for the received signals and the symbol rate can be estimated via the spectrum of the correlation function of the detailed coefficients at different stages. The performance of the proposed algorithm is less influenced by the parameters of the received signals, such as modulation type and carrier frequency, thus is very suitable for the estimation before modulation identification.4. In the aspect of recognition of the target signals, the emphasis is put on the effective identification of single-path MPSK and MFSK signals.A matched recognition alogirthm irrespective of the modulation type of the received signals is proposed. Effective recognition of the short wave PSK and FSK signals can be implemented in a uniform flow chart by employing parameters, including symbol rate, modulation order and frequency interval, as the template parameters.A method for peak-value-search, named as difference-threshold algorithm, is proposed, which converts the parameter estimation of MFSK signals, including the modulation order, frequency interval and carrier frequency, into a peak search problem of the Power Spectral Density (PSD) function or its higher-order counterparts.5. According to the requirements of the tasks the author undertaken in the project, a software platform for presence detection of burst signals and a software platform for matched recognition of target signals are designed and implemented respectively. Detailed experimental results with regard to various simulated signals as well as practical signals are provided which have proved the feasibility and effectiveness of the above two platforms.