As one of the main types of spread spectrum communication, Frequency-Hopping(FH) communication has become an important means of counterreconnaissance and anti-jamming in military field, and been widely applied in military communications, because of its anti-jamming performance, networking capability, low probability of interception and inherent security features. The application of FH technique is a great challenge to carry on radio reconnaissance. The processing technique of FH signals is prosperous in the communication countermeasure domain. This paper is aming to investigate the following four problems according to two different factors(the number of sensors and the types of sample): 1) the parameter estimate of traditional samples of FH based on single-sensor; 2) the parameter estimate of compressed samples of FH based on single-sensor; 3) the online processing of multiple FH based on multi-sensors; 4) FH network sorting based on underdetermined blind source separation. The main content of the dissertation are detailed as follows:In chapter 2, the problem of hopping period estimation of FH signals is investigated. An algorithm of hop period estimation is developed based on the time-frequency pattern modification. Firstly, the optimization problem model is established to estimate the TF pattern, according to the dual TF sparsity of FH signals. Then, matching search algorithm is developed to obtain the solution of the optimization problem, and the clear spectrogram is gotten. Finally, the hopping period is estimated by clustering algorithm. Simulation results show that the performance of this method is superior to the current existing method for estimating hopping period. Moreover, the proposed method can adapt to multiple FH signals.In chapter 3, the problem of time frequency analysis for compressed sampling FH signals and hop timing estimation is investigated. A novel time-frequency analysis method based on sparse representation is developed, which can get clear and concentrated time-frequency representation. Then, a novel method for estimating hop timing for FH signals precisely based on improved orthogonal matching pursuit(IOMP) algorithm is proposed. The both methods all exploit the time-frequency sparse of FH signals sufficiently. Firstly, the unconstrained sparse representation model of FH signals is established according to the punish function theory. Then, the guideline of punish parameters are analysed theoretically and get time-frequency representation by sloving the optimization problem used approximate l0 norm finally. Finally, the coarse hop period and hop frequencies can be obtained from the pattern. Based on the coarse hop period and hop frequencies, the sparse representation model for hop timing estimation is established, and the IOMP is used to solute the model and get hop timing. The proposed method is capable of getting clear time-frequency pattern and obtaining precise hop timing quickly, especially for wide-band FH signals. The simulation results show the effectiveness of the proposed algorithms.In chapter 4, the problem of online processing of multiple FH signals is investigated based on receiving antenna arrays. This paper proposes a real-time algorithm to detect hop timing and estimate frequencies and DOAs of multiple FH signals with antenna arrays for synchronous or asynchronous networks. The technique of particle filtering is introduced to obtain the array responding vectors of the incident signals first, and then recover the signal waveforms to estimate the signal frequencies. After that, the responding vector and signal frequency estimates are combined to get DOAs. In order to realize online hop timing detection, the ARMA model based method is used to detect frequency hoppings online. Then the sorting of asynchronous network is implemented by exploiting the frequency continuity of the hop-free signals at each hop instant, and that of synchronous network is implemented according to the distinguishable and inactive signal directions. The method is capable of adapting to arrays with random geometry, that only requestes knowing the number of networks. The simulation results show the effectiveness of the proposed method.In chapter 5, the problem of FH signals sorting in underdetermined case is investigated. This paper proposes an algorithm to sort FH signals based on UBSS TF representation. Considering the TF sparsity of FH signals, the method of mixing matix estimation based on TF ratio matrix clustering and the method of sources separation based on subspace projection are improved. To eatimate the mixing matix, firstly, calculate the TF ratio matix of the mixtures on the whole TF support points, then preprocess the TF ratio matix to eliminate the wild vetors, finally the k-means clustering algorithm is developed to estimate the mixing matrix and calculate the comparative powers. To separate FH networks, the subspace projection algorithm is improved by combining the comparative powers. The proposed methods provide better performance in mixing matix estimation, and relax the sparsity condition of the sources in the TF domain since the number of the FH signals that exist at any TF point simultaneously is allowed to equal that of the sensors. Moreover, the proposed methods have the similar computational complexity compared with previous algorithms. The simulation results show the effectiveness of the proposed methods. |