The major drawback of existing Differential Chaos Shift Keying (DCSK) communication system is low transmission rate. To solve the problem, a Correlation Delay-Differential Chaos Shift Keying (CD-DCSK) communication scheme without inter-signal interference was proposed. At the transmitting side, two orthogonal chaotic signals were generated by an orthogonal signal generator and normalized by the sign function to keep the energy of the transmitted signal constant. Then, two chaotic signals and their chaotic signals with different delay time intervals were respectively modulated by 1 bit data information to form a frame of transmission signal. At the demodulation side, correlation demodulation was used to extract data information and the information bits were recovered by detecting the sign of correlator output. The theoretical Bit Error Rate (BER) performance of system under Additive White Gaussian Noise (AWGN) channel was analyzed by using Gaussian Approximation (GA) method, and was compared with classical chaotic communication systems. The performance analysis and experimental results indicate that, compared with DCSK system, the transmission rate of CD-DCSK system without inter-signal interference increases by 50 percentage points, and the BER performance of the proposed system is better than that of Correlation Delay Shift Keying (CDSK) system.

In order to improve the transmission performance of chaotic signals, a Multi-User Orthogonal Correlation Delay Shift Keying (MU-OCDSK) scheme was proposed based on Correlation Delay Shift Keying (CDSK) scheme and Multi-Carrier Correlation Delay Shift Keying (MC-CDSK) scheme. The multi-carrier was used to modulate chaotic signals. Compared with CDSK, the proposed scheme not only has higher spectral efficiency, but also improves bit error rate. Theoretical simulation and Monte Carlo simulation show that compared with MC-CDSK, the proposed scheme not only doubles the transmission rate, but also improves the bit error rate. The results of theoretical simulation and Monte Carlo simulation are consistent.

Monaural speech enhancement algorithms obtain enhanced speech by estimating and negating the noise components in speech with noise. However, the over-estimation and the error of the introduction to make up the over-estimation of noise power make detrimental effect on the enhanced speech. To constrain the distortion caused by noise over-estimation, a time-frequency mask estimation and optimization algorithm based on Computational Auditory Scene Analysis (CASA) was proposed. Firstly, Decision Directed (DD) algorithm was used to estimate the priori Signal-to-Noise Ratio (SNR) and calculate the initial mask. Secondly, the Inter-Channel Correlation (ICC) factor between noise and speech with noise in each Gammatone filterbank channel was used to calculate the noise presence probability, the new noise estimation was obtained by the probability combining with the power spectrum of speech with noise, and the over-estimation of the primary estimated noise was decreased. Thirdly, the initial mask was iterated by the optimization algorithm to reduce the error caused by the noise over-estimation and raise the target speech components in the mask, and the new mask was obtained when the iteration stopped with the conditions met. Finally, the optimization method was used to optimize the estimated mask. The enhanced speech was composed by using the new mask. Experimental results demonstrate that the new mask has higher Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility measure (STOI) values of the enhanced speech in comparison with the mask before optimization, improving the intelligibility and listening feeling of speech.

Concerning the blind estimation of the combination code sequence of Time Division Data Modulation-Binary Offset Carrier (TDDM-BOC) modulation signal under low Signal-to-Noise Ratio (SNR), a new method based on Sanger Neural Network (Sanger NN), a kind of multi-principal component neural network, was proposed. Firstly, the segmented TDDM-BOC signal was used as input signal, and the weight vectors of multi-feature components of the segmented TDDM-BOC signal were adaptively extracted by Sanger NN algorithm. Secondly, the weight vectors were trained repeatedly until convergence by continuously inputing segmented TDDM-BOC signal. Finally, the combination signal code sequence was rebuilt by the symbolic function of each weight vector, thus realizing the blind estimation of the TDDM-BOC signal. Furthermore, an optimal variable step method was used in Sanger NN algorithm to greatly improve the convergence speed. Theoretical analysis and simulation results demonstrate that the Sanger NN algorithm can achieve blind estimation of the TDDM-BOC combined code sequence with low SNR of -20.9~0 dB, and its complexity is significantly lower than that of Singular Value Decomposition (SVD) and on-line unsupervised learning neural network for adaptive feature extraction via principal component analysis (LEAP). Although the number of data group required for the convergence of Sanger NN algorithm is larger than that of LEAP algorithm, but the convergence time of Sanger NN is lower than that of LEAP algorithm.

To estimate pseudo code period of multipath tamed spread spectrum signal, a blind method based on power spectrum reprocessing was proposed to estimate the pseudo code period of the tamed spread spectrum signal in multipath channel. Firstly, the general single path tamed spectrum signal was extended to multipath model. Then, the primary power spectrum of the signal was calculated on the basis of the tamed spread spectrum signal model in multipath environment. Next, the obtained primary power spectrum was used as the input signal to calculate the secondary power spectrum of the signal, and the theoretical analyses showed that the peak line of the secondary power spectrum of the signal would appear in the integral multiple of the pseudo code period. Finally, the pseudo code period of the tamed spread spectrum signal could be estimated by detecting the spacing between the peak spectrum lines. In the comparison experiments with time domain correlation method, the Signal-to-Noise Ratio (SNR) of the proposed method was improved by about 1 dB and 2 dB when the correct rate of pseudo code period was 100% and the length of pseudo code sequence was 127 bits and 255 bits, and the average accumulation times of the proposed method was less under the same condition. The experimental results show that the proposed method not only has less computational complexity, but also improves the estimation correct rate.

Concerning the problem of modulation identification in the Rayleigh channel, a new algorithm based on cumulants was proposed. The method was efficient and could easily classify seven kinds of signals of BPSK (Binary Phase Shift Keying), QPSK (Quadrature Phase Shift Keying), 4ASK (4-ary Amplitude Shift Keying), 16QAM (16-ary Quadrature Amplitude Modulation), 32QAM (32-ary Quadrature Amplitude Modulation), 64QAM (64-ary Quadrature Amplitude Modulation) and OFDM (Orthogonal Frequency Division Multiplexing) by using the decision tree classifier and the feature parameters that were extracted from combination of four-order cumulant and six-order cumulant. Through theoretical derivation and analysis, the algorithm is insensitive to Rayleigh fading and AWGN (Additive White Gaussian Noise). The computer simulation results show that the successful rates are over 90% when SNR (Signal-to-Noise Ratio) is higher than 4dB in Rayleigh channel, which demonstrates the feasibility and effectiveness of the proposed algorithm.

To solve the problem of the Orthogonal Frequency Division Multiplexing (OFDM) broadband signal processing, an algorithm for the Direction of Arrival (DOA) estimation of OFDM signal based on the broadband focused matrix and higher-order cumulant was introduced. In the former algorithm, broadband array data was broken down into several narrowband signals by Fourier transform, the direction matrices under different frequence bands were transformed to the same reference frequency by a focused matrix, and then with the Multiple Signal Classification (MUSIC) algorithm DOA was estimated. In the higher-order cumulant algorithm, through the focus operation, array output vectors at different frequency bins were transformed to focusing frequency and individual cumulant matrix was got. Each cumulant matrix was made weighted average and eigen-decomposition, and then the MUSIC algorithm was applied to estimate DOA. Theoretical analysis and simulation results show that the two methods are able to accurately estimate DOA of OFDM signal, the spatial resolution of four-order cumulant method is better than the focusing matrix method. The four-order cumulant expanded the array aperture, and it also has good adaptability when the Signal-to-Noise Ratio (SNR) is low.

According to the blind extracting issues within the spread-spectrum watermark, a kind of blind extracting algorithm which could be used in the extraction of the digital audio signals was proposed. In the algorithm, wavelet transform was applied to the audio document, then the Discrete Cosine Transform (DCT) was used to its low-frequency coefficient. Afterwards, the fifth coefficient was got and it was used to hide the watermark information being spectrum spread. As the spread-spectrum sequence and its length were unknown during the extraction, spectrum-reprocessing and Singular Value Decomposition (SVD) were introduced to estimate the spread-spectrum using in the embedding process, and the blind extraction to the spread-spectrum watermark of the given digital signal was fulfilled. The simulation results show that with unknown spread-spectrum parameter, watermark image with Normalized Coefficient (NC) of one can be extracted, and it is of strong robustness. Under the attacks of noises and low-pass filter, the accuracy rate of the estimating spread-spectrum sequence is over 90%, which guarantees the recovery of clear water mark image with normalization coefficient higher than 0.98.