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In this study, a novel concept, i.e. the tuneable sigmoid cyclic correlation, is proposed, and a relative estimator, named the generalised sigmoid cyclic cross-ambiguity function, is also derived to deal with impulsive noise and co-channel interference. Based on these concepts, a robust joint estimation method for time delay and Doppler frequency shift is proposed as well. Both theoretical analysis and simulations demonstrate the superior performances of the proposed approach over other existing methods, especially under strong impulsive noise.

The underlying neural cognitive basis of brain-computer interfaces (BCIs) can be revealed by eventrelated potentials (ERPs) analysis, which provides another way of perceiving the extraordinary advancements in the BCI field. However, the stimulus onset asynchronies in BCIs are far shorter than those in traditional ERP studies. Moreover, strong periodic disturbances can be observed in averaged BCI signals; these cause difficulties in evaluating ERP component parameters such as amplitudes and latencies, which are required for ERP analysis. The mechanisms of such disturbances, as well as the methods to suppress them, are still unknown in the literature. In this study, an ERP signal isolation model of a BCI was built, which demonstrated that the overlapping effect of ERP components contributed to the periodic disturbances. To isolate ERP components from the contaminated signals, a Toeplitz method and difference-wave method were further proposed for the solution of the model. Experimental results showed that, by using the Toeplitz method, ERP components could be recovered from the averaged signal corresponding to the non-targets of the BCI; meanwhile, the difference wave exhibited a strong ability to suppress the overlapping effect, and ERP signals with relatively flat baselines could be obtained. These results validated the rationality of the proposed model. The study provides grounds for the explanation and suppression of the periodic disturbances in ERP analyses, while providing reasonable support for cognitive research of BCI.

In this paper, a novel algorithm for fractional time delay estimation is proposed. It is based on the maximum correntropy criterion and the Lagrange fractional delay filter (FDF). The instantaneous correntropy is introduced to measure the similarity between received signals and estimated ones in the proposed algorithm. It leads to an effective performance under both Gaussian and impulsive noises. The performances, including the convergence of the algorithm and the variance of the time delay estimation, are theoretically analyzed. Simulations demonstrate that the time delay estimation precision of the proposed algorithm is higher than that utilizing the mixed modulated Lagrange explicit time delay estimation (MMLETDE) especially under low generalized signal-to-noise ratio (GSNR). A novel factional time delay estimation algorithm is proposed.The proposed algorithm works well under both Gaussian and impulsive noises.The proposed algorithm has better signal adaptability.The convergence and the estimation variance of the algorithm are theoretically analyzed.

We extend the bearing estimation method for noncircular signals to the impulsive noise scenario which can be modeled as a complex symmetric alpha-stable (SαS) process. We define the extended covariation based matrix of the sensor outputs and show that it can be applied with subspace techniques to extract the bearing information from noncircular sources. Comprehensive simulations demonstrate that the proposed direction finding algorithm outperforms the traditional NC-MUSIC algorithm in the presence of a wide range of impulsive noise environments.

Time-frequency analysis is the important tool in signal processing, especially for non-stationary signals. When the interesting signals contaminated by impulsive noises, conventional time-frequency analysis method degenerate seriously because of lacking of limited second moment. In order to solve the degeneration of the estimation algorithm under non-Gaussian impulsive noise environment, the improved time delay estimation algorithm using fractional lower order Wigner-Ville distribution is proposed in this paper. Theoretical analysis and computer experiment demonstrates that the new algorithm has improved performance compared with conventional time-frequency analysis method.

The traditional HB-weighted time-delay estimation (TDE) method degenerates under the impulsive noise environment. Two new time-delay estimation methods are proposed based on fractional lower order statistics (FLOS) according to the impulsive characteristics of fractional lower order α-stable noises. Theoretic analysis and computer simulations indicate that the proposed covariation based HB weighted (COV-HB) algorithm can suppress impulsive noises in one received signal for 1 ≤α ≤ 2, whereas the other proposed fractional lower order covariance-based HB weighted (FLOC-HB) algorithm has robust performance under arbitrary impulsive noise conditions for the whole range of 0

The presence of non-Gaussian impulsive noise in wireless system can degrade the performance of existing equalizers and signal detectors. In this paper, the problem of blind source separation and equalization for MIMO systems in heavy-tailed impulsive noise is studied. A generalized multiuser constant modulus cost function by employing the fractional lower-order statistic of the equalizer input signal is proposed. The associated adaptive blind equalization algorithm based on a stochastic gradient descent method is defined as fractional lower-order multi-user constant modulus algorithm (FLOS MU CMA). Computer simulations are presented to illustrate the performance of the new algorithm.

Multipath time delay estimation is constrained by the width of the signal correlation function when using correlation based methods. This paper obtains a high resolution time delay estimation by introducing Burg algorithm and Marple algorithm of the maximum entropy power spectral estimation to non-resolvable multipath time delay estimatoin. The principles, the performaces and the results of computer simulation are given.

An LMS adaptive time delay estimation method with two windows is presented. This method can reduce the superfluous calculation greatly when the time of correlation is long. It is suitable for the time delay estimation of white band-limited random signals. The feasibility and the performances of this method are also studied.

The capability of CDMA system is restricted by MAI and Near-far effect problems. Two blind multi-user detector algorithms based on Improved-Infomax and FastICA are proposed and compared in the paper, which could detect target user effectively. These two detectors solve the order indeterminacy of ICA problem by initiating the demix vector with the target user's spreading codes sequence. Simulation results show that new detectors have good performances in BER(bit error rate), convergence speed and user capability by comparing with traditional method.

This paper presents a tumor detecting system that allows interactive 3D tumor visualization and tumor volume measurements. An improved level set method is proposed to automatically segment the tumor images slice by slice. PET images are used to detect the tumor while CT images make a 3D representation of the patient's body possible. An initial slice with a seed within the tumor is firstly chosen by the operator. The system then performs automatically the tumor volume segmentation that allows the clinician to visualize the tumor, to measure it and to evaluate the best medical treatment adapted to the patient.

In this paper we propose the analogy measurement of two random variables, and discuss the principle of maximizing nongaussianity of observed data to estimate independent components sequentially based on unsupervised learning neural network. We also prove the non-polynomial moment theorem in a generalized scheme, and reveal the feasibility that replaces the analogy measurement by the expectation of a non-quadratic smooth even function based on the theorem. A formula to compute the sign of above algorithm is given, which overcomes the contradiction between the objective function and the sign computation formula.

Evoked potential (EP) signals have been widely used to quantify neurological system properties. Delay in EP signals latency may be linked to neurological injuries. For abnormal cases where noises in EP signals usually appear impulsive, a novel robust approach, covariation correlation algorithm, is proposed to estimate EP latency delay based on fractional lower order statistics (FLOS) under alpha-stable distribution noise. Firstly, the auto-covariation of reference signal and cross-covariation between reference and detected signals are computed. Secondly, correlation between these two covariations series is calculated. Finally, the estimation value of EP latency delay is obtained from location of this correlation peak Simulation studies show that the estimation precise of this novel algorithm is higher than that of correlation or covariation algorithms especially under lower mixed signal noise ratio (MSNR).

The fractional Fourier transform can be considered as the generalization of Fourier transform. After the real cepstrum theory is reviewed, the fractional cepstrum is presented based on fractional Fourier transform. At the same time, how to use the fractional cepstrum is discussed in blind chirp signal processing. A numerical result confirms that the proposed fractional cepstrum does work even at low signal-to-noise ratios.

It is very difficult to describe a nonstationary random signal, to say nothing of processing it effectively. In recent years, the time-varying parametric model, especially, time-varying auto-regressive parametric model has been used widely. It is well known that a wavelet neural network has very good performance on function approximation. In this paper, the wavelet neural network is introduced into the time-varying auto-regressive parametric model, so a new time-varying auto-regressive parametric model based on wavelet neural network is presented. At the same time, a new algorithm for model parameters estimate is also presented. A few simulations indicate that the performance of the new time-varying auto-regressive parametric model is better than the old one.

This paper analyzes the Kurtosis based ICA method, and presents the geometrical explanation of its principle. Through the analysis of ICA solution process, this paper also explains the reasons for the indeterminacy and probabilistic property of the ICA solution. The analytical results and conclusions can be applied to the studies of other ICA methods.