Soft Computing Theory And Its Applications In Signal Processing

With the development of communication, computer and electronics, the en-gineering applications have become more widely and more deeply, the number ofmathematical methods increases rapidly in abundant engineering applications. Be-cause of the need of information processing, soft computing methods and othermathematical methods are used widely. The theory of soft computing developedrapidly, and some other relative theories appear constantly, which increases its im-portance in the application of information processing. This thesis investigates theuse of soft computing methods (including: wavelet theory, artificial neural network,fractional theory, chaos theory and fuzzy mathematics, etc) in information process-ing, reveals the problems in engineering applications of soft computing methods andcombines different mathematics methods to realize information processing.This thesis investigates and discusses the use of soft computing methods inultra-wide band(UWB) communications, weak signal detection and multi-sensormulti-target tracking. These techniques are used to construct novel ultra-wide bandcommunication multi-user detectors, weak signal detection systems and multi-senormulti-target tracking system.Firstly, in digital communication signal processing fields, two multi-user de-tection methods are proposed. For impulse radio UWB systems based on the proba-bility and statistics, the first novel multi-user detection is presented. HMM systemsof time-hopping BPSK in UWB are developed separately in the paper. Consideringseeking for the following sequence of maximal transition probability correspond-ing to previous sequence, a maximum a posteriori (MAP) multi-user detector hasbeen constructed. The second novel blind multi-user adaptive detection algorithm ispresented. Dynamical systems of time-hopping BPSK in UWB are developed sepa-rately in the paper. multi-user detector has been constructed using the recently pro-posed canonical representation of multi-user receivers and Kalman filtering. The-oretical analysis and numerical results show that the new scheme has better BERperformance comparing with the conventional detector.Secondly, in digital signal processing, for strong chaotic noise and fractionalnoise, two weak signal detection schemes are investigated respectively. A novel approach of weak sinusoid signal detection from a chaotic noise background is pro-posed. The RBF neural networks are used to predict the chaotic background, and theradial center is gained by fuzzy c-means algorithm. The noisy error signal extractedfrom the detected signal, then the error signal is added into the Duffing chaotic os-cillator using the sensitivities to the initial conditions and immunity to noise of thesystem. Based on the motion transition of a chaotic system, new schemes to de-tect weak sinusoidal signal berried in a chaotic background are presented forward.Simulation measured data demonstrate the effectiveness of the proposed algorithm.Weak signal detection of strong fractional Brownian motion (fBm)using Duffingoscillator and fuzzy adaptive Kalman filter is proposed in this paper. A dynamicsystems based on the orthonormal wavelet decomposition coefficients of the fBm isformulated, fuzzy adaptive Kalman filter is used to estimate the fBm when the vari-ance of measurement noise is unknown. The noisy error signal extracted from thedetected signal, then the error signal is added into Duffing chaotic oscillator usingthe sensitivities to the initial conditions and immunity to noise of the system. Basedon the motion transition of a chaotic system and the estimation of fuzzy adaptiveKalman filtering, new schemes to detect weak sinusoidal signal berried in a strongfractional background noise are presented forward. Simulation results demonstratethe effectiveness of the proposed algorithm.A new statistical understanding of immunity to noise of Duffing systems is alsopresented.Finally, the Joint Probabilistic Data Association (JPDA) solves single sensormulti-target tracking in clutter, but it can not be used directly in multi-sensor multi-target tracking (MMT) and has high computational complexity with the numbers oftargets and the number of returns. The novel algorithm was presented by combiningMaximum Likelihood Estimation (MLE) with Fuzzy C-Means (FCM) clustering.The MLE is used to classify the same source observations at one time into the sameset, then the FCM approach is used to calculate the data association probability, andthe similarity structure of JPDA algorithm is used to realize the MMT. The computersimulations indicate that the scheme achieve MMT perfectly with low computationalcomplexity, higher precision and easy realization.