Study On The Theory Of Generalized Normal-distribution Signal Processing And Its Application In Communications

In practice, many signals and noises are not exactly Gauss distributed. Furthermore, spikes and impulsiveness often accompany these signals and noises. If the signals and noises are still modeled by Gaussian distribution, the signal processing algorithms will degrade inevitably. In order to solve the problem theoretically, Alpha-stable distribution was introduced to model the signals and noises at the end of twentieth century. Based on the contribution of many people in the last decade, a new theory named Theory of Generalized Normal-distribution Signal Processing (TGNSP) comes into being.The noises in communication channels have been proved to be impulsive. To improve the performance of communication systems, the noises are modeled as Alpha-stable distribution in this paper. In the frame of TGNSP, the problems of beamforming, equalization of communication channel, signal copy, subspace tracking, adaptive array and time delay estimation (TDE) are studied. Main researches and conclusions of this paper are listed as follows:(l)The noises in wireless channel and the shallow water channel are often strongly impulsive. The traditional Constant Modulus (CM) algorithm and the CM array degrade greatly;even they cannot work any more under such impulsive noise condition. In the frame of TGNSP, the Generalized CM (GCM) algorithm is proposed. The GCM algorithm has strong robustness compared to the traditional CM algorithm. It can capture the desired signal in impulsive noise which is modeled as Alpha-stable distribution in this paper. On the other hand, the traditional CM algorithm is a special case of the GCM algorithm. An analysis model consisted of two sinusoid signal is adopted to study the gain process of the GCM algorithm. An important conclusion is obtained: In Alpha-stable noise environment, the performance that one of the input signals is captured by the GCM algorithm is only determined by the initial weight vector and the relative power of the input signals. Generally speaking, the GCM algorithm always captures one of the input signals with max power and suppresses others. The noises in shallow water channel are examed. It is proved that the noise is non-gaussian distributed with great amount of impulsiveness. The GCM algorithm is applied to equalization of shallow water channel. It is shown that the GCM algorithm improves the performance of Bit Error Rate (BER). The GCM beamformer and the new signal canceller construct the GCM array. The steady-state properties of the GCM array at convergence are examined. And the closed-form expression of steady point of the GCM array is derived in Alpha-stable noise. The cascade GCM array is also constructed to perform multi-signal copy. Furthermore, the signal-capture and direction-finding capabilities of each stage are discussed.This research has the potential application in cell mobile communication system in which the cochannel signal is the main interference.(2)Tow robust Subspace Tracking (ST) algorithms and one robust adaptive array algorithm based on subspace are proposed. The first ST algorithm is to track the minimum eigenvalue and corresponding eigen vector, where the eigenvalue and eigen vector are taken as the only minimum of a quadratic function and solution. The second ST algorithm is tracking the signal subspace, where the tracking process is looked as optimization problem without constraint. A new cost function for ST in Alpha-stable noise is proposed and a robust ST algorithm based on FLOS, i.e.FLOS-ST, is developed. In order to reduce computation load, The FLOS-ST is simplified by M-estimation. Then, a more robust ST algorithm, i.e.M-ST, is obtained. Based on the research about ST, a robust adaptive array for IS-54/136 system is studied. A new technique to estimate propagation vector by exploiting the whole frame of the received signal in Alpha-stable noise is proposed. Then, the combining weight vector of adaptive array can be estimated more steadily and the desired signal can be recovered with lower BER.(3)A wireless location technique based on TDOA (Time Difference of Arrival) is proposed in this paper, and a new wireless location system is developed. Some critical problems, such as denoise algorithms, impact of center frequency conversion on TDOA, and TDOA algorithms are studied. And some problems in application are also considered. The wireless source location system is constructed by embedding the software into corresponding hardware system. From the outdoor test, it is proved that the location system has high location accuracy. It is believed the location equipment will be seen in market soon.