Study Of Sonar Blind Beamforming Algorithms Based On Computing Intelligence

Beamforming is used widely in sonar, radar, mobile communication and electronic countermeasure and reconnaissance, etc. Conventional (i.e.. non-blind) beamforming approaches require a reference signal or accurate knowledge of the array response vector corresponding to the desired signal. In practice, array model must have some errors unavoidably. Even though there is a very small error, system will degrade severely. It is more serious in underwater environment. Configuration and dimension of the array will change with the change of temperature and depth of seawater. As a result, beamforming can be degraded because actual array manifold is not same as the theoretical one. Because array correction must be done for many times and a lot of array manifold information is stored, and correction signal source is provided, it is very inconvenient for the project implement. Blind beamforming techniques in general exploit some signal property itself. It can decrease the model error by some algorithms. But it is necessary to compute the inverse and/or matrix eigen-decomposition of a matrix in course of implementing blind beamforming. With the increase of array element number, computing load for the algorithm will increase severely. So it is difficult to carry out the beamforming in real time. Furthermore, some adaptive algorithms use the optimization calculation based on the gradient. When the step-size parameter can not be selected correctly, it is easy to get the local extremum. Computing intelligence whose representative is neural networks has a lot of advantages, for example, parallelism, fault tolerance, self-learning, self-adapting and self-organizing. It can overcome the shortcoming of the blind beamforming algorithm.This dissertation is focused in the study of introducing the computing intelligence to carry outthe sonar blind beamforming. The content can be outlined as follows:In chapter 2, basic model and process frame of blind beamforming is presented. At the sametime, we also study the basic approach and method using the computing intelligence to carry outblind beamforming.In chapter 3, two kinds of sonar blind beamforming algorithms implemented by neural networks are presented based on higher-order cumulants. One is a based-TH neural networks blind beamforming algorithm. This algorithm transforms the question of solving the weights of beamformer into the one of parameter design of neural networks. When the neural network converges to a stable state within a circuit time constant, its output will approximate the weight vector of beamformer. Furthermore, this algorithm is easily implemented on-line by hardware. Another is based radial basis function neural networks (RBFNN) blind beamforming algorithm. Through developed learning method, learning performance is improved remarkably. And the algorithm is robust to system errors. This two kinds of methods combine the characteristic that higher-order cumulant can eliminate effect of Gauss noise with structural advantage that neuralnetwork can operate in parallel. So blind beamforming can be performed efficiently.In chapter 4, we discuss the blind beamforming method based on signal cyclostationary, and study a sonar blind beamforming algorithm based on a cross correlation neural network model. This method transforms the question of estimating beamforming weight vectors into the one of computing the SVD of the cross correlation matrix of array input signals and their frequency shift signals so as to decrease minish the computing load in the course of blind beamforming. Through using the character of signal orthogonalization in the course of selecting the initial values of the neural network and restricting the learning rate of the network weight according to the input of the neural network, we present a improved cross-coupled Hebbian learning rule to make the convergence of the network rapid. This method can provide an approach to perform beamforming in real time and restrain noise and interference.In chapter 5, we study the based eigen-structure sonar blind beamforming method. A parallel learning algorithm of the PCA (Principal Component Analysis) neural network is presents. In the algorithm, principal components are obtained simultaneously. Furthermore, the algorithm does not require recycled use of the data. Hence the time of the neural network convergence is shortened considerably. In addition, we also propose a algorithm to obtain the minimal eigen-value directly based on MCA(Minor Component Analysis) neural networks. And we extend the two kinds of algorithm to the complex field and combine the JADE (Joint Approximate Diagonalization of Eigen—Matrices) algorithm to perform the blind beamforming.In chapter 6, a new blind beamforming algorithm for multitargets is presented. Through defining a new cost function, we develop a blind beamforming algorithm which can separate non-Gaussian signals with arbitrary kurtosis. In addition, weight vector is estimated using complex coding genetic algorithm so as to improve the performance of the global convergence greatly.In chapter 7, we study the conventional beamforming and blind beamforming methods based on a vector sensor. Firstly, received signal model for the vector sensor array is given. Then, an optimizing weight vector formulation is given for beamforming of the vector sensor array ? Based on this, we define a fourth-order cumulant of received signals and blindly estimate the steering vector for the vector sensor array. In addition, we propose a steering vector blind estimation method for a pressure-velocity combination hydrophone array by developing the ESPRIT and combining PCA. At the same time, we study a kind of aerial sonar based on the vector sensor. Finally, we bring the dissertation to a close by providing some key concluding remarks of this work and an overview of current development and future trends in this field.