| Estimating the angles and frequencies of space signal sources through signal processing method is one of the basic problems of radar array signal processing. And the state-space model provide a new idea to cognize the problem of signal processing basically. The facility and agility of the state-space model also provide a new idea for the joint angle-frequency estimation.Recently it has attracted scientists'attention that solve the problems of signal processing from enginevalue decomposing method. It can be deposed two simple aspects: one is the problem of linear model parameter estimation, the other is nonlinear process. The two aspects are very important for the whole parameter estimation.During the research of signal process, linear model multinomial is often supposed, but state-space realization of linear model may be the other choice .It is appropriate for solving of some signal processing. It can more easily expose the nature of the problems than other models such as AR, MA and ARMA model, because the state-space model possesses invariant structure which if being properly made use of can ultimately improve the performance of DOA estimation, and again, the structure property provides new way and new ideal for signal processing. In addition, the well-known subspace methods based on AR, MA and ARMA models can also be totally summarized as the special cases of the methods based on state-space model.As a helpful complementarity of traditional linear system identification methods, subspace identification methods has received extensive attention. This class of methods colligates the ideas of system theory, linear algebra and statistics. Its characteristic is to identify the system state-space model directly from the input and output data, so it is greatly suit for multi-variable system identification. The core of subspace identification methods is to obtain the broad sense observability matrix, then find the system state-space model through the broad sense observability matrix. The whole identification process can be divided into two steps: First step, decompose the very linear space which composed from sample data into two orthogonal subspace, one correspond to signal part of the system, the other correspond to noise part of the system. And then, obtain the consistent estimation of board sense observability matrix under the similarity transformation using the subspace corresponding to the system signal. Second step, compute the system matrix using the estimation of board sense observability matrix above.The thesis researched the joint DOA-frequency estimation based on state-space model. The main work can be generalized as follows:1. Firstly the thesis introduced the basic concept and knowledge of array signal processing. And it introduced some classical algorithms of DOA which are applied widely. Then it generalized the characteristics of state-space model.2. Whereafter, the thesis introduced the basic theory, idea and classification of the subspace identification method. Then introduced the basic algorithms of the subspace identification method according to the different strategy which the algorithms used.3. The thesis applied the subspace identification method into the practical application of array signal DOA and frequency estimation. Introduced the basic principle of DOA and frequency estimation using CVA, N4SID and MOESP algorithms separately.4. The algorithms were testified through numerical simulations and compared with other algorithms.5. A brief summary of this thesis and some suggestions for the future are put forward.Compared with general approaches ,the methods in this paper have many virtues:1. State-space model provides some flexibility for model parameter2. State-space description allows some robotic technology comparing with other methods.3. The result of using subspace identification method is more outstanding under the gauss colored noise.4. DOA and frequency estimations were paired automatically which decreased the quantity of numeration. |
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