Research Of Parameter Estimation And Tracking Method For Distributed Source Using Multiple Arrays

Distributed source phenomenon always appears in radar, sonar and wireless communication fields. Several distributed models and many parameter estimation algorithms have been proposed in recently years. However, most of them have some problems as follows: Firstly, many algorithms are designed for one-dimensional (1D) distributed sources and some of them estimate the central direction-of-arrivals (DOAs) only. Secondly, the algorithms that can estimate both the central DOAs and angular spreads all need 1D or 2D search, which will bring heavy computation burden. How to build a low-complexity algorithm is the research hot topic in distributed source field.Based on the previous work, this dissertation proposes several new low-complexity algorithms for distributed source using multiple arrays. In addition, a fast DOAs tracking algorithm is also addressed in the last part. The main content is summarized as follows:1. A low-complexity parameter estimation method is proposed for 1D coherently distributed (CD) sources. Based on two uniform linear arrays (ULAs), we derive an approximate rotational invariance property with respect to the central DOAs, which can be used to obtain the central DOAs estimation with ESPRIT (Estimating Signal Parameter via Rotational Invariance Techniques) type algorithms. The angular spreads are estimated by FOCUSS (Focal Underdetermined System Solver). Because there is no search operator, the computational complexity of the proposed method is much lower than that of DSPE (Distributed Signal Parameter Estimator) algorithm. In addition, we also cast the central DOAs estimation problem into sparse signal representation frame to obtain the central DOAs estimation.2. A low-complexity 2D CD sources decoupled DOAs estimation approach is proposed. Based on two parallel ULAs, we use the approximate rotational invariance property, which is derived from the one-order Taylor series expansion of generalized steering vector at the central DOAs, and the quadric rotational invariance property (QRIP) of generalized steering vector to realize a decoupled DOAs estimation. The proposed method needs not search, and it is suitable for unknown angular signal distribution case. In addition, we also present two parameters matching methods: min-minimum eigenvalue and max-maximum eigenvalue method to solve the DOAs matching problem.3. A low-complexity parameter estimation method for 2D ID sources is proposed. Based on two parallel uniform circular arrays (UCAs), we derive an approximate rotational invariance property with respect to the central elevation DOAs, with which the central elevation DOAs of ID source can be estimated using TLS-ESPRIT, the central azimuth DOAs is estimated by constructing a novel 1D generalized MUSIC (GMUSIC) spectrum. Based on the preliminary estimation results, the angular spreads can be given by covariance matrix matching technique. Our method is suitable for unknown angular signal distribution case. In addition, the complexity of our method is low and there is no parameters matching problem.4. To obtain the central DOAs estimation when the location of distributed source varies, we also proposed a fast central DOAs tracking method for CD and ID source based on subspace updating. Simulation results show that our method can give more exact DOAs tracking results for small angular spread.