A new state-space approach for direction finding

• Published in: IEEE transactions on signal processing Vol. 42; no. 11; pp. 3234 - 3237
• Main Authors: Vaccaro, R.J., Yinong Ding
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.1994; Institute of Electrical and Electronics Engineers
• Subjects: Amplitude estimation; Applied sciences; Covariance matrix; Direction of arrival estimation; Equations; Exact sciences and technology; Least squares approximation; Least squares methods; Parameter estimation; Radiolocalization and radionavigation; Sensor arrays; Signal processing; Telecommunications; Telecommunications and information theory; Vectors; Least squares method; Estimation; Arrival angle; Radiolocalisation; State space; Radiogoniometry; Covariance matrix; Sensor array
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/78.330382

Direction-of-arrival estimation using state-space models in sensor array processing with a uniform Linear array can be reduced to finding a solution to the equation U/spl tilde//sub 1/F/spl ap/U/spl tilde//sub 2/ for F, where noises in both sides of the equation are highly correlated. Least squares or even total least squares solutions are not optimal, and the complicated covariance structure in U/spl tilde//sub 1/ and U/spl tilde//sub 2/ does not allow a weighted total least squares procedure to be carried out. The approach presented in this correspondence is to first solve a least squares problem to get an estimate of the underlying subspace represented by the noisy basis vectors in U/spl tilde//sub 1/ and U/spl tilde//sub 2/. An approximate error covariance matrix for the least squares problem is obtained using a first-order perturbation expansion. This covariance matrix is used to solve for the underlying subspace in a weighted least squares sense. Parameters are then extracted from the estimated subspace. Numerical examples show that the performance of the proposed method is very close to the Cramer-Rao bound.< >