Improved Source Number Detection and Direction Estimation With Nested Arrays and ULAs Using Jackknifing

摘要: We consider the problem of source number detection and direction-of-arrival (DOA) estimation, based on uniform linear arrays (ULAs) newly proposed nested arrays. A ULA with N sensors can detect at most N-1 sources, whereas a array provides O(N2) degrees freedom O(N) sensors, enabling us to K sources sensors. In order make full use available limited valuable data, we propose novel strategy, which is inspired by jackknifing resampling method. Exploiting numerous iterations subsets whole data set, this strategy greatly improves results existing DOA estimation methods. With assumption that set contain enough information, theoretically prove improvement or performance, compared original performance without jackknifing, guaranteed when accuracy greater than equal 50%. Numerical simulations demonstrate superiority our applied both for ULAs