title: Improvement of Source Number Estimation Method for Single Channel Signal.
abstract: Source number estimation methods for single channel signal have been investigated and the improvements for each method are suggested in this work. Firstly, the single channel data is converted to multi-channel form by delay process. Then, algorithms used in the array signal processing, such as Gerschgorin's disk estimation (GDE) and minimum description length (MDL), are introduced to estimate the source number of the received signal. The previous results have shown that the MDL based on information theoretic criteria (ITC) obtains a superior performance than GDE at low SNR. However it has no ability to handle the signals containing colored noise. On the contrary, the GDE method can eliminate the influence of colored noise. Nevertheless, its performance at low SNR is not satisfactory. In order to solve these problems and contradictions, the work makes remarkable improvements on these two methods on account of the above consideration. A diagonal loading technique is employed to ameliorate the MDL method and a jackknife technique is referenced to optimize the data covariance matrix in order to improve the performance of the GDE method. The results of simulation have illustrated that the performance of original methods have been promoted largely.
authors.affiliation: College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, Hunan Province, China.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Dong
authors.affiliation: College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, Hunan Province, China.
authors.forename: Junpeng
authors.initials: J
authors.lastname: Hu
authors.affiliation: College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China.
authors.forename: Bolun
authors.initials: B
authors.lastname: Du
authors.affiliation: College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China.
authors.forename: Yunze
authors.initials: Y
authors.lastname: He
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical; D016011:Normal Distribution