Table
Summary and 1 Introduction
1.1 Esprit algorithm and error scaling Central Limit
1.2 bet
1.3 related work
1.4 Technical overview and 1.5 organization
2 Proof of the central boundaries of the scaling of the error
3 Proof of an optimal error scaling
4 Theory of disturbance of a second -level self -executive
5 strong comparison of self -vector
5.1 “Good” p.
5.2 Taylor's expansion relative to error terms
5.3 The cancellation of the error in Taylor's enlargement
5.4 Proof of theorem 5.1
Original
B VandanderMony Matriches
C 2nd deferred evidence
Division Deutered Deuter
E Projected Evidence 5. For Section 5
F lower limit of f spectrum assessment
References
1.2 bet
The main contribution of this work is the innovative analysis of the Esprits algorithm, which shows the optimal error in Esprit. These findings have been summarized in the following theores:
Note 1.5 (Prerequisite α> 1). In the above theor, we expect α> 1 to simplify the form of our borders. Note that theorem 1.4 still gives the Esprit algorithm's results for a small noise and zero noise EJ ≡ 0, since α must only be the upper limit of the upper tail decomposition speed. Our evidence can be expanded to give the sharpest estimates of α small values.
We present theorem 1.6 certificate in the Annex F. This result is similar to the Cramér – Rao (cf. [SN89]).). Due to the various settings, we offer the entire variety collection between GAUS distributions using independent evidence.
Authors:
(1) Zhiyan Ding, Department of Mathematics at the University of Berkeley California;
(2) Ethan N. Epperly, Department of Computers and Mathematical Sciences of the California Institute of Technology, Pasadena, Ca, USA;
(3) Lin, Mathematics Department of the University of California, Berkeley, Department of Applied Mathematics and Calculatory Research, Lawrence Berkeley National Laboratory and Challenge Institute for California University, Berkeley;
(4) Ruizhe Zhang, Institute of Simons Calculation.
