Estimating the frequency of a noisy sinusoid by linear regression
IEEE Transactions on Information Theory
Soft decoding techniques for codes and Lattices, including the Golay code and the Leech Lattice
IEEE Transactions on Information Theory
The shortest vector problem in L2 is NP-hard for randomized reductions (extended abstract)
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Frequency estimation, phase unwrapping and the nearest lattice point problem
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
On the complexity of sphere decoding in digital communications
IEEE Transactions on Signal Processing
GLRT-Optimal Noncoherent Lattice Decoding
IEEE Transactions on Signal Processing - Part II
A universal lattice code decoder for fading channels
IEEE Transactions on Information Theory
The hardness of the closest vector problem with preprocessing
IEEE Transactions on Information Theory
Closest point search in lattices
IEEE Transactions on Information Theory
An Algorithm to Compute the Nearest Point in the Lattice
IEEE Transactions on Information Theory
Linear-time nearest point algorithms for coxeter lattices
IEEE Transactions on Information Theory
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Single frequency estimation is a long-studied problem with application domains including radar, sonar, telecommunications,astronomy and medicine. One method of estimation,called phase unwrapping, attempts to estimate the frequency by performing linear regression on the phase of the received signal.This procedure is complicated by the fact that the received phase is 'wrapped' modulo 2π and therefore must be 'unwrapped' before the regression can be performed. In this paper, we propose an estimator that performs phase unwrapping in the least squares sense. The estimator is shown to be strongly consistent and its asymptotic distribution is derived. We then show that the problem of computing the least squares phase unwrapping is related to a problem in algorithmic number theory known as the nearest lattice point problem. We derive a polynomial time algorithm that computes the least squares estimator. The results of various simulations are described for different values of sample size and SNR.