Fine resolution frequency estimation from three DFT samples: Case of windowed data

An efficient and low complexity frequency estimation method based on the discrete Fourier transform (DFT) samples is described. The suggested method can operate with an arbitrary window function in the absence or presence of zero-padding. The frequency estimation performance of the suggested method is shown to follow the Cramer-Rao bound closely without any error floor due to estimator bias, even at exceptionally high signal-to-noise-ratio (SNR) values.


Direction finding with a uniform circular array via single snapshot processing
Koc, AT; Tanik, Y (1997-01-01)
In this work a new algorithm for multiple emitter direction finding by using a uniform circular array is proposed. The algorithm is based on single snapshot processing, and therefore, it has no restriction on the coherency of the sources. The problem formulation is based on the transformation of the snapshot. The transformed sequence is formed by taking the discrete Fourier transform of the snapshot and weighting it suitably. It contains the so-called distortion terms, which are taken into account by using ...
Frequency estimation of a single real-valued sinusoid: An invariant function approach
Candan, Çağatay; Çelebi, Utku (2021-08-01)
An invariant function approach for the computationally efficient (non-iterative and gridless) maximum likelihood (ML) estimation of unknown parameters is applied on the real-valued sinusoid frequency estimation problem. The main attraction point of the approach is its potential to yield a ML-like performance at a significantly reduced computational load with respect to conventional ML estimator that requires repeated evaluation of an objective function or numerical search routines. The numerical results ind...
Analysis Window Length Selection For Linear Signal Models
Yazar, Alper; Candan, Çağatay (2015-05-19)
A method is presented for the selection of analysis window length, or the number of input samples, for linear signal modeling without compromising the model assumptions. It is assumed that the signal of interest lies in a known linear space and noisy samples of the signal is provided. The goal is to use as many signal samples as possible to mitigate the effect of noise without violating the assumptions on the model. An application example is provided to illustrate the suggested method.
Residual based Adaptive Unscented Kalman filter for satellite attitude estimation
Söken, Halil Ersin (2012-12-01)
Determining the process noise covariance matrix in Kalman filtering applications is a difficult task especially for estimation problems of the high-dimensional states where states like biases or system parameters are included. This study introduces a simplistic residual based adaptation method for the Unscented Kalman Filter (UKF), which is used for small satellite attitude estimation. For a satellite with gyros and magnetometers onboard, the proposed adaptive UKF algorithm estimates the attitude as well as...
Digital computation of linear canonical transforms
Koc, Aykut; Ozaktas, Haldun M.; Candan, Çağatay; KUTAY, M. Alper (2008-06-01)
We deal with the problem of efficient and accurate digital computation of the samples of the linear canonical transform (LCT) of a function, from the samples of the original function. Two approaches are presented and compared. The first is based on decomposition of the LCT into chirp multiplication, Fourier transformation, and scaling operations. The second is based on decomposition of the LCT into a fractional Fourier transform followed by scaling and chirp multiplication. Both algorithms take similar to N...
Citation Formats
Ç. Candan, “Fine resolution frequency estimation from three DFT samples: Case of windowed data,” SIGNAL PROCESSING, pp. 245–250, 2015, Accessed: 00, 2020. [Online]. Available: