Observer-Based Recursive Sliding Discrete Fourier Transform [Tips &amp; Tricks]

Kollár, Zsolt; Plesznik, Ferenc; Trumpf, Simon

doi:10.1109/msp.2018.2853196

Cited by 18 publications

(12 citation statements)

References 11 publications

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“…A previously published sliding spectrum analysis network cleverly implements a bank of N paths where each path is equivalent to the complex resonator in Figure 2 tuned to a frequency of 2k/N radians/sample [11,12]. The value for k in each path are one of the integers in the set k  {1,2,...,N-1}.…”

Section: Comparison With Previously Published Sdft Algorithmsmentioning

confidence: 99%

“…Other than increasing k and N, at the cost of additional computations alternate processing techniques can be effective in reducing the unwanted non-integer k SDFT output magnitude fluctuations. Those techniques are: 1) performing simple unity-gain lowpass filtering of the Figure 7(b) output magnitude samples; 2) implementing time-domain windowing of the input signal by means of frequency-domain convolution [1]; 3) if a real-valued input signal is a single tone we can convert that input signal to a positive-frequency only analytic signal, prior to SDFT processing, using a Hilbert transform network [14] (note that unlike the other listed options, this Hilbert option will not reduce the fluctuations caused by additional input frequency components); and 4) using the oSDFT networks in [11,12] will eliminate unwanted signal magnitude tracking fluctuations because their sinc frequency functions stretch rather than shift to provide complete attenuation of a real-valued input signal's negative-frequency spectral components and a periodic input signal's harmonics.…”

Section: Integer K and Non-integer K Sdft Signal Tracking Examplesmentioning

confidence: 99%

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Improvements to the Sliding Discrete Fourier Transform Algorithm [Tips & Tricks]

Lyons¹,

Howard

2021

IEEE Signal Process. Mag.

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The IEEE Publication Services & Products Board (PSPB) last revised its Operations Manual Section 8.1.9 on Electronic Information Dissemination (known familiarly as "author posting policy") on 7 December 2012.PSPB accepted the recommendations of an ad hoc committee, which reviewed the policy that had previously been revised in November 2010. The highlights of the current policy are as follows: The policy reaffirms the principle that authors are free to post their own version of their IEEE periodical or conference articles on their personal Web sites, those of their employers, or their funding agencies for the purpose of meeting public availability requirements prescribed by their funding agencies. Authors may post their version of an article as accepted for publication in an IEEE periodical or conference proceedings. Posting of the final PDF, as published by IEEE Xplore®, continues to be prohibited, except for open-access journal articles supported by payment of an article processing charge (APC), whose authors may freely post the final version.  The policy provides that IEEE periodicals will make available to each author a preprint version of that person's article that includes the Digital Object Identifier, IEEE's copyright notice, and a notice showing the article has been accepted for publication.  The policy states that authors are allowed to post versions of their articles on approved third-party servers that are operated by not-for-profit organizations. Because IEEE policy provides that authors are free to follow public access mandates of government funding agencies, IEEE authors may follow requirements to deposit their accepted manuscripts in those government repositories.

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Section: Comparison With Previously Published Sdft Algorithmsmentioning

confidence: 99%

Section: Integer K and Non-integer K Sdft Signal Tracking Examplesmentioning

confidence: 99%

Improvements to the Sliding Discrete Fourier Transform Algorithm [Tips & Tricks]

Lyons¹,

Howard

2021

IEEE Signal Process. Mag.

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“…The leakage can be reduced by various omethods [25][26][27][28][29], which are mostly based on the estimation of fundamental frequency. However, the optimal measurement condition is to make the sampling process satisfy the requirement of coherent sampling for both the fundamental frequency and its harmonic frequencies simultaneously, which significantly increases the spectral resolution of the FFT and creates an ideal environment for critically ADDR performance evaluation of the DUT.…”

Section: Leakagementioning

confidence: 99%

An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems

et al. 2020

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The dynamic range (DR) index lacking of an official definition leads to ambiguities in performance evaluation. The existing measurement methods of DR do not always match with the various actual application conditions, and some detailed distortion behavior of the device under test (DUT) is not extracted. In this paper, a new index for evaluating the DR performance of audio systems is proposed and validated, herein referred to as the audio distortion dynamic range (ADDR). It reduces the uncertainty of measurement conditions by an explicit definition and unifies the signal-to-noise ratio (SNR) and the signal-to-noise-and-distortion ratio (SINAD) indexes if under the same measurement condition. Moreover, to comprehensively reflect the impact of harmonic, spurious, and noise components on the DUT, the definitions of the traditional indexes based on classification of distorted components are replaced by the variable thresholds in the ADDR definition. Subsequently, the detailed steps of ADDR and the critical factors influencing its accuracy, are analyzed and then the optimized measurement conditions are given. Experiments based on simulated DUTs show the ADDR index can distinguish performance difference that the traditional indexes cannot distinguish, which proves it is an effective supplementary to the existing indexes in some real applications.

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“…This method is only applicable in a hopping scenario where each window hops N/4 samples (N is the DFT size). Another recursive algorithm for C-SDFT is introduced in [22] utilizing the observer theory in the control systems. The algorithm calculates the C-SDFT while solving the stability problem associated with recursive SDFTs with less memory than what is required by the SFFT.…”

Section: A Complete Sdft (C-sdft)mentioning

confidence: 99%

On the Low Complexity Implementation of the DFT-Based BFSK Demodulator for Ultra-Narrowband Communications

Safapourhajari

Kokkeler

2020

IEEE Access

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The DFT-based demodulator for BFSK has been introduced for applications where the received signal experiences a carrier frequency offset (CFO) much larger than the symbol rate. The Ultra-Narrowband (UNB) communication techniques have been introduced for implementing the emerging Low Power Wide Area Networks (LPWAN). Since UNB communication is prone to CFO, a DFT-based BFSK demodulator is an interesting option for this type of communication. However, for proper operation in a large frequency offset, the DFT-based demodulator requires a complex window synchronization which is not desirable for low power nodes. The main source of complexity, is calculating the DFT of a window which slides over the preamble. In this work, the complexity is alleviated by considering the window synchronization algorithm and its implementation together. First, a new window synchronization algorithm is proposed which is designed such that an efficient class of implementations of the sliding DFT (SDFT), called Single Bin SDFT (SB-SDFT) in this work, can be used. Moreover, a new stable implementation of SB-SDFT is designed to enable zero-padding which is required by the demodulator. The complexity of the proposed algorithm implemented using the SB-SDFT, scales more efficiently compared to the conventional algorithm when the range of tolerable CFO increases. Using the proposed method, for a CFO tolerance in the order of 14.5 times the symbol rate (±14.5 kHz for a symbol rate equal to 100 Hz), the number of complex operations is reduced by more than 85% (and memory by 90%) compared to the conventional method.

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Observer-Based Recursive Sliding Discrete Fourier Transform [Tips & Tricks]

Cited by 18 publications

References 11 publications

Improvements to the Sliding Discrete Fourier Transform Algorithm [Tips & Tricks]

Improvements to the Sliding Discrete Fourier Transform Algorithm [Tips & Tricks]

An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems

On the Low Complexity Implementation of the DFT-Based BFSK Demodulator for Ultra-Narrowband Communications

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