A Sampling Rate Selecting Algorithm for the Arbitrary Waveform Generator

The arbitrary waveform generator is now commonly used to generate waveforms in many fields. The Nyquist sampling theorem only provides an open interval for the sampling rate selection, but how to determine a specific sampling rate within this open interval has not been discussed. In practice, the sample rate conversion is adopted to convert the arbitrarily selected sample rate to a safe one which keeps the mirror frequency out of a fixed passband of an anti-aliasing filter in an arbitrary waveform generator whose sample rate is variable. To select a safe sample rate with low computational cost and memory requirements for conversion, an algorithm, with fractional sample rate conversion in mind, is proposed in this paper to guarantee the safety of the target sample rate and the bound of resource cost. The experimental results show that not only the computational cost and memory requirements of sampling rate conversion but also the frequency stability and harmonics of the generated signal in the proposed algorithm overcome the method that arbitrary converts the sampling rate to a fixed maximum one.

[1]  O. Panfilov,et al.  Frequency Agile Signal Processing in Direct Conversion Software Radio , 2006, 2006 International Conference on Actual Problems of Electron Devices Engineering.

[2]  Alok N. Choudhary,et al.  Design and Implementation of an FPGA Architecture for High-Speed Network Feature Extraction , 2007, 2007 International Conference on Field-Programmable Technology.

[3]  Maciej Sac,et al.  Variable Fractional Delay Filter Design Using a Symmetric Window , 2014, Circuits Syst. Signal Process..

[4]  Michael M. Marefat,et al.  Metacognition and the next generation of cognitive radio engines , 2016, IEEE Communications Magazine.

[5]  A. Frappe,et al.  All-digital RF signal generation for software defined radio , 2008, 2008 4th European Conference on Circuits and Systems for Communications.

[6]  Andreas Dr.-Ing. Franck,et al.  Efficient algorithms for arbitrary sample rate conversion with application to wave field synthesis , 2012 .

[7]  Jiong Yang,et al.  Implementation of a multi-waveband, high-precision, wideband waveform generator based on DDWS for antenna channel characteristics test , 2016, 2016 Progress in Electromagnetic Research Symposium (PIERS).

[8]  Shulin Tian,et al.  Precisely synchronous and cascadable multi-channel arbitrary waveform generator. , 2017, The Review of scientific instruments.

[9]  Brandon Ravenscroft,et al.  Gapped spectrum shaping for tandem-hopped radar/communications & cognitive sensing , 2016, 2016 IEEE Radar Conference (RadarConf).

[10]  Marek Blok.  On Sample Rate Conversion Based on Variable Fractional Delay Filters , 2013, Int. J. Comput. Sci. Appl..

[11]  Liu Ke,et al.  A complex waveform sequence synthesis method based on FPGA , 2015, 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI).

[12]  Shilong Pan,et al.  Microwave photonic signal generation for radar applications , 2016, 2016 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM).

[13]  Ram M. Narayanan,et al.  Experimental demonstration of cognitive spectrum sensing & notching for radar , 2018, 2018 IEEE Radar Conference (RadarConf18).

[14]  Xie Wei.  Implementation of DDWS-Based Complicated Waveform Generators , 2012 .

[15]  Hong Zhang,et al.  A Filter Structure for Arbitrary Re-Sampling Ratio Conversion of a Discrete Signal , 2017, Inf..

[16]  Zhao Yun,et al.  Signal Generation Techniques Based on Arbitrary Sample Rate Conversion , 2014, 2014 IEEE International Conference on Computer and Information Technology.

[17]  Z. Ye,et al.  Analysis of Multistage Sampling Rate Conversion for Potential Optimal Factorization , 2013 .

[18]  Augusto Aubry,et al.  A cognitive approach for radar receiver adaptation , 2016, 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI).

[19]  Abbas Yongaçoglu,et al.  Interference map generation based on Delaunay triangulation in cognitive radio networks , 2012, 2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[20]  Xia Li,et al.  Demonstration of cognitive radar for target localization under interference , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[21]  Christopher D. Ziomek,et al.  Advanced waveform generation techniques for ATE , 2009, 2009 IEEE AUTOTESTCON.

[22]  Joshua D. Reiss,et al.  Practical considerations on optimising multistage decimation and interpolation processes , 2016, 2016 IEEE International Conference on Digital Signal Processing (DSP).