Optimized sparse fractional Fourier transform: Principle and performance analysis
•Neyman-Pearson detection is applied to estimate large coefficients of noise-corrupted signals in the fractional Fourier domain.•Distribution of phase error is obtained via Parzen-Rosenblatt window method.•Location error correction method is proposed.•Important properties of the proposed optimized s...
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| Veröffentlicht in: | Signal processing Jg. 174; S. 107646 |
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01.09.2020
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| Abstract | •Neyman-Pearson detection is applied to estimate large coefficients of noise-corrupted signals in the fractional Fourier domain.•Distribution of phase error is obtained via Parzen-Rosenblatt window method.•Location error correction method is proposed.•Important properties of the proposed optimized sparse fractional Fourier transform are investigated via extensive simulations.•Real data collected from a continuous-wave radar is processed and the velocity of a free falling target is estimated.
For the input signals that can be sparsely represented in the fractional Fourier domain, sparse discrete fractional Fourier transform (SDFrFT) has been proposed to accelerate the numerical computation of discrete fractional Fourier transform. While significantly alleviating the computational load, SDFrFT has narrow applicability since it is more suitable for large-scale input signals. In this regard, the objective of this work is to overcome the limitation and further optimize the numerical computation of SDFrFT by exploiting the underlying phase information. We first employ Neyman-Pearson approach to achieve a noise-robust detection. Then, we derive the probability distribution function of the phase error in the location stage and, accordingly, design a location error correction algorithm. The proposed algorithm, termed optimized sparse fractional Fourier transform (OSFrFT), can reduce the computational complexity while guarantee sufficient robustness and estimation accuracy. Simulation results are provided to validate the effectiveness of the proposed algorithm. A successful application of OSFrFT to continuous wave radar signal processing is also presented. |
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| AbstractList | •Neyman-Pearson detection is applied to estimate large coefficients of noise-corrupted signals in the fractional Fourier domain.•Distribution of phase error is obtained via Parzen-Rosenblatt window method.•Location error correction method is proposed.•Important properties of the proposed optimized sparse fractional Fourier transform are investigated via extensive simulations.•Real data collected from a continuous-wave radar is processed and the velocity of a free falling target is estimated.
For the input signals that can be sparsely represented in the fractional Fourier domain, sparse discrete fractional Fourier transform (SDFrFT) has been proposed to accelerate the numerical computation of discrete fractional Fourier transform. While significantly alleviating the computational load, SDFrFT has narrow applicability since it is more suitable for large-scale input signals. In this regard, the objective of this work is to overcome the limitation and further optimize the numerical computation of SDFrFT by exploiting the underlying phase information. We first employ Neyman-Pearson approach to achieve a noise-robust detection. Then, we derive the probability distribution function of the phase error in the location stage and, accordingly, design a location error correction algorithm. The proposed algorithm, termed optimized sparse fractional Fourier transform (OSFrFT), can reduce the computational complexity while guarantee sufficient robustness and estimation accuracy. Simulation results are provided to validate the effectiveness of the proposed algorithm. A successful application of OSFrFT to continuous wave radar signal processing is also presented. |
| ArticleNumber | 107646 |
| Author | Liu, Shengheng Zhang, Hongchi Shan, Tao Tao, Ran |
| Author_xml | – sequence: 1 givenname: Hongchi surname: Zhang fullname: Zhang, Hongchi organization: School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China – sequence: 2 givenname: Tao surname: Shan fullname: Shan, Tao organization: School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China – sequence: 3 givenname: Shengheng orcidid: 0000-0001-6579-9798 surname: Liu fullname: Liu, Shengheng email: s.liu@seu.edu.cn organization: School of Information Science and Engineering, Southeast University, Nanjing 210096, China – sequence: 4 givenname: Ran surname: Tao fullname: Tao, Ran organization: School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China |
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| References | Neto, Lima, Silva, Souza (bib0007) Dec. 2019; 165 Peng, Tang, Du, Qian (bib0020) Jun. 2017; 64 Abbas, Sun, Foroosh (bib0014) Apr. 2017; 65 Pawar, Ramchandran (bib0015) Jan. 2018; 64 Miao, Zhang, Tao (bib0017) Jun. 2019; 67 Hassanieh, Indyk, Katabi, Price (bib0022) Jan. 2012 Ozaktas, Arikan, Kutay, Bozdaği (bib0004) Sept. 1996; 44 Chen, Tsai, Yang (bib0013) Nov. 2017; 65 Yu, Chen, Huang, Guan (bib0012) 2019 Candan, Kutay, Ozaktas (bib0002) May 2000; 48 Neto, Lima (bib0006) Dec. 2017; 65 Zhang, Zhang, Chao, Tseng (bib0021) May 2018; 32 Liu, Shan, Zhang, Tao, Feng (bib0011) May 2015 Liu, Zeng, Zhang, Fan, Shan, Tao (bib0025) Mar. 2015 Santhanam, McClellan (bib0003) Apr. 1996; 44 Liu, Shan, Tao, Zhang, Zhang, Zhang, Wang (bib0010) Dec. 2014; 62 Simon, Alouini (bib0023) Jul. 2003; 2 Hassanieh, Indyk, Katabi, Price (bib0018) Jan. 2012 Gilbert, Indyk, Iwen, Schmidt (bib0009) Sept. 2014; 31 Peleg, Friedlander (bib0024) Jan. 1996; 32 Hsue, Chang (bib0008) Oct. 2015; 62 Pei, Ding (bib0005) May 2000; 48 Pawar, Ramchandran (bib0016) Jan. 2018; 64 Wang, Patel, Petropulu (bib0019) Dec. 2017; 53 Almeida (bib0001) Nov. 1994; 42 Zhang (10.1016/j.sigpro.2020.107646_bib0021) 2018; 32 Pawar (10.1016/j.sigpro.2020.107646_bib0015) 2018; 64 Liu (10.1016/j.sigpro.2020.107646_bib0011) 2015 Hassanieh (10.1016/j.sigpro.2020.107646_bib0022) 2012 Neto (10.1016/j.sigpro.2020.107646_bib0007) 2019; 165 Chen (10.1016/j.sigpro.2020.107646_bib0013) 2017; 65 Liu (10.1016/j.sigpro.2020.107646_bib0010) 2014; 62 Peleg (10.1016/j.sigpro.2020.107646_bib0024) 1996; 32 Neto (10.1016/j.sigpro.2020.107646_bib0006) 2017; 65 Simon (10.1016/j.sigpro.2020.107646_bib0023) 2003; 2 Yu (10.1016/j.sigpro.2020.107646_bib0012) 2019 Ozaktas (10.1016/j.sigpro.2020.107646_bib0004) 1996; 44 Pawar (10.1016/j.sigpro.2020.107646_bib0016) 2018; 64 Wang (10.1016/j.sigpro.2020.107646_bib0019) 2017; 53 Miao (10.1016/j.sigpro.2020.107646_bib0017) 2019; 67 Candan (10.1016/j.sigpro.2020.107646_bib0002) 2000; 48 Pei (10.1016/j.sigpro.2020.107646_bib0005) 2000; 48 Liu (10.1016/j.sigpro.2020.107646_bib0025) 2015 Gilbert (10.1016/j.sigpro.2020.107646_bib0009) 2014; 31 Abbas (10.1016/j.sigpro.2020.107646_bib0014) 2017; 65 Hassanieh (10.1016/j.sigpro.2020.107646_bib0018) 2012 Santhanam (10.1016/j.sigpro.2020.107646_bib0003) 1996; 44 Almeida (10.1016/j.sigpro.2020.107646_bib0001) 1994; 42 Peng (10.1016/j.sigpro.2020.107646_bib0020) 2017; 64 Hsue (10.1016/j.sigpro.2020.107646_bib0008) 2015; 62 |
| References_xml | – volume: 64 start-page: 429 year: Jan. 2018 end-page: 450 ident: bib0015 article-title: FFAST: an algorithm for computing an exactly publication-title: IEEE Trans. Inform. Theory – start-page: 563 year: Jan. 2012 end-page: 578 ident: bib0018 article-title: Nearly optimal sparse Fourier transform publication-title: Proc. 44th Symp. Theory Comput., New York, NY, USA – volume: 42 start-page: 3084 year: Nov. 1994 end-page: 3091 ident: bib0001 article-title: The fractional Fourier transform and time-frequency representations publication-title: IEEE Trans. Signal Process. – volume: 44 start-page: 994 year: Apr. 1996 end-page: 998 ident: bib0003 article-title: The discrete rotational Fourier transform publication-title: IEEE Trans. Signal Process. – volume: 62 start-page: 6582 year: Dec. 2014 end-page: 6595 ident: bib0010 article-title: Sparse discrete fractional Fourier transform and its applications publication-title: IEEE Trans. Signal Process. – volume: 32 start-page: 378 year: Jan. 1996 end-page: 387 ident: bib0024 article-title: Multicomponent signal analysis using the polynomial-phase transform publication-title: IEEE Trans. Aerosp. Electron. Syst. – year: 2019 ident: bib0012 article-title: Fast detection method for low-observable maneuvering target via robust sparse fractional Fourier transform publication-title: IEEE Geosci. Remote Sens. Lett. Early Access – volume: 65 start-page: 5716 year: Nov. 2017 end-page: 5729 ident: bib0013 article-title: On performance of sparse fast Fourier transform and enhancement algorithm publication-title: IEEE Trans. Signal Process. – volume: 48 start-page: 1329 year: May 2000 end-page: 1337 ident: bib0002 article-title: The discrete fractional Fourier transform publication-title: IEEE Trans. Signal Process. – start-page: 1139 year: May 2015 end-page: 1143 ident: bib0011 article-title: A fast algorithm for multi-component LFM signal analysis exploiting segmented DPT and SDFrFT publication-title: Proc. IEEE Int. Radar Conf. (RadarCon), Arlington, VA, USA – volume: 48 start-page: 1338 year: May 2000 end-page: 1353 ident: bib0005 article-title: Closed-form discrete fractional and affine Fourier transforms publication-title: IEEE Trans. Signal Process. – start-page: 1183 year: Jan. 2012 end-page: 1194 ident: bib0022 article-title: Simple and practical algorithm for sparse Fourier transform publication-title: Proc. 23rd Annu. ACM-SIAM Symp. Discrete Algorithms, Kyoto, Japan – volume: 2 start-page: 611 year: Jul. 2003 end-page: 615 ident: bib0023 article-title: Some new results for integrals involving the generalized Marcum Q function and their application to performance evaluation over fading channels publication-title: IEEE Trans. Wireless Commun. – volume: 32 start-page: 675 year: May 2018 end-page: 707 ident: bib0021 article-title: Kernel mixture model for probability density estimation in Bayesian classifiers publication-title: Data Min. Knowl. Discov. – volume: 44 start-page: 2141 year: Sept. 1996 end-page: 2150 ident: bib0004 article-title: Digital computation of the fractional Fourier transform publication-title: IEEE Trans. Signal Process. – volume: 53 start-page: 2735 year: Dec. 2017 end-page: 2755 ident: bib0019 article-title: The robust sparse Fourier transform (RSFT) and its application in radar signal processing publication-title: IEEE Trans. Aerosp. Electron. Syst. – volume: 165 start-page: 72 year: Dec. 2019 end-page: 82 ident: bib0007 article-title: Computation of an eigendecomposition-based discrete fractional Fourier transform with reduced arithmetic complexity publication-title: Signal Process. – volume: 67 start-page: 3181 year: Jun. 2019 end-page: 3196 ident: bib0017 article-title: Fractional Fourier analysis using the Möbius inversion formula publication-title: IEEE Trans. Signal Process – start-page: 799 year: Mar. 2015 end-page: 803 ident: bib0025 article-title: Automatic human fall detection in fractional Fourier domain for assisted living publication-title: Proc. 41st IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), Shanghai, China – volume: 64 start-page: 4866 year: Jun. 2017 end-page: 4875 ident: bib0020 article-title: Multimode process monitoring and fault detection: asparse modeling and dictionary learning method publication-title: IEEE Trans. Ind. Electron. – volume: 31 start-page: 91 year: Sept. 2014 end-page: 100 ident: bib0009 article-title: Recent developments in the sparse Fourier transform: a compressed Fourier transform for big data publication-title: IEEE Signal Process. Mag. – volume: 65 start-page: 2033 year: Apr. 2017 end-page: 2048 ident: bib0014 article-title: An exact and fast computation of discrete Fourier transform for polar and spherical grid publication-title: IEEE Trans. Signal Process. – volume: 64 start-page: 451 year: Jan. 2018 end-page: 466 ident: bib0016 article-title: R-FFAST: a robust sub-linear time algorithm for computing a sparse DFT publication-title: IEEE Trans. Inform. Theory – volume: 65 start-page: 6171 year: Dec. 2017 end-page: 6184 ident: bib0006 article-title: Discrete fractional Fourier transforms based on closed-form Hermite-Gaussian-like DFT eigenvectors publication-title: IEEE Trans. Signal Process. – volume: 62 start-page: 2594 year: Oct. 2015 end-page: 2605 ident: bib0008 article-title: Real discrete fractional Fourier, Hartley, generalized Fourier and generalized Hartley transforms with many parameters publication-title: IEEE Trans. Circuits Syst. I Regul. Pap. – volume: 32 start-page: 378 issue: 1 year: 1996 ident: 10.1016/j.sigpro.2020.107646_bib0024 article-title: Multicomponent signal analysis using the polynomial-phase transform publication-title: IEEE Trans. Aerosp. Electron. Syst. doi: 10.1109/7.481277 – start-page: 563 year: 2012 ident: 10.1016/j.sigpro.2020.107646_bib0018 article-title: Nearly optimal sparse Fourier transform – volume: 42 start-page: 3084 issue: 11 year: 1994 ident: 10.1016/j.sigpro.2020.107646_bib0001 article-title: The fractional Fourier transform and time-frequency representations publication-title: IEEE Trans. Signal Process. doi: 10.1109/78.330368 – volume: 65 start-page: 5716 issue: 21 year: 2017 ident: 10.1016/j.sigpro.2020.107646_bib0013 article-title: On performance of sparse fast Fourier transform and enhancement algorithm publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2017.2740198 – volume: 48 start-page: 1329 issue: 5 year: 2000 ident: 10.1016/j.sigpro.2020.107646_bib0002 article-title: The discrete fractional Fourier transform publication-title: IEEE Trans. Signal Process. doi: 10.1109/78.839980 – start-page: 1183 year: 2012 ident: 10.1016/j.sigpro.2020.107646_bib0022 article-title: Simple and practical algorithm for sparse Fourier transform – volume: 65 start-page: 2033 issue: 8 year: 2017 ident: 10.1016/j.sigpro.2020.107646_bib0014 article-title: An exact and fast computation of discrete Fourier transform for polar and spherical grid publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2016.2645510 – volume: 32 start-page: 675 issue: 3 year: 2018 ident: 10.1016/j.sigpro.2020.107646_bib0021 article-title: Kernel mixture model for probability density estimation in Bayesian classifiers publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-018-0550-5 – volume: 65 start-page: 6171 issue: 23 year: 2017 ident: 10.1016/j.sigpro.2020.107646_bib0006 article-title: Discrete fractional Fourier transforms based on closed-form Hermite-Gaussian-like DFT eigenvectors publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2017.2750105 – volume: 165 start-page: 72 year: 2019 ident: 10.1016/j.sigpro.2020.107646_bib0007 article-title: Computation of an eigendecomposition-based discrete fractional Fourier transform with reduced arithmetic complexity publication-title: Signal Process. doi: 10.1016/j.sigpro.2019.06.032 – year: 2019 ident: 10.1016/j.sigpro.2020.107646_bib0012 article-title: Fast detection method for low-observable maneuvering target via robust sparse fractional Fourier transform publication-title: IEEE Geosci. Remote Sens. Lett. Early Access – volume: 67 start-page: 3181 issue: 12 year: 2019 ident: 10.1016/j.sigpro.2020.107646_bib0017 article-title: Fractional Fourier analysis using the Möbius inversion formula publication-title: IEEE Trans. Signal Process doi: 10.1109/TSP.2019.2912878 – volume: 53 start-page: 2735 issue: 6 year: 2017 ident: 10.1016/j.sigpro.2020.107646_bib0019 article-title: The robust sparse Fourier transform (RSFT) and its application in radar signal processing publication-title: IEEE Trans. Aerosp. Electron. Syst. doi: 10.1109/TAES.2017.2714918 – volume: 48 start-page: 1338 issue: 5 year: 2000 ident: 10.1016/j.sigpro.2020.107646_bib0005 article-title: Closed-form discrete fractional and affine Fourier transforms publication-title: IEEE Trans. Signal Process. doi: 10.1109/78.839981 – volume: 44 start-page: 994 issue: 4 year: 1996 ident: 10.1016/j.sigpro.2020.107646_bib0003 article-title: The discrete rotational Fourier transform publication-title: IEEE Trans. Signal Process. doi: 10.1109/78.492554 – volume: 62 start-page: 6582 issue: 24 year: 2014 ident: 10.1016/j.sigpro.2020.107646_bib0010 article-title: Sparse discrete fractional Fourier transform and its applications publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2014.2366719 – volume: 31 start-page: 91 issue: 5 year: 2014 ident: 10.1016/j.sigpro.2020.107646_bib0009 article-title: Recent developments in the sparse Fourier transform: a compressed Fourier transform for big data publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2014.2329131 – volume: 2 start-page: 611 issue: 4 year: 2003 ident: 10.1016/j.sigpro.2020.107646_bib0023 article-title: Some new results for integrals involving the generalized Marcum Q function and their application to performance evaluation over fading channels publication-title: IEEE Trans. Wireless Commun. doi: 10.1109/TWC.2003.814344 – volume: 64 start-page: 4866 issue: 6 year: 2017 ident: 10.1016/j.sigpro.2020.107646_bib0020 article-title: Multimode process monitoring and fault detection: asparse modeling and dictionary learning method publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2017.2668987 – volume: 64 start-page: 451 issue: 1 year: 2018 ident: 10.1016/j.sigpro.2020.107646_bib0016 article-title: R-FFAST: a robust sub-linear time algorithm for computing a sparse DFT publication-title: IEEE Trans. Inform. Theory doi: 10.1109/TIT.2017.2679053 – start-page: 799 year: 2015 ident: 10.1016/j.sigpro.2020.107646_bib0025 article-title: Automatic human fall detection in fractional Fourier domain for assisted living – volume: 44 start-page: 2141 issue: 9 year: 1996 ident: 10.1016/j.sigpro.2020.107646_bib0004 article-title: Digital computation of the fractional Fourier transform publication-title: IEEE Trans. Signal Process. doi: 10.1109/78.536672 – volume: 64 start-page: 429 issue: 1 year: 2018 ident: 10.1016/j.sigpro.2020.107646_bib0015 article-title: FFAST: an algorithm for computing an exactly k-sparse DFT in O(klog k) time publication-title: IEEE Trans. Inform. Theory doi: 10.1109/TIT.2017.2746568 – volume: 62 start-page: 2594 issue: 10 year: 2015 ident: 10.1016/j.sigpro.2020.107646_bib0008 article-title: Real discrete fractional Fourier, Hartley, generalized Fourier and generalized Hartley transforms with many parameters publication-title: IEEE Trans. Circuits Syst. I Regul. Pap. doi: 10.1109/TCSI.2015.2468996 – start-page: 1139 year: 2015 ident: 10.1016/j.sigpro.2020.107646_bib0011 article-title: A fast algorithm for multi-component LFM signal analysis exploiting segmented DPT and SDFrFT |
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| Title | Optimized sparse fractional Fourier transform: Principle and performance analysis |
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