Vol. 20
Latest Volume
All Volumes
PIERM 115 [2023] PIERM 114 [2022] PIERM 113 [2022] PIERM 112 [2022] PIERM 111 [2022] PIERM 110 [2022] PIERM 109 [2022] PIERM 108 [2022] PIERM 107 [2022] PIERM 106 [2021] PIERM 105 [2021] PIERM 104 [2021] PIERM 103 [2021] PIERM 102 [2021] PIERM 101 [2021] PIERM 100 [2021] PIERM 99 [2021] PIERM 98 [2020] PIERM 97 [2020] PIERM 96 [2020] PIERM 95 [2020] PIERM 94 [2020] PIERM 93 [2020] PIERM 92 [2020] PIERM 91 [2020] PIERM 90 [2020] PIERM 89 [2020] PIERM 88 [2020] PIERM 87 [2019] PIERM 86 [2019] PIERM 85 [2019] PIERM 84 [2019] PIERM 83 [2019] PIERM 82 [2019] PIERM 81 [2019] PIERM 80 [2019] PIERM 79 [2019] PIERM 78 [2019] PIERM 77 [2019] PIERM 76 [2018] PIERM 75 [2018] PIERM 74 [2018] PIERM 73 [2018] PIERM 72 [2018] PIERM 71 [2018] PIERM 70 [2018] PIERM 69 [2018] PIERM 68 [2018] PIERM 67 [2018] PIERM 66 [2018] PIERM 65 [2018] PIERM 64 [2018] PIERM 63 [2018] PIERM 62 [2017] PIERM 61 [2017] PIERM 60 [2017] PIERM 59 [2017] PIERM 58 [2017] PIERM 57 [2017] PIERM 56 [2017] PIERM 55 [2017] PIERM 54 [2017] PIERM 53 [2017] PIERM 52 [2016] PIERM 51 [2016] PIERM 50 [2016] PIERM 49 [2016] PIERM 48 [2016] PIERM 47 [2016] PIERM 46 [2016] PIERM 45 [2016] PIERM 44 [2015] PIERM 43 [2015] PIERM 42 [2015] PIERM 41 [2015] PIERM 40 [2014] PIERM 39 [2014] PIERM 38 [2014] PIERM 37 [2014] PIERM 36 [2014] PIERM 35 [2014] PIERM 34 [2014] PIERM 33 [2013] PIERM 32 [2013] PIERM 31 [2013] PIERM 30 [2013] PIERM 29 [2013] PIERM 28 [2013] PIERM 27 [2012] PIERM 26 [2012] PIERM 25 [2012] PIERM 24 [2012] PIERM 23 [2012] PIERM 22 [2012] PIERM 21 [2011] PIERM 20 [2011] PIERM 19 [2011] PIERM 18 [2011] PIERM 17 [2011] PIERM 16 [2011] PIERM 14 [2010] PIERM 13 [2010] PIERM 12 [2010] PIERM 11 [2010] PIERM 10 [2009] PIERM 9 [2009] PIERM 8 [2009] PIERM 7 [2009] PIERM 6 [2009] PIERM 5 [2008] PIERM 4 [2008] PIERM 3 [2008] PIERM 2 [2008] PIERM 1 [2008]
2011-08-29
Detection and Estimation of Multi-Component Polynomial Phase Signals by Constructing Regular Cross Terms
By
Progress In Electromagnetics Research M, Vol. 20, 143-153, 2011
Abstract
A regular cross terms algorithm is derived for the parameter estimation of the multi-component polynomial phase signals in additive white Gaussian noise. The basic idea is first to separate its phase parameters into two sets by nonlinear procedures£¬and then each set has half of the parameters in its auto-terms. Furthermore, using two linear transforms to deal with the two signals respectively, the phase coefficients of cross terms can be regulated for the identification and elimination of false peaks caused by the cross terms. Simulations are presented to illustrate the performance of the proposed algorithm.
Citation
Xihui Zhang Jingye Cai Lianfu Liu Yuanwang Yang , "Detection and Estimation of Multi-Component Polynomial Phase Signals by Constructing Regular Cross Terms," Progress In Electromagnetics Research M, Vol. 20, 143-153, 2011.
doi:10.2528/PIERM11071110
http://www.jpier.org/PIERM/pier.php?paper=11071110
References

1. Lim, K.-S. and V. C. Koo, "Design and construction of wideband Vna ground-based radar system with real and synthetic aperture measurement capabilities," Progress In Electromagnetics Research, Vol. 86, 259-275, 2008.
doi:10.2528/PIER08092204

2. Sabry, R. and P. W. Vachon, "Advanced polarimetric synthetic aperture radar (SAR) and electro-optical (Eo) data fusion through unified coherent formulation of the scattered EM field," Progress In Electromagnetics Research, Vol. 84, 189-203, 2008.
doi:10.2528/PIER08071005

3. Zhao, Y. W., M. Zhang, and H. Chen, "An effcient ocean SAR raw signal simulation by employing fast Fourier transform," Journal of Electromagnetic Waves and Applications, Vol. 24, No. 16, 2273-2284, 2010.
doi:10.1163/156939310793699064

4. Grouffaud, J., P. Larzabal, A. Ferreol, and H. Clergeot, "Adaptive maximum likelihood algorithms for the blind tracking of time-varying multipath channels," International Journal of Adaptive Control and Signal Processing, Vol. 12, No. 2, 207-222, 1998.
doi:10.1002/(SICI)1099-1115(199803)12:2<207::AID-ACS488>3.0.CO;2-H

5. Ikram, M. Z. and G. Tong Zhou, "Estimation of multicomponent polynomial phase signals of mixed orders," Signal Processing, Vol. 81, No. 11, 2293-2308, Nov. 2001.
doi:10.1016/S0165-1684(01)00095-0

6. Ferrari, A., C. Theys, and G. Alengrin, "Polynomial-phase signal analysis using stationary moments," Signal Processing, Vol. 54, No. 3, 239-248, Nov. 1996.
doi:10.1016/S0165-1684(96)00110-7

7. Angeby, J., "Estimating signal parameters using the nonlinear instantaneous least squares approach," IEEE Trans. Signal Process., Vol. 48, No. 10, 2721-2732, Oct. 2000.

8. Wu, Y., H. C. So, and H. Liu, "Subspace-based algorithm for parameter estimation of polynomial phase signals," IEEE Trans. Signal Process., Vol. 56, No. 10, Oct. 2008.

9. Peleg, S. and B. Porat, "Estimation and classification of polynomial phase signals," IEEE Trans. Inf. Theory, Vol. 37, 422-431, Mar. 1991.
doi:10.1109/18.75269

10. Wang, Y. and G. Zhou, "On the use of high-order ambiguity function for multi-component polynomial phase signals," Signal Processing, Vol. 65, No. 2, 283-296, Mar. 1998.
doi:10.1016/S0165-1684(97)00224-7

11. Wang, Y. and Y. C. Jiang, "New time-frequency distribution based on the polynomial Wigner-Ville distribution and L class of Wigner-Ville distribution," IET Signal Process., Vol. 4, No. 2, 130-136, 2010.
doi:10.1049/iet-spr.2009.0026

12. Pham, D. S. and A. M. Zobir, "Analysis of multicomponent polynomial phase signals," IEEE Trans. Signal Process., Vol. 55, No. 1, Jan. 2007.
doi:10.1109/TSP.2006.882085

13. Barbarossa, S., A. Scaglione, and G. B. Giannakis, "Product high-order ambiguity function for multicomponent polynomial-phase signal modeling," IEEE Trans. Signal Process., Vol. 46, 691-708, Mar. 1998.
doi:10.1109/78.661336

14. Barkat, B. and B. Boashash, "Design of higher order polynomial Wigner-Ville distributions," IEEE Trans. Signal Process., Vol. 47, No. 9, 2608-2611, Sep. 1999.
doi:10.1109/78.782225

15. Viswanath, G. and T. V. Sreenivas, "IF estimation using higher order TFRs," Signal Processing, Vol. 82, No. 2, 127-132, Feb. 2000.
doi:10.1016/S0165-1684(01)00168-2

16. O'Shea, P. and R. A. Wiltshire, "A new class of multilinear functions for polynomial phase signal analysis," IEEE Trans. Signal Process., Vol. 57, No. 6, Jun. 2009.
doi:10.1109/TSP.2009.2014811

17. Cornu, C., S. Stankovic, C. Ioana, A. Quinquis, and L. Stankovic, "Generalized representation of phase derivatives for regular signals," IEEE Trans. Signal Process., Vol. 55, No. 10, 4831-4838, Oct. 2007.
doi:10.1109/TSP.2007.896280

18. Wang, P., I. Djurovic, and J. Yang, "Generalized high-order phase function for parameter estimation of polynomial phase signal," IEEE Trans. Signal Process., Vol. 54, No. 7, 3023-3028, Jul. 2008.