基于自适应步长选择的周期格型线搜索估计

电子与自动控制

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

基于自适应步长选择的周期格型线搜索估计

叶浩欢, 柳征, 姜文利

国防科学技术大学电子科学与工程学院, 湖南长沙 410073

收稿日期:2011-11-18 修回日期:2012-01-11 出版日期:2012-08-25 发布日期:2012-08-23
通讯作者: 柳征 E-mail:nudtlz@163.com
基金资助:
国家自然科学基金(61002026)

Period Estimation via Lattice Line Search with Adaptive Step-size Selection

YE Haohuan, LIU Zheng, JIANG Wenli

College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073,China

Received:2011-11-18 Revised:2012-01-11 Online:2012-08-25 Published:2012-08-23
Supported by:
National Natural Science Foundation of China (61002026)

摘要/Abstract

摘要： 稀疏、含噪观测条件下周期点过程的周期估计是一个经典的信号处理问题。针对该问题,提出了一种格型线搜索(LLS)算法,该算法通过数值方式搜索似然函数的最大值,但其性能取决于人为预先选取的搜索步长。推导了一个步长计算公式,并利用该公式改进了LLS算法。改进的LLS算法能够自适应选择搜索步长,其达到的克拉美-罗界(CRLB)的信噪比(SNR)门限与最大似然估计(MLE)算法一致,但计算复杂度比后者低一个多的数量级。性能分析与仿真实验表明,所提算法比已有算法能更好地实现估计精度与复杂度的折中。

关键词: 周期估计, 周期点过程, 格型, 搜索步长, 脉冲重复周期

Abstract: A lattice line search (LLS) algorithm is employed to estimate the period of a periodic point process when the observations are sparse and noisy. However, the algorithm involves a numerical search for the maximum of the likelihood function which is previously identified, and the chosen step-size affects estimation performance. This paper focuses on the issue of step-size determination, for which a formula is developed. By using that formula, the LLS algorithm is modified to be able to adaptively determine a suitable step-size from the observation data. Compared with the maximum likelihood estimator (MLE), the modified algorithm can attain the Cramer-Rao lower bound (CRLB) at the same signal-to-noise ratio (SNR) threshold, but its computational complexity is lower by more than one order. Performance analysis and simulations show that the proposed estimator can achieve a better tradeoff between estimation accuracy and computational complexity than do the existing estimators.

Key words: period estimation, periodic point process, lattice, step-size, pulse repetition interval

中图分类号:

V243.2
TN95

叶浩欢, 柳征, 姜文利. 基于自适应步长选择的周期格型线搜索估计[J]. 航空学报, 2012, 33(8): 1498-1507.

YE Haohuan, LIU Zheng, JIANG Wenli. Period Estimation via Lattice Line Search with Adaptive Step-size Selection[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, 33(8): 1498-1507.

参考文献

[1] Wiley R G. Elint: the interception and analysis of radar signals. Norwood: Artech House, 2006.
[2] Liu J Y, Meng H D, Liu Y M, et al. Deinterleaving pulse trains in unconventional circumstances using multiple hypothesis tracking algorithm. Signal Processing, 2010, 90(8): 2581-2593.
[3] Fogel E, Gavish M. Performance evaluation of zero-crossing-based bit synchronizers. IEEE Transactions on Communications, 1989, 37(6): 1107-1121.
[4] Repko W L, Laurentius R, Valk V D. Method of recovering timing over a granular packet network: US, 20060269029A1. 2006-11-30.
[5] Emad A, Beaulieu N C. Lower bounds to the performance of bit synchronization for bandwidth efficient pulse-shaping. IEEE Transactions on Communications, 2010, 58(10): 2789-2794.
[6] Sadler B M, Casey S D. Sinusoidal frequency estimation via sparse zero crossings. Journal of the Franklin Institute, 2000, 337(2-3): 131-145.
[7] Sidiropoulos N D, Swami A, Sadler B M. Quasi-ML period estimation from incomplete timing data. IEEE Transactions on Signal Processing, 2005, 53(2): 733-739.
[8] Fogel E, Gavish M. Parameter estimation of quasi-periodic sequences. International Conference Acoustics, Speech, and Signal Processing (ICASSP), 1988: 2348-2351.
[9] Clarkson I V L. Approximate maximum-likelihood period estimation from sparse, noisy timing data. IEEE Transactions on Signal Processing, 2008, 56(5): 1779-1787.
[10] Quinn B G, Mckilliam R G, Clarkson I V L. Maximizing the periodogram. IEEE Global Telecommunications Conference, 2008: 3478-3482.
[11] Sadler B M, Casey S D. On periodic pulse pnterval analysis with outliers and missing observations. IEEE Transactions on Signal Processing, 1998, 46(11): 2990-3002.
[12] Mckilliam R, Clarkson I V L. Maximum-likelihood period estimation from sparse, noisy timing data. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2008: 3697-3700.
[13] Clarkson I V L, Howard S D, Mareels I M Y. Estimating the period of a pulse train from a set of sparse, noisy measurements. Fourth International Symposium on Signal Processing and Its Application, 1996: 885-888.
[14] Casey S D, Sadler B M. Modifications of the euclidean algorithm for isolating periodicities from a sparse set of noisy measurements. IEEE Transactions on Signal Processing, 1996, 44(9): 2260-2272.
[15] Conway J H, Sloane N J A. Sphere packings, lattices and groups. 3rd ed. Berlin: Springer-Verlag, 1998.
[16] Clarkson I V L. An algorithm to compute a nearest point in the lattice. Lecture Notes in Computer Science, 1999, 1719/1999: 104-120.
[17] Mckilliam R, Clarkson I V L. An algorithm to compute the nearest point in the lattice. IEEE Transactions on Information Theory, 2008, 54(9): 4378-4381.
[18] Rife D C, Boorstyn R R. Single-tone parameter estimation from discrete-time observations. IEEE Transactions on Information Theory, 1974, 20(5): 591-598.
[19] McKilliam R. Period estimation software. (2010-9-10). https://github.com/harprobey/pusim.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于自适应步长选择的周期格型线搜索估计

Period Estimation via Lattice Line Search with Adaptive Step-size Selection

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	刘劲, 韩雪侠, 宁晓琳, 陈晓, 康志伟. 基于EMD-CS的脉冲星周期超快速估计[J]. 航空学报, 2020, 41(8): 623486-623486.
[2]	许成维, 陶建武. 基于稀疏重构的混叠脉冲序列的周期估计[J]. 航空学报, 2018, 39(7): 322054-322054.
[3]	田旭, 李杰. 一种改进的果蝇优化算法及其在气动优化设计中的应用[J]. 航空学报, 2017, 38(4): 120370-120370.