基于声波传播时间估计的气流速度测量方法
收稿日期: 2014-04-16
修回日期: 2014-09-05
网络出版日期: 2014-09-10
基金资助
国家自然科学基金(61172126);吉林省自然科学基金(20140101073JC)
Airspeed measurement method based on propagation time estimation of acoustic waves
Received date: 2014-04-16
Revised date: 2014-09-05
Online published: 2014-09-10
Supported by
National Natural Science Foundation of China (61172126);Natural Science Foundation of Jilin Province (20140101073JC)
为实现对亚声速和超声速气流速度的统一测量,提出了一种基于声传感器的新型测量方法。首先,根据声波在亚声速和超声速气流中的传播特性,利用特定的测量装置建立了声波传播时间与气流速度之间的数学模型,从而将气流速度的测量问题转化为声波传播时间的测量问题。然后,在此基础上,利用计时法和最大似然估计(MLE)方法来估计声波传播时间;其中,计时法在实时性上优势明显,而MLE方法则在可靠性上优于前者。最后,分别从阵元位置扰动性、计时误差和克拉美-罗界(CRB)3个方面对所提算法的性能进行了分析与仿真验证。结果表明,该算法能够实现对亚声速和超声速气流速度的精确测量。
虞飞 , 陶建武 , 钱立林 . 基于声波传播时间估计的气流速度测量方法[J]. 航空学报, 2015 , 36(4) : 1285 -1298 . DOI: 10.7527/S1000-6893.2014.0204
A novel airspeed measuring method based on acoustic sensors, applying to both subsonic and supersonic circumstances, is presented. Firstly,according to the propagation property of acoustic waves in subsonic and supersonic air current, the relationship between propagation time of acoustic waves and airflow velocity is derived for a given measuring equipment. Then, based on that, a timing method and a maximum likelihood estimation (MLE) algorithm are proposed to estimate the traveling time of acoustic waves. The timing method gains an advantage over the MLE algorithm in real-time, while the MLE algorithm is more reliable than the former. Finally, the performance of the proposed algorithms are verified by simulations and analyzed in terms of the perturbation of sensor position, the airspeed estimation error caused by timing error and the Cramér-Rao bound (CRB) on the estimation error of propagation time. The results show that the proposed algorithms are able to measure subsonic and supersonic airspeed with high accuracy.
[1] Whitmore S A, Cobleigh B R, Haering E A. Design and calibration of the X-33 flush airdata sensing (FADS) system, TM-1998-206540[R]. Washington D.C.: NASA, 1998.
[2] Baumann E, Pahle J W, Davis M C. X-43A flush airdata sensing system flight-test results[J]. Journal of Spacecraft and Rockets, 2010, 47(1): 48-61.
[3] Ellsworth J C. An analytical explanation for the X-43A flush air data sensing system pressure mismatch between flight and theory, AIAA-2010-4964[R]. Reston: AIAA, 2010.
[4] Wen R H, Zheng S D, Ye W. Development and current situation of flush air data sensing technologies[J]. Electronics Optics & Control, 2008, 15(8): 53-56 (in Chinese). 温瑞珩, 郑守铎, 叶玮. 嵌入式大气数据传感技术的发展现状[J]. 电光与控制, 2008, 15(8): 53-56.
[5] Nehorai A, Paldi E. Acoustic vector-sensor array processing[J]. IEEE Transactions on Signal Processing, 1994, 42(9): 2481-2491.
[6] Wu Y I, Wong K T, Lau S K. The acoustic vector-sensor's near-field array-manifold[J]. IEEE Transactions on Signal Processing, 2010, 58(7): 3946-3951.
[7] Wu Y I, Wong K T. Acoustic near-field source-localization by two passive anchor-nodes[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 159-169.
[8] Wang L X, Tao J W. A new measuring airdata algorithm[J]. Acta Metrologica Sinica, 2011, 32(1): 31-35 (in Chinese). 王立新, 陶建武. 一种新型的大气数据测量方法[J]. 计量学报, 2011, 32(1): 31-35.
[9] Wang L X, Tao J W, Guo W. Measurement of airdata based on acoustic vector sensor[C]//Proceedings of 2009 Civil Aviation Administration of China. Beijing: Chinese Automation Congress, 2009: 1-3 (in Chinese). 王立新, 陶建武, 郭伟. 基于矢量传感器的的大气数据测量方法[C]// 2009中国自动化大会暨两化融合高峰会议论文集. 北京: 中国自动化学会, 2009: 1-3.
[10] Chen C, Tao J W. Robust H∞ estimation of airspeed based on acoustic vector sensor array[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(2): 361-370 (in Chinese). 陈诚, 陶建武. 基于声矢量传感器阵列的鲁棒H∞空气速度估计算法[J]. 航空学报, 2013, 34(2): 361-370.
[11] Chen C, Tao J W, Zeng B. Estimation of airspeed in case of missing data[J]. Acta Electronica Sinica, 2014, 42(3): 491-497 (in Chinese). 陈诚, 陶建武, 曾宾. 数据缺失情形下的基于声矢量传感器阵列的空气流动速度估计算法[J]. 电子学报, 2014, 42(3): 491-497.
[12] Chen C, Tao J W. Estimation of airspeed for near-field based on acoustic vector sensor array[C]//Proceedings of 2012 Second International Conference on Electric Information and Control Engineering. Wuhan: Hubei Institute of Automation, 2012: 4593-4596.
[13] Yang X R, Chen Y. Atmospheric acoustics[M]. 2nd ed. Beijing: Science Press, 2007: 44-46 (in Chinese). 杨训仁, 陈宇. 大气声学[M]. 2版. 北京: 科学出版社, 2007: 44-46.
[14] Zeng B, Tao J W, Yu F, et al. A new estimation method of airspeed based on acoustic vector sensor[C]// Proceedings of 2013 3th International Conference on Signal Processing, Communications and Computing. Piscataway, NJ: IEEE, 2013: 307-310.
[15] Fan L Q, Zhou D Y. Aircraft aerodynamics[M]. Xi'an: Northwestern Polytechnical University Press, 1989: 47-57 (in Chinese). 范立钦, 周鼎义. 飞机空气动力学[M]. 西安: 西北工业大学出版社, 1989: 47-57.
[16] Sheng X H, Hu Y H. Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks[J]. IEEE Transactions on Signal Processing, 2005, 53(1): 44-53.
[17] Zhao S J, Zhao J X. Signal detection and estimation theory[M]. Beijing: Tsinghua University Press, 2005: 84-88 (in Chinese). 赵树杰, 赵建勋. 信号检测与估计理论[M]. 北京: 清华大学出版社, 2005: 84-88.
[18] Chen C, Tao J W, Zeng B. Estimation of airspeed based on acoustic vector sensor array[C]// Proceedings of 2012 11th International Conference on Signal Processing. Piscataway, NJ: IEEE, 2012: 307-310.
/
| 〈 |
|
〉 |
CJA