基于分段信号相关累加的变速度多站联合直接定位方法

逯志宇; 任衍青; 巴斌; 王大鸣; 张杰

doi:10.7498/aps.66.020503

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摘要

在低信噪比条件下，基于时延和多普勒频移的直接定位算法在解决宽带信号源定位时精度较差.针对此问题，提出了一种基于分段信号相关累加的变速度多站联合直接定位算法，并给出了其克拉美罗下界.该算法利用多个变速度的观测站对信号进行接收，然后将同一观测站接收的目标信号分割成多段不重叠的短时信号，采用最大似然估计器，联合各段信号的时延、多普勒频移信息对目标进行直接定位.算法充分利用了观测信号包含的定位信息，并利用观测站速度的变化增加了目标位置信息，解决了分段信号联合估计带来的定位模糊问题，使定位精度进一步提高，增加了算法的实用性.仿真实验表明，较之传统直接定位算法，本文算法定位精度更高，尤其在低信噪比条件下更能逼近克拉美罗界.

关键词:

作者及机构信息

1.
解放军信息工程大学, 郑州 450000;

2.
解放军66159部队, 武汉 430000

通信作者: 逯志宇, zhiyulu1030@126.com

基金项目: 国家高技术研究发展计划（批准号：2012AA01A502，2012AA01A505）和国家自然科学基金（批准号：61401513）资助的课题.

参考文献

[1]	Oh D, Kim S, Yoon S H 2013 IEEE Trans. Wireless Commun. 12 3130
[2]	Lei H, So H C 2013 IEEE Trans. Signal Process. 61 4860
[3]	Ba B, Liu G C, Li T, Lin Y C, Wang Y 2015 Acta Phys. Sin. 64 078403 (in Chinese)[巴斌, 刘国春, 李韬, 林禹丞, 王瑜2015物理学报64 078403]
[4]	Lu Z Y, Wang D M, Wang J H, Wang Y 2015 Acta Phys. Sin. 64 150502 (in Chinese)[逯志宇, 王大鸣, 王建辉, 王跃2015物理学报64 150502]
[5]	Wang G, Li Y M, Nirwan A 2013 IEEE Trans. Veh. Technol. 62 853
[6]	Ho K C, Chan Y T 1997 IEEE Trans. Aerosp. Electron. Syst. 33 770
[7]	Chan Y T, Ho K C 2005 IEEE Trans. Signal Process. 53 2625
[8]	Bosse J, Ferréol A, Larzabal P 2011 IEEE Statistical Signal Processing Workshop Nice, France, June 2-6, 2011 p701
[9]	Weiss A J 2004 IEEE Signal Process Lett. 11 513
[10]	Weiss A J, Amar A 2005 EURASIP J. Adv. Signal Process. 1 37
[11]	Amar A, Weiss A J 2006 Digital Signal Process. 16 52
[12]	Bar-Shalom O, Weiss A J 2011 Signal Process. 91 2345
[13]	Pourhomayoun M, Fowler M L 2014 IEEE Trans. Aerosp. Electron. Syst. 50 2878
[14]	Vankayalapati N, Kay S, Ding Q 2014 IEEE Trans. Aerosp. Electron. Syst. 50 1616
[15]	Tirer T, Weiss A J 2016 IEEE Signal Process Lett. 23 192
[16]	Fu Z J, Sun X M, Liu Q, Zhou L, Shu J G 2015 IEICE Trans. Commun. 98 190
[17]	Ren Y J, Shen J, Wang J, Han J, Lee S Y 2015 J. Internet Technol. 16 317
[18]	Gu B, Sheng V S, Wang Z J, Ho D, Osman S, Li S 2015 Neural Networks 67 140
[19]	Wen X Z, Shao L, Xue Y, Fang W 2015 Inform. Sci. 295 395
[20]	Xie S D, Wang Y X 2014 Wireless Pers. Commun. 78 231
[21]	Amar A, Weiss A J 2008 IEEE Trans. Signal Process. 56 5500
[22]	Weiss A J 2011 IEEE Trans. Signal Process. 59 2513
[23]	Oispuu M, Nickel U 2010 International Itg Workshop on Smart Antennas IEEE 1 300
[24]	Bosse J, Ferreol A, Larzabal P 2013 IEEE Trans. Signal Process. 61 5485
[25]	Godrich H, Haimovich A M, Blum R S 2010 Information Theory IEEE Transactions on 56 2783
[26]	Li J Z, Yang L, Guo F C, Jiang W L 2015 Digital Signal Process. 48 58
[27]	Yeredor A, Angel E 2011 IEEE Trans. Signal Process. 59 1612

施引文献

[1]	Oh D, Kim S, Yoon S H 2013 IEEE Trans. Wireless Commun. 12 3130
[2]	Lei H, So H C 2013 IEEE Trans. Signal Process. 61 4860
[3]	Ba B, Liu G C, Li T, Lin Y C, Wang Y 2015 Acta Phys. Sin. 64 078403 (in Chinese)[巴斌, 刘国春, 李韬, 林禹丞, 王瑜2015物理学报64 078403]
[4]	Lu Z Y, Wang D M, Wang J H, Wang Y 2015 Acta Phys. Sin. 64 150502 (in Chinese)[逯志宇, 王大鸣, 王建辉, 王跃2015物理学报64 150502]
[5]	Wang G, Li Y M, Nirwan A 2013 IEEE Trans. Veh. Technol. 62 853
[6]	Ho K C, Chan Y T 1997 IEEE Trans. Aerosp. Electron. Syst. 33 770
[7]	Chan Y T, Ho K C 2005 IEEE Trans. Signal Process. 53 2625
[8]	Bosse J, Ferréol A, Larzabal P 2011 IEEE Statistical Signal Processing Workshop Nice, France, June 2-6, 2011 p701
[9]	Weiss A J 2004 IEEE Signal Process Lett. 11 513
[10]	Weiss A J, Amar A 2005 EURASIP J. Adv. Signal Process. 1 37
[11]	Amar A, Weiss A J 2006 Digital Signal Process. 16 52
[12]	Bar-Shalom O, Weiss A J 2011 Signal Process. 91 2345
[13]	Pourhomayoun M, Fowler M L 2014 IEEE Trans. Aerosp. Electron. Syst. 50 2878
[14]	Vankayalapati N, Kay S, Ding Q 2014 IEEE Trans. Aerosp. Electron. Syst. 50 1616
[15]	Tirer T, Weiss A J 2016 IEEE Signal Process Lett. 23 192
[16]	Fu Z J, Sun X M, Liu Q, Zhou L, Shu J G 2015 IEICE Trans. Commun. 98 190
[17]	Ren Y J, Shen J, Wang J, Han J, Lee S Y 2015 J. Internet Technol. 16 317
[18]	Gu B, Sheng V S, Wang Z J, Ho D, Osman S, Li S 2015 Neural Networks 67 140
[19]	Wen X Z, Shao L, Xue Y, Fang W 2015 Inform. Sci. 295 395
[20]	Xie S D, Wang Y X 2014 Wireless Pers. Commun. 78 231
[21]	Amar A, Weiss A J 2008 IEEE Trans. Signal Process. 56 5500
[22]	Weiss A J 2011 IEEE Trans. Signal Process. 59 2513
[23]	Oispuu M, Nickel U 2010 International Itg Workshop on Smart Antennas IEEE 1 300
[24]	Bosse J, Ferreol A, Larzabal P 2013 IEEE Trans. Signal Process. 61 5485
[25]	Godrich H, Haimovich A M, Blum R S 2010 Information Theory IEEE Transactions on 56 2783
[26]	Li J Z, Yang L, Guo F C, Jiang W L 2015 Digital Signal Process. 48 58
[27]	Yeredor A, Angel E 2011 IEEE Trans. Signal Process. 59 1612

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基于分段信号相关累加的变速度多站联合直接定位方法

1. 解放军信息工程大学, 郑州 450000;

2. 解放军66159部队, 武汉 430000

通信作者: 逯志宇, zhiyulu1030@126.com

基金项目:

国家高技术研究发展计划（批准号：2012AA01A502，2012AA01A505）和国家自然科学基金（批准号：61401513）资助的课题.

收稿日期: 2016-07-26

修回日期: 2016-11-03

刊出日期: 2017-01-20

摘要: 在低信噪比条件下，基于时延和多普勒频移的直接定位算法在解决宽带信号源定位时精度较差.针对此问题，提出了一种基于分段信号相关累加的变速度多站联合直接定位算法，并给出了其克拉美罗下界.该算法利用多个变速度的观测站对信号进行接收，然后将同一观测站接收的目标信号分割成多段不重叠的短时信号，采用最大似然估计器，联合各段信号的时延、多普勒频移信息对目标进行直接定位.算法充分利用了观测信号包含的定位信息，并利用观测站速度的变化增加了目标位置信息，解决了分段信号联合估计带来的定位模糊问题，使定位精度进一步提高，增加了算法的实用性.仿真实验表明，较之传统直接定位算法，本文算法定位精度更高，尤其在低信噪比条件下更能逼近克拉美罗界.

关键词:

An improved direct position determination method based on correlation accumulation of short-time signals with variable velocity receivers

1.
PLA Information Engineering University, Zhengzhou 450000, China;

2.
The 66159^th Unit of PLA, Wuhan 430000, China

Fund Project:

Project supported by the National High Technology Research and Development Program of China (Grant Nos. 2012AA01A502, 2012AA01A505) and the National Natural Science Foundation of China (Grant No. 61401513).

Received Date: 26 July 2016

Accepted Date: 03 November 2016

Published Online: 20 January 2017

Abstract: Wireless target location technology has been widely used in civil and military fields. In the two-step localization algorithms, the signal measurements, such as thetime of arrival, theangle of arrival, the frequency difference of arrival, etc., should be extracted first from the receivedsource signal. Then the target position is identified by calculating the location equation. Compared with the two-step localization algorithms, the direct position determination (DPD) method, which need not estimate the signal parameters and calculate the position step by step, but obtainsthe source position from the received signals directly based on the maximum likelihood criterion, has been shown to have a goodestimation accuracy and robustness, especially under low signal-to-noise ratio (SNR) conditions. So it has been widely studied in recent years and has made remarkable achievements in academic research. However, the DPD algorithm of wideband signals emitters is not performing well with moving receivers in the joint positioning based on time delay and Doppler shift under the low SNRs. To obtaina better positioning performance, in this paper we present a DPD algorithm with variable velocity receivers based on coherent summation of short-time signal segments, and derive the source position Cramer-Rao lower bound (CRLB). The algorithm designs a positioning model in which the multiple variable velocity receivers are usedtoobtain the source signal, then the signal received at the same receiver is patitioned into multiple non-overlapping short-time signal segments, based on which, an approximate maximum likelihood estimator for the new DPD algorithm is developed. The algorithm makes full use of the location information contained in the coherency among the signals segments, while extra target position information is acquired through the speed variability in the positioning model, and thus the problem of location ambiguity is solved. The simulation results show thatthe algorithm proposed in this paper further improves the positioning performance, and outperforms the traditional DPD algorithms with more accurate results. Especially in the low SNR, it is closer to the CRLB.

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