在直視性與非直視性混合環境下使用雙層交互性多模型演算法追蹤機動性的目標物

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姓名

魏周賢(Chou-Hsien Wei) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

在直視性與非直視性混合環境下使用雙層交互性多模型演算法追蹤機動性的目標物
(Maneuvering Mobile Tracking in Mixing LOS and NLOS Environments Using a New Two-Layered IMM Algorithm)

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摘要(中)

在無線通訊中位置定位是個很重要的課題，但非直視性(non-line-of-sight, NLOS) 誤差一直是影響目標物追蹤準確性相當重要的因素之一，為了提高無線定位在直視性 (line-of-sight, LOS) 與非直視性混合環境下的準確性和可靠性，我們考慮使用到達時間(Time of Arrival, TOA) 測量技術來對目標物做追蹤，在本篇論文中，NLOS 的測量被模擬成擾亂雜訊所呈現，標準的濾波器如擴展卡爾曼濾波器 (Extended Kalman Filter, EKF) ，在非高斯雜訊情況底下會產生很大的誤差，於是利用 Masreiez 濾波器 (MF) 克服這點，因此我們同時並行兩個濾波器在交互性多模型 (Interacting Multiple Model, IMM) 的框架中，在 LOS 環境時，EKF 可以有很高的精確度，而 MF 可以加強當 NLOS 傳輸時的狀態估計，但目標物行走在街道上不太可能只有等速度一種行為而已，所以我們加入了等加速與座標轉彎模型來改善追蹤性能，之後再次使用交互性多模型分別在 LOS 與 NLOS 的情況下對三種運動模型作相互間的狀態估計，這就是我們所提出的雙層交互性多模型 (Nested Interacting Multiple Model) 架構。在數值分析中，我們提出的演算法性能會優於只使用 IMMEKF 或者 IMMMF，並且我們提出的演算法的均方誤差 (Root Mean Square Error,RMSE) 會比較靠近 Cramer-Rao Lower Bound(CRLB)。

摘要(英)

Localization of mobile nodes is an important issue in wireless communications. The non-Gaussian noise resulted from the non-line-of-sight (NLOS) measurement greatly affects the tracking accuracy. In order to enhance the tracking reliability in mixing line-of-sight (LOS)and
NLOS environments, we develop a new algorithm for mobile node tracking based on time of arrival (TOA) measurements. In this thesis, two kinds of different non-Gaussian noises modeled by the mixture of Gaussian and Laplacian noises are considered as the NLOS noise. In non-Gaussian noise
environments, the extended Kalman filter (EKF) loses the optimality for state estimation. Here, we employ the Masreliez filter (MF) to deal with the non-Gaussian noise. Besides the concern of the noise model, three possible dynamic models, i.e., constant velocity motion (CV), constant acceleration motion (CA), and coordinated turn
(CT), are usually adopted for maneuvering target tracking. Since the conventional EKF has higher tracking precision in the LOS environment while the MF provides robust state estimation in the NLOS environment, a new interacting multiple model (IMM) algorithm possessing two conventional IMM operation in parallel, called Layered IMM algorithm, is
used to simultaneously accommodate two different measurement models and three different dynamic models. Numerical results show that the Layered IMM algorithm outperforms the conventional IMM-EKF algorithm and the IMM-MF algorithm. The root mean squared error (RMSE)analysis also indicates that the tracking error performance of the proposed algorithm is quite close to the posterior Cramer-Rao lower bound (CRLB)in steady state.

關鍵字(中)

★ 直視性
★ 非直視性
★ 擴展卡爾曼濾波器
★ Masreiez 濾波器
★ 交互性多模型

關鍵字(英)

論文目次

中文摘要 . . . . . . . . . . . . . . . . . . i
英文摘要 . . . . . . . . . . . . . . . . . . iii
目錄 . . . . . . . . . . . . . . . . . . i
圖目錄 . . . . . . . . . . . . . . . . . . ii
表目錄 . . . . . . . . . . . . . . . . . . iii
第 1 章序論 . . . . . . . . . . . . . . . . . . 1
1.1 前言 . . . . . . . . . . . . . . . . . . 1
1.2 章節架構 . . . . . . . . . . . . . . . . . . 5
第 2 章系統架構 . . . . . . . . . . . . . . . . . . 6
2.1 系統模型 . . . . . . . . . . . . . . . . . . 6
2.1.1 動態模型 . . . . . . . . . . . . . . . . . . 7
2.1.2 觀測模型 . . . . . . . . . . . . . . . . . . 13
2.2 NLOS . . . . . . . . . . . . . . . . . . 15
2.2.1 觀測距離偏差 . . . . . . . . . . . . . . . . . . 15
2.2.2 正偏差高斯雜訊 . . . . . . . . . . . . . . . . . . 16
2.2.3 閃爍雜訊 . . . . . . . . . . . . . . . . . . 16
第 3 章雙層交互性多模型演算法 . . . . . . . . . . . . . . . . 19
3.1 雙層交互性多模型濾波演算法. . . . . . . . . . . . . . . i9
3.1.1 交互性多模型擴展卡爾曼濾波器. . . . . . . . . . . . . 21
3.1.2 交互性多模型 Masreliez 濾波器 . . . . . . . . . . . . 29
3.1.3 雙層交互性多模型演算法 . . . . . . . . . . . . . . . 38
第 4 章Cramer-Rao Lower Bound . . . . . . . . . . . . . . 41
第 5 章系統模擬與結果分析 . . . . . . . . . . . . . . . . . . 43
5.1 Case1 . . . . . . . . . . . . . . . . . . 43
5.1.1 系統模擬參數 . . . . . . . . . . . . . . . . . . 44
5.1.2 模擬結果與討論 . . . . . . . . . . . . . . . . . . 48
5.2 Case2 . . . . . . . . . . . . . . . . . . 55
5.2.1 系統模擬參數 . . . . . . . . . . . . . . . . . . 56
5.2.2 模擬結果與討論 . . . . . . . . . . . . . . . . . . 59
第 6 章結論 . . . . . . . . . . . . . . . . . . 66
附錄 A：雅可比矩陣 H 推導 . . . . . . . . . . . . . . . . . . 67
參考文獻 . . . . . . . . . . . . . . . . . . 70

參考文獻

[1] J.C.Liberti and T. Rappaport, Smart Antennas for Wireless Communication:IS-95 and Third Generation CDMA Application.Prentice Hall, 1999.
[2] Y. Chan and K. Ho, “A simple and efficient estimator for hyperbolic location,”IEEE Trans. Signal Processing, vol. 42, no. 8, pp. 1905 –1915, Aug 1994.
[3] M. Malajner, P. Planinsic, and D. Gleich, “Angle of arrival estimation using rssi and omnidirectional rotatable antennas,”IEEE Sensors Journal, vol. 12, no. 6, pp. 1950 –1957, June 2012.
[4] X. Shen, J. W. Mark, and J. Ye, “Mobile location estimation in cellular networks using fuzzy logic,”in IEEE-VTS Fall VTC 2000. 52nd Vehicular Technology Conference, vol. 5, Boston, Sep 2000, pp. 2108 –2114.
[5] M. Spirito and A. Mattioli, “Preliminary experimental results of a gsm mobile phones positioning system based on timing advance,”in VTC 1999 - Fall. IEEE VTS 50th Vehicular Technology Conference,vol. 4, Sep 1999, pp. 1090–3038.
[6] M. Wylie and J. Holtzman, “The non-line of sight problem in mobile location estimation,”in IEEE International Conference on Universal Personal communications, vol. 2, Cambridge, Oct 1996, pp.
827 –831.
[7] U. Hammes and A. Zoubir, “Robust mt tracking based on m-estimation and interacting multiple model algorithm,”IEEE Trans.Signal Processing, vol. 59, no. 7, pp. 3398 –3409, July 2011.
[8] B. L. Le, K. Ahmed, and H. Tsuji, “Mobile location estimator with nlos mitigation using kalman filtering,”in IEEE Wireless Communications and Networking, vol. 3, New Orleans, March 2003, pp.1969 –1973.
[9] J.-F. Liao and B.-S. Chen, “Robust mobile location estimator with nlos mitigation using interacting multiple model algorithm,”IEEE Trans. Wireless Communications, vol. 5, no. 11, pp. 3002 –3006,Nov. 2006.
[10] W.-R. Wu, “Target tracking with glint noise,”IEEE Trans.Aerospace and Electronic Systems, vol. 29, no. 1, pp. 174 –185,Jan 1993.
[11] Y. Boers and J. Driessen, “Interacting multiple model particle filter,”IEE Proceedings Radar, Sonar and Navigation, vol. 150, no. 5,pp. 344 –349, oct. 2003.
[12] R. Guo, Z. Qin, X. Li, and J. Chen, “Interacting multiple model particle-type filtering approaches to ground target tracking,”Journal of Computers, vol. 3, pp. 23–30, 2008.
[13] X. Li and V. Jilkov, “Survey of maneuvering target tracking. part i.dynamic models,”IEEE Trans. Aerospace and Electronic Systems,vol. 39, no. 4, pp. 1333 –1364, Oct 2003.
[14] C. Fritsche, U. Hammes, A. Klein, and A.Zoubir, “Robust mobile terminal tracking in nlos environments using interacting multiple model algorithm,”in IEEE International Conference on Acoustics,
Speech and Signal Processing, Taipei, April 2009, pp. 1520–6149.
[15] M. McGuire, K. Plataniotis, and A.
Venetsanopoulos, “Data fusion of power and time
measurements for mobile terminal location,”IEEE Trans. Mobile Computing, IEEE Transactions on, vol. 4,
no. 2, pp. 142 –153, Feb. 2005.
[16] W.-R. Wu and A. Kundu, “Recursive filtering with non-gaussian noises,”IEEE Trans. Signal Processing, vol. 44, no. 6, pp. 1454 –1468, June 1996.
[17] D. Simon, “Kalman filtering with state constraints: a survey of linear and nonlinear algorithms,”IET Control Theory & Applications,vol. 4, no. 8, pp. 1303 –1318, Aug 2010.
[18] R. Adnan and A. . Z. Z. Ruslan, F.A. ;
Samad, “Extended kalman filter (ekf) prediction of flood water level,”in IEEE Control and System Graduate Research Colloquium (ICSGRC), Shah Alam, Selangor, July 2012, pp. 171 –174.
[19] B.-S. Chen, C.-Y. Yang, F.-K. Liao, and J.-F. Liao, “Mobile location estimator in a rough wireless environment using extended kalman-based imm and data fusion,”IEEE Trans. Vehicular Technology, vol. 58, no. 3, pp. 1157 –1169, March 2009.
[20] W.-R. Wu and P.-P. Cheng, “A nonlinear imm algorithm for maneuvering target tracking,”IEEE Trans. Aerospace and Electronic Systems, vol. 30, no. 3, pp. 875 –886, July 1994.
[21] N. S. Kumar and S. K. Kashyap, “Target tracking an non-gaussian environment,”Jan 2009.
[22] M. S. Arulampalam, B. Ristic, N. Gordon, and T. Mansell,“Bearings-only tracking of manoeuvring targets using particle filters,”EURASIP Journal on Advances in Signal Processing,vol. 2004, no. 15, p. 562960, 2004. [Online]. Available: http:
//asp.eurasipjournals.com/content/2004/15/562960
[23] P. Tichavsky and A. Muravchik, C.H. ; Nehorai, “Posterior cramer-rao bounds for discrete-time nonlinear filtering,”IEEE Trans. Signal Processing, vol. 46, no. 5, pp. 1386 –1396, May 1998.

指導教授

張大中(Dah-Chung Chang)

審核日期

2013-12-2

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