An optimal innovation based adaptive estimation Kalman filter for accurate positioning in a vehicular ad-hoc network

Sumithra, S. ; Vadivel, R.

International Journal of Applied Mathematics and Computer Science, Tome 31 (2021) no. 1, pp. 45-57.

Résumé

The vehicular ad-hoc network (VANET) is subject to various attacks because of its dynamic nature and ephemeral character. In VANET, vehicles communicate with each other for safety awareness. The positioning of an unknown vehicle is one of the critical factors to determine the vehicle’s trustworthiness. Although some positioning techniques have achieved a high accuracy level in VANET, they suffer from dynamic noise in real-world environments. This drawback leads to inaccuracy and unreliability during vehicle positioning. In this paper, an optimal innovation based adaptive estimation Kalman filter (OIAE-KF) is proposed. This algorithm offers an alternative solution for the basic Kalman filter and the innovation based adaptive estimation Kalman filter (IAE-KF). The proposed algorithm makes use of fusion of the global navigation satellite system (GNSS) and the inertial measurement unit (IMU) to improve its performance. The OIAE-KF works based on the innovation sequence and involves three steps such as establishing the innovation sequence, applying the innovation property, checking the optimality of the Kalman filter and, finally, estimating process noise (Q) and measurement noise (R). An optimal swapping method is introduced for optimality check. The efficiency of the proposed OIAE-KF method is proved by comparing the predictions of the existing methods such as the IAE-KF. The results show that the OIAE-KF performs better than the existing techniques. It improves the accuracy and consistency in VANET positioning.

Détail

Keywords: Kalman filter, dead reckoning, DR, global positioning system, GPS, simulation of urban mobility, SUMO
Mots-clés : filtr Kalmana, DR, system nawigacji satelitarnej, GPS, SUMO

Bibliographie

[1] [1] Al-Sultan, S., Al-Doori, M.M., Al-Bayatti, A.H. and Zedan, H. (2014). A comprehensive survey on vehicular ad hoc network, Journal of Network and Computer Applications 37: 380–392.

[2] [2] Alam, N. and Dempster, A.G. (2013). Cooperative positioning for vehicular networks: Facts and future, IEEE Transactions on Intelligent Transportation Systems 14(4): 1708–1717.

[3] [3] Amadeo, M., Campolo, C. and Molinaro, A. (2012). Enhancing IEEE 802.11 p/wave to provide infotainment applications in VANETs, Ad Hoc Networks 10(2): 253–269.

[4] [4] Bar-Shalom, Y., Li, X.R. and Kirubarajan, T. (2004). Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software, John Wiley, Hoboken.

[5] [5] Bidyuk, P., Podladchikov, V.D. and Podladchikova, I. (1993). Analytical study of the Kalman filter for stationary dynamic systems, AppliedMathematics and Computer Science 3(2): 313–328.

[6] [6] Byrski, W., Drapała, M. and Byrski, J. (2019). An adaptive identification method based on the modulating functions technique and exact state observers for modeling and simulation of a nonlinear MISO glass melting process, International Journal of Applied Mathematics and Computer Science 29(4): 739–757, DOI: 10.2478/amcs-2019-0055.

[7] [7] Chabir, K., Rhouma, T., Keller, J.Y. and Sauter, D. (2018). State filtering for networked control systems subject to switching disturbances, International Journal of Applied Mathematics and Computer Science 28(3): 473–482, DOI: 10.2478/amcs-2018-0036.

[8] [8] Chatterjee, A. and Matsuno, F. (2007). A neuro-fuzzy assisted extended Kalman filter-based approach for simultaneous localization and mapping (SLAM) problems, IEEE Transactions on Fuzzy Systems 15(5): 984–997.

[9] [9] Chen, X., Li, L. and Zhang, Y. (2010). A Markov model for headway/spacing distribution of road traffic, IEEE Transactions on Intelligent Transportation Systems 11(4): 773–785.

[10] [10] Ding, W., Wang, J., Rizos, C. and Kinlyside, D. (2007). Improving adaptive Kalman estimation in GPS/INS integration, The Journal of Navigation 60(3): 517.

[11] [11] ETSI (2010). TS 102 731 v1. 1.1—Intelligent Transport Systems (ITS); Security; Security Services and Architecture, European Telecommunications Standards Institute, Sophia-Antipolis.

[12] [12] Gao, W., Li, J., Zhang, Y., Wang, G. and Sun, X. (2017). Improved innovation-based adaptive estimation for measurement noise uncertainty in SINS/GNSS integration system, 2017 Forum on Cooperative Positioning and Service (CPGPS), Harbin, China, pp. 22–28.

[13] [13] Ghaleb, F.A., Zainal, A. and Rassam, M.A. (2016). Mobility information estimation algorithm using Kalman-filter for vehicular ad hoc networks, International Journal of Information and Computer Security 8(3): 221–240.

[14] [14] Golestan, K., Sattar, F., Karray, F., Kamel, M. and Seifzadeh, S. (2015). Localization in vehicular ad hoc networks using data fusion and V2V communication, Computer Communications 71: 61–72.

[15] [15] Havangi, R., Nekoui, M.A. and Teshnehlab, M. (2010). Adaptive neuro-fuzzy extended Kalman filtering for robot localization, Proceedings of the 14th International Power Electronics and Motion Control Conference (EPE-PEMC), Ohrid, Macedonia, pp. T5–130.

[16] [16] Hide, C., Moore, T. and Smith, M. (2004). Adaptive Kalman filtering algorithms for integrating GPS and low cost INS, PLANS 2004: Position Location and Navigation Symposium, Monterey, USA, pp. 227–233.

[17] [17] Hou, Y., Edara, P. and Sun, C. (2013). Modeling mandatory lane changing using Bayes classifier and decision trees, IEEE Transactions on Intelligent Transportation Systems 15(2): 647–655.

[18] [18] Hu, I. (2017). Extended Kalman filter approach for reducing traffic congestion in VANET, International Journal of Engineering Development and Research 5(2): 204–210.

[19] [19] Huang, C.-M. and Lin, S.-Y. (2014). Cooperative vehicle collision warning system using the vector-based approach with dedicated short range communication data transmission, IET Intelligent Transport Systems 8(2): 124–134.

[20] [20] Hubaux, J.-P., Capkun, S. and Luo, J. (2004). The security and privacy of smart vehicles, IEEE Security Privacy 2(3): 49–55.

[21] [21] Jiancheng, F. and Sheng, Y. (2011). Study on innovation adaptive EKF for in-flight alignment of airborne POS, IEEE Transactions on Instrumentation and Measurement 60(4): 1378–1388.

[22] [22] Kalman, R.E. (1960). A new approach to linear filtering and prediction problems, Transactions of the ASME—Journal of Basic Engineering 82(D): 35–45.

[23] [23] Kato, S., Tsugawa, S., Tokuda, K., Matsui, T. and Fujii, H. (2002). Vehicle control algorithms for cooperative driving with automated vehicles and intervehicle communications, IEEE Transactions on Intelligent Transportation Systems 3(3): 155–161.

[24] [24] Khattab, A., Fahmy, Y.A. and Abdel Wahab, A. (2015). High accuracy GPS-FREE vehicle localization framework via an ins-assisted single RSU, International Journal of Distributed Sensor Networks 11(5): 795036.

[25] [25] Korbicz, J., Podladchikov, V. and Bidyuk, P. (1994). Integration of multisensor measurements using modified Kalman filter, Applied Mathematics and Computer Science 4(1): 39–51.

[26] [26] Langbein, J. and Johnson, H. (1997). Correlated errors in geodetic time series: Implications for time-dependent deformation, Journal of Geophysical Research: Solid Earth 102(B1): 591–603.

[27] [27] Le, L., Festag, A., Baldessari, R. and Zhang, W. (2009). Vehicular wireless short-range communication for improving intersection safety, IEEE Communications Magazine 47(11): 104–110.

[28] [28] Lee, S. and Lim, A. (2013). An empirical study on ad hoc performance of DSRC and wi-fi vehicular communications, International Journal of Distributed Sensor Networks 9(11): 482695.

[29] [29] Li, J., Song, N., Yang, G. and Jiang, R. (2016). Fuzzy adaptive strong tracking scaled unscented Kalman filter for initial alignment of large misalignment angles, Review of Scientific Instruments 87(7): 075118.

[30] [30] Li, L., Wen, D., Zheng, N.-N. and Shen, L.-C. (2011). Cognitive cars: A new frontier for ADAS research, IEEE Transactions on Intelligent Transportation Systems 13(1): 395–407.

[31] [31] Li, W., Wang, Z., Wei, G., Ma, L., Hu, J. and Ding, D. (2015). A survey on multisensor fusion and consensus filtering for sensor networks, Discrete Dynamics in Nature and Society 2015, Article ID: 683701, DOI: 10.1155/2015/683701.

[32] [32] Liu, M. and Xiong, F. (2011). A fuzzy adaptive GPS/INS integrated navigation algorithm, Procedia Engineering 15: 660–664.

[33] [33] Loebis, D., Sutton, R., Chudley, J. and Naeem, W. (2004). Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system, Control Engineering Practice 12(12): 1531–1539.

[34] [34] Lobo, F., Grael, D., Oliveira, H., Villas, L., Almehmadi, A. and El-Khatib, K. (2019). Cooperative localization improvement using distance information in vehicular ad hoc networks, Sensors 19(23): 5231.

[35] [35] Lobo, J., Lucas, P., Dias, J. and De Almeida, A.T. (1995). Inertial navigation system for mobile land vehicles, Proceedings of the IEEE International Symposium on Industrial Electronics, Athens, Greece, Vol. 2, pp. 843–848.

[36] [36] Mehra, R. (1970). On the identification of variances and adaptive Kalman filtering, IEEE Transactions on Automatic Control 15(2): 175–184.

[37] [37] Milanés, V., Shladover, S.E., Spring, J., Nowakowski, C., Kawazoe, H. and Nakamura, M. (2013). Cooperative adaptive cruise control in real traffic situations, IEEE Transactions on Intelligent Transportation Systems 15(1): 296–305.

[38] [38] Mohamed, A. and Schwarz, K. (1999). Adaptive Kalman filtering for INS/GPS, Journal of Geodesy 73(4): 193–203.

[39] [39] Montillet, J.-P., Tregoning, P., McClusky, S. and Yu, K. (2012). Extracting white noise statistics in GPS coordinate time series, IEEE Geoscience and Remote Sensing Letters 10(3): 563–567.

[40] [40] Mrugalski, M. (2013). An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection, International Journal of Applied Mathematics and Computer Science 23(1): 157–169, DOI: 10.2478/amcs-2013-0013.

[41] [41] Parker, R. and Valaee, S. (2007). Vehicular node localization using received-signal-strength indicator, IEEE Transactions on Vehicular Technology 56(6): 3371–3380.

[42] [42] Rauh, A., Butt, S.S. and Aschemann, H. (2013). Nonlinear state observers and extended kalman filters for battery systems, International Journal of Applied Mathematics and Computer Science 23(3): 539–556, DOI: 10.2478/amcs-2013-0041.

[43] [43] Rezaei, S. and Sengupta, R. (2007). Kalman filter-based integration of DGPS and vehicle sensors for localization, IEEE Transactions on Control Systems Technology 15(6): 1080–1088.

[44] [44] Sarkka, S. and Nummenmaa, A. (2009). Recursive noise adaptive Kalman filtering by variational Bayesian approximations, IEEE Transactions on Automatic Control 54(3): 596–600.

[45] [45] Sierociuk, D. and Dzieliński, A. (2006). Fractional Kalman filter algorithm for the states, parameters and order of fractional system estimation, International Journal of Applied Mathematics and Computer Science 16(1): 129–140.

[46] [46] Skog, I. and Handel, P. (2009). In-car positioning and navigation technologies—A survey, IEEE Transactions on Intelligent Transportation Systems 10(1): 4–21.

[47] [47] Thiemann, C., Treiber, M. and Kesting, A. (2008). Estimating acceleration and lane-changing dynamics from next generation simulation trajectory data, Transportation Research Record 2088(1): 90–101.

[48] [48] Wang, H., Fu, G., Li, J., Yan, Z. and Bian, X. (2013). An adaptive UKF based SLAM method for unmanned underwater vehicle, Mathematical Problems in Engineering 2013, Article ID: 605981.

[49] [49] Wang, Z., Woodward, W.A. and Gray, H.L. (2009). The application of the Kalman filter to nonstationary time series through time deformation, Journal of Time Series Analysis 30(5): 559–574.

[50] [50] Woo, R., Yang, E.-J. and Seo, D.-W. (2019). A fuzzy-innovation-based adaptive Kalman filter for enhanced vehicle positioning in dense urban environments, Sensors 19(5): 1142.

[51] [51] Xi, C. and Cheng-dong, X. (2017). Performance analysis of multi-constellation GNSS in urban canyons based on fuzzy comprehensive evaluation, 29th Chinese Control and Decision Conference (CCDC), Chongqing, China, pp. 3040–3045.

[52] [52] Xu, Q., Mak, T., Ko, J. and Sengupta, R. (2004). Vehicle-to-vehicle safety messaging in DSRC, Proceedings of the 1st ACM International Workshop on Vehicular AD HOC Networks, Philadelphia, USA, pp. 19–28.

[53] [53] Yim, J., Joo, J. and Park, C. (2011). A Kalman filter updating method for the indoor moving object database, Expert Systems with Applications 38(12): 15075–15083.

[54] [54] Yu, M.-J. (2012). INS/GPS integration system using adaptive filter for estimating measurement noise variance, IEEE Transactions on Aerospace and Electronic Systems 48(2): 1786–1792.

[55] [55] ZhiWen, X., XiaoPing, H. and JunXiang, L. (2013). Robust innovation-based adaptive Kalman filter for INS/GPS land navigation, 2013 Chinese Automation Congress, Changsha, China, pp. 374–379.