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

• Published in: International journal of applied mathematics and computer science Vol. 31; no. 1; pp. 45 - 57
• Main Authors: Sumithra, S., Vadivel, R.
• Format: Journal Article
• Language: English
• Published: Zielona Góra Sciendo 01.03.2021; De Gruyter Poland
• Subjects: Adaptive filters; Algorithms; dead reckoning (DR); Global navigation satellite system; global positioning system (GPS); Inertial platforms; innovation; Innovations; Kalman filter; Kalman filters; Mobile ad hoc networks; Noise measurement; Optimization; simulation of urban mobility (SUMO)
• ISSN: 1641-876X; 2083-8492
• DOI: 10.34768/amcs-2021-0004

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.