Abstract
In this paper, we study the problem fusion data from multi-sensors in presence of biases. We discuss both approaches, measurement data level, and track sensor level. A previous algorithm for local track fusion using a pseudo-equation with Jacobian matrix is presented for 2D sensors. In 2D case, this algorithm worked well and gave an equivalent performance and higher computational efficiency comparing with exact Kalman filter method. We extend the algorithm to 3D sensors to know how it works in this case. It is not totally straightforward when the computation of Jacobian matrix in 3D is very complex. We give the computation true Jacobian matrix for pseudo-equation using MATLAB and also give a simpler approximation Jacobian matrix. This helps to improve computational efficiency. The simulation compares the performance of the methods: Local track fusion and measurement fusion in the other cases as two sensors, four sensors, track fusion using Jacobian matrix, approximate Jacobian matrix.
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Pham, C.D., Bui Tang, B.N., Nguyen, Q.B., Le Tran, S. (2019). Multi-sensors 3D Fusion in the Presence of Sensor Biases. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 886. Springer, Cham. https://doi.org/10.1007/978-3-030-03402-3_9
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DOI: https://doi.org/10.1007/978-3-030-03402-3_9
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