A new approach to maneuvring target tracking in passive multisensor environment

International audienceThis paper present a new approach to the multisensor Bearing-Only Tracking applications (BOT). Usually, a centralized data fusion scheme which involves a stacked vector of all the sensor measurements is applied using a single estimation filter which copes with the non-linear relation between the states and the measurements. The aforementioned approach is asymptotically optimal but suffers from the computational burden due to the augmented measurement vector and transmission aleas like delays generated by the bottleneck that occurs at the fusion center. Alternatively, since the Cartesian target positions can be determined by fusing at least 2 infrared sensor measurements in 2D case, one can use a local linear filter to estimate the target motion parameters, then a state fusion formula based on the Likelihood of the expected overall local measurements is applied to obtain the global estimate. The simulation results show that the proposed approach performance is equivalent to the centralized fusion schema in terms of tracking accuracy but exhibits the advantages of the decentralized fusion schema like parallel processing architecture and robustness against transmission delays. In addition, the low complexity of the obtained algorithm is well suited for real-time applications

A new approach in distributed multisensor tracking systems based on Kalman filter methods

International audienceIn multisensor tracking systems, the state fusion also known as track to track fusion is a crucial issue where the derivation of the best track combination is obtained according to a stochastic criteria in a minimum variance sense. Recently, sub-optimal weighted combination fusion algorithms involving matrices and scalars were developed. However, hence they only depend on the initial parameters of the system motion model and noise characteristics, these techniques are not robust against erroneous measures and unstable environment. To overcome this drawbacks, this work introduces a new approach to the optimal decentralized state fusion that copes with erroneous observations and system shortcomings. The simulations results show the effectiveness of the proposed approach. Moreover, the reduced complexity of the designed algorithm is well suited for real-time implementation