Combination of Maximum Correntropy Criterion & α-Rényi Divergence for a Robust and Fail-Safe Multi-Sensor Data Fusion

A combination of a robust optimality criterion, the Maximum Correntropy Criterion (MCC), and a powerful Fault Detection and Exclusion (FDE) strategy for a robust and fault-tolerant multi-sensor fusion approach is presented in this paper taking advantage of the information theory. The used estimator is called the MCCNIF, which is in the Nonlinear Information Filter (NIF) under the MCC. The NIF deals well with Gaussian noises but, its performance decreases when abruptly facing heavy non-Gaussian noises causing a divergence. Conversely, the NIF deals fairly with nonlinearity problems. Hence, to deal with non-Gaussian noises, the MCC shows good performance especially with shot noises and Gaussian mixture noises. To detect and exclude the erroneous measurements, an FDE layer, based on α-Rényi Divergence (α-RD) between the a priori and a posteriori probability distributions, is created. Then an adaptive threshold is calculated as a decision support based on the α-Rényi criterion (α-Rc).In order to test in real conditions the proposed framework, an autonomous vehicle multi-sensor localization example is taken. Indeed, for this application, in stringent environments (such as urban canyon, building, forests…), it is necessary to ensure both integrity and accuracy. The proposed solution is to combine the Global Navigation Satellite System (GNSS) data with the odometer (odo) data by a tight integration. The main contributions of this paper are the design and development of unique framework integrating a robust filter the MCCNIF and an FDE method using residual based on α-RD with an adaptive threshold. Real experimental data are presented and encourages the validation of the proposed approach.