Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation

International audienceTo perform long-term and long-range missions, underwater vehicles need reliable navigation algorithms. This paper considers multi-beam Terrain Aided Navigation which can provide a drift-free navigation tool. This leads to an estimation problem with implicit observation equation and unknown likelihood. Indeed, the measurement sensor is considered to be a numerical black box model that introduces some unknown stochastic noise. We introduce a measurement updating procedure based on an adaptive kernel derived from Approximate Bayesian Computational filters. The proposed method is based on two well-known particle filters: Regularized Particle Filter and Rao-Blackwellized Particle Filter. Numerical results are presented and the robustness is demonstrated with respect to the original filters, yielding to twice as less non-convergence cases. The proposed method increases the robustness of particle-like filters while remaining computationally efficient

Incidence, outcomes, and risk factors of pleural effusion in patients receiving dasatinib therapy for Philadelphia chromosome-positive leukemia.

Dasatinib, a second-generation BCR-ABL1 tyrosine kinase inhibitor, is approved for the treatment of chronic myeloid leukemia and Philadelphia chromosome-positive acute lymphoblastic leukemia, both as first-line therapy and after imatinib intolerance or resistance. While generally well tolerated, dasatinib has been associated with a higher risk for pleural effusions. Frequency, risk factors, and outcomes associated with pleural effusion were assessed in two phase 3 trials (DASISION and 034/Dose-optimization) and a pooled population of 11 trials that evaluated patients with chronic myeloid leukemia and Philadelphia chromosome-positive acute lymphoblastic leukemia treated with dasatinib (including DASISION and 034/Dose-optimization). In this largest assessment of patients across the dasatinib clinical trial program (N=2712), pleural effusion developed in 6-9% of patients at risk annually in DASISION, and in 5-15% of patients at risk annually in 034/Dose-optimization. With a minimum follow up of 5 and 7 years, drug-related pleural effusion occurred in 28% of patients in DASISION and in 33% of patients in 034/Dose-optimization, respectively. A significant risk factor identified for developing pleural effusion by a multivariate analysis was age. We found that overall responses to dasatinib, progression-free survival, and overall survival were similar in patients who developed pleural effusion and in patients who did not. clinicaltrials.gov identifier 00481247; 00123474

Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation

International audienceTo perform long-term and long-range missions, underwater vehicles need reliable navigation algorithms. This paper considers multi-beam Terrain Aided Navigation which can provide a drift-free navigation tool. This leads to an estimation problem with implicit observation equation and unknown likelihood. Indeed, the measurement sensor is considered to be a numerical black box model that introduces some unknown stochastic noise. We introduce a measurement updating procedure based on an adaptive kernel derived from Approximate Bayesian Computational filters. The proposed method is based on two well-known particle filters: Regularized Particle Filter and Rao-Blackwellized Particle Filter. Numerical results are presented and the robustness is demonstrated with respect to the original filters, yielding to twice as less non-convergence cases. The proposed method increases the robustness of particle-like filters while remaining computationally efficient

Bathymetry and Atomic Gravimetry Sensor Fusion for Autonomous Underwater Vehicle

International audienceTerrain-aided navigation provides a drift-free navigation approach for autonomous underwater vehicles. However, velocity is often tricky to estimate with conventional bathymetry (mono or multi-beam telemetry) sensors. Cold atom gravimetry is a promising absolute and autonomous additional sensor that is seldom considered for this kind of application. We investigate a multi-beam telemeter and gravimeter centralized fusion scenario and the resulting observability gain on velocity. To do so, an Adaptive Approximate Bayesian Computation Regularized Particle Filter is implemented and compared to conventional Regularized Particle Filter. Numerical results are presented and the robustness of the bathymetry and gravimetry fusion strategy is demonstrated, yielding less non-convergence cases and more accurate position and velocity estimation

Interacting Weighted Ensemble Kalman Filter applied to Underwater Terrain Aided Navigation

Terrain Aided Navigation (TAN) provides a driftfree navigation approach for Unmanned Underwater Vehicles. This paper focuses on an improved version of the Weighted Ensemble Kalman Filter (WEnKF) to solve the TAN problem. We analyze some theoretical limitations of the WEnKF and derive an improved version which ensures that the asymptotic variance of weights remains bounded. This improvement results in an enhanced robustness to nonlinearities in practice. Numerical results are presented and the robustness is demonstrated with respect to conventional WEnKF, yielding twice as less nonconvergence cases

Adaptive Approximate Bayesian Computational Particle Filters for Underwater Terrain Aided Navigation

To perform long-term and long-range missions, underwater vehicles need reliable navigation algorithms. This paper considers multi-beam Terrain Aided Navigation which can provide a drift-free navigation tool. This leads to an estimation problem with implicit observation equation and unknown likelihood. Indeed, the measurement sensor is considered to be a numerical black box model that introduces some unknown stochastic noise. We introduce a measurement updating procedure based on an adaptive kernel derived from Approximate Bayesian Computational filters. The proposed method is based on two well-known particle filters: Regularized Particle Filter and Rao-Blackwellized Particle Filter. Numerical results are presented and the robustness is demonstrated with respect to the original filters, yielding to twice as less non-convergence cases. The proposed method increases the robustness of particle-like filters while remaining computationally efficient