Existing urban navigation algorithms employ integrity monitoring (IM) to mitigate the impact of measurement bias errors and determine system availability when estimating the position of a receiver. Many IM techniques, such as receiver autonomous integrity monitoring (RAIM), utilize measurement residuals associated with a single receiver position to provide integrity. However, identifying a single correct receiver position is often challenging in urban environments due to low satellite visibility and multiple measurements with bias errors. To address this, we propose Particle RAIM as a novel framework for robust state estimation and IM using GNSS and odometry measurements. Particle RAIM integrates residual-based RAIM with a particle filter and Gaussian mixture model likelihood to jointly perform state estimation and fault mitigation using a multimodal probability distribution of the receiver state. Our experiments on simulated and real-world data show that Particle RAIM achieves smaller positioning errors as well as smaller probability of false alarm and probability of missed-identification in determining system availability than existing urban localization and IM approachesin challenging environments with a relatively small computation overhead .
We propose a novel particle filter-based framework for robust state estimation and integrity monitoring in urban environments using GNSS and odometry measurements. Using a Gaussian mixture model (GMM) for computing particle weights, we develop an expectation-maximization algorithm for jointly inferring GMM weight parameters and a robust particle distribution of receiver state. From the inferred particle distribution and GMM, we determine the navigation system availability based on specified integrity requirements. Unlike traditional residual-based integrity monitoring algorithms that analyze measurement residuals from an estimated receiver position, we incorporate measurement residuals from multiple particles to estimate the receiver position and monitor integrity. Our method achieves small horizontal positioning errors compared to existing filter-based state estimation techniques on challenging simulated and real urban driving scenarios with multiple erroneous measurements. Through multiple simulations, we also show that our method determines system availability with comparable false alarms and missed identifications to best-performing existing integrity monitoring approaches .
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Existing urban navigation algorithms employ integrity monitoring (IM) to mitigate the impact of measurement bias errors and determine system availability when estimating the position of a receiver. Many IM techniques, such as receiver autonomous integrity monitoring (RAIM), utilize measurement residuals associated with a single receiver position to provide integrity. However, identifying a single correct receiver position is often challenging in urban environments due to low satellite visibility and multiple measurements with bias errors. To address this, we propose Particle RAIM as a novel
We propose a novel particle filter-based
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IM
integrity monitoring in urban environments
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Particle RAIM integrates residual-based RAIM with a particle filter and
Using a
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likelihood to jointly perform state estimation and fault mitigation using a multimodal probability distribution of the
(GMM) for computing particle weights, we develop an expectation-maximization algorithm for jointly inferring GMM weight parameters and a robust particle distribution of
R
Our experiments on simulated and real-world data show that Particle RAIM achieves smaller positioning errors as well as smaller probability of false alarm and probability of missed-identification in determining system availability than existing urban localization and IM approachesin challenging environments with a relatively small computation overhead
From the inferred particle distribution and GMM, we determine the navigation system availability based on specified integrity requirements. Unlike traditional residual-based integrity monitoring algorithms that analyze measurement residuals from an estimated receiver position, we incorporate measurement residuals from multiple particles to estimate the receiver position and monitor integrity. Our method achieves small horizontal positioning errors compared to existing filter-based state estimation techniques on challenging simulated and real urban driving scenarios with multiple erroneous measurements. Through multiple simulations, we also show that our method determines system availability with comparable false alarms and missed identifications to best-performing existing integrity monitoring approaches