Safety-critical navigation applications require that estimation errors be reliably quantified and bounded. This can be challenging for linear dynamic systems if the process noise or measurement errors have uncertain time correlation. In many systems (e.g., in satellite-based or inertial navigation systems), there are sources of time-correlated sensor errors that can be well modeled using Gauss-Markov processes (GMP). However, uncertainty in the GMP parameters, particularly in the correlation time constant, can cause misleading error bounds. In this paper, we develop time-correlated models that ensure tight upper bounds on the estimation error variance, assuming that the actual error is a stationary first-order GMP with a variance and time constant that are only known to reside within an interval. We first use frequency-domain analysis to derive stationary GMP models in both the continuous and discrete time domains, which outperform models previously described in the literature. Then, we derive an even tighter estimation error bound using a non-stationary GMP model, for which we determine the minimum initial variance that guarantees bounding conditions. Both models can easily be implemented in a linear estimator like the Kalman filter.

The robust detection of GNSS non-line-of-sight (NLOS) signals is of vital importance for land- and close-to-land-based safe navigation applications. The usage of GNSS measurements affected by NLOS can lead to large unbounded positioning errors and loss of safety. Due to the complex signal conditions in urban environments, the use of machine learning or artificial intelligence techniques and algorithms has recently been identified as potential tools to classify GNSS LOS/NLOS signals. The design of machine learning algorithms with GNSS features is an emerging field of research that must, however, be tackled carefully to avoid biased estimation results and to guarantee algorithms that can be generalized for different scenarios, receivers, antennas, and their specific installations and configurations. This work first provides new options to guarantee a proper generalization of trained algorithms by means of a pre-normalization of features with models extracted in open-sky (nominal) scenarios. The second main contribution focuses on designing a branched (or parallel) machine learning process to handle the intermittent presence of GNSS features in certain frequencies. This allows to exploit measurements in all available frequencies as compared to current approaches in the literature based on only the single frequency. The detection by means of logistic regression not only provides a binary LOS/NLOS decision but also an associated probability which can be used in the future as a means to weight-specific measurements. The detection with the proposed branched logistic regression with pre-normalized multi-frequency features has shown better results than the state-of-the-art algorithms, reaching 90% detection accuracy in the validation scenarios evaluated.

Prior work established a model for uncertain Gauss‐Markov (GM) noise that is guaranteed to produce a Kalman filter (KF) covariance matrix that overbounds the estimate error distribution. The derivation was conducted for the continuous‐time KF when the GM time constants are only known to reside within specified intervals. This paper first provides a more accessible derivation of the continuous‐time result and determines the minimum initial variance of the model. This leads to a new, non‐stationary model for uncertain GM noise that we prove yields an overbounding estimate error covariance matrix for both sampled‐data and discrete‐time systems. The new model is evaluated using covariance analysis for a one‐dimensional estimation problem and for an example application in Advanced Receiver Autonomous Integrity Monitoring (ARAIM).

Communication, Navigation and Surveillance (CNS) technologies are key enablers for future safe operation of drones in urban environments. However, the design of navigation technologies for these new applications is more challenging compared to e.g., civil aviation. On the one hand, the use cases and operations in urban environments are expected to have stringent requirements in terms of accuracy, integrity, continuity and availability. On the other hand, airborne sensors may not be based on high-quality equipment as in civil aviation and solutions need to rely on tighter multisensor solutions, whose safety is difficult to assess. In this work, we first provide some initial navigation requirements related to precision approach operations based on recently proposed vertiport designs. Then, we provide an overview of a possible multisensor navigation architecture solution able to support these types of operations and we comment on the challenges of each of the subsystems. Finally, initial proof of concept for some navigation sensor subsystems is presented based on flight trials performed during the German Aerospace Center (DLR) project HorizonUAM.

The project City-ATM, launched by the German Aerospace Center DLR in 2018, aims to integrate new airspace users like unmanned aerial vehicles into uncontrolled airspace. An air traffic management and traffic flow control concept were developed. The phase 1 demonstration in 2019 showed a bridge inspection featuring, amongst other elements, the flight of several drones in a limited area, beyond visual line of sight operation, strategic flight planning considering strategic geo-fences and tactical conflict detection. To enrich the base concept from phase 1 with more U-Space services, the concept has been extended for phase 2 by dynamic geo-fencing for the tactical avoidance of danger spots. This paper describes how dynamic geo-fences can be modeled around a hazard area, how the dynamic opening and closure of these fences can be distributed to relevant systems, and how the fences are considered by on-ground and already in air vehicles. The complete concept has been implemented and tested at the National Experimental Test Center in Cochstedt. Finally, the execution of successful flight trials is described.

This paper derives an automated method to obtain models for time-correlated noise that are guaranteed to produce an upper bound on the Kalman filter (KF) estimate error covariance matrix. The noise is assumed to be zero-mean Gaussian and stationary over the filtering duration, but otherwise has no known structure. We first show that the covariance matrix predicted by the KF upper bounds the true error covariance matrix when the noise model's power spectral density (PSD) function exceeds the true PSD at every frequency. An approach is then developed to automatically obtain autoregressive models up to second order that satisfy this criterion. The method is evaluated using covariance analysis for an example application in GPS-based relative positioning