This paper introduces two new algorithms to accurately estimate the process noise covariance of a discrete-time Kalman filter online for robust orbit determination in the presence of dynamics model uncertainties. Common orbit determination process noise techniques, such as state noise compensation and dynamic model compensation, require offline tuning and a priori knowledge of the dynamical environment. Alternatively, the process noise covariance can be estimated through adaptive filtering. However, many adaptive filtering techniques are not applicable to onboard orbit determination due to computational cost or the assumption of a linear time-invariant system. Furthermore, existing adaptive filtering techniques do not constrain the process noise covariance according to the underlying continuous-time dynamical model, and there has been limited work on adaptive filtering with colored process noise. To overcome these limitations, a novel approach is developed which optimally fuses state noise compensation and dynamic model compensation with covariance matching adaptive filtering. This yields two adaptive and dynamically constrained process noise covariance estimation techniques. Unlike many adaptive filtering approaches, the new techniques accurately extrapolate over measurement outages and do not rely on ad hoc methods to ensure the process noise covariance is positive semi-definite. The benefits of the proposed algorithms are demonstrated through two case studies: an illustrative linear system and the autonomous navigation of two spacecraft orbiting an asteroid.

This paper develops a tight integrity risk bound for Residual-Based (RB) Advanced Receiver Autonomous Integrity Monitoring (ARAIM). ARAIM measurement models include nominal biases accounting for unknown but bounded errors, and faults of unbounded magnitude. In RB methods, upper bounding the integrity risk requires that one finds the worst-case directions of both the multi-satellite fault vector and of the all-in-view nominal bias vector. Previous methods only account for the worst-case fault direction assuming zero nominal bias. To address this issue, in this paper, we derive a new bounding method in parity space. The method establishes a direct relationship between mean estimation error and RB test statistic non-centrality parameter, which accounts for both faults and nominal errors. ARAIM performance is evaluated to quantify the improvement provided by the proposed method over previous approaches.

CubeSat swarm in LEO orbit is an attractive alternative to present-day expensive and bulky satellite-based remote sensing systems. This paper presents the design and optimization rules to achieve omnidirectional, high speed, long-range (more than 100 km) data communication among CubeSats. The unprecedented size, weight, power, and cost constraints imposed by the CubeSat platform and the availability of the commercial-off-the-shelf components are considered in the analyses. Analytical studies related to the scanning mirror-based beam steering system as well as scanning mirror's smallest step angle requirement are presented. In addition, we demonstrate the relations and dependencies among scanning mirror's smallest step angle, laser beam divergence, optics dimensions, communication distance, and scanning area filling efficiency, etc. Furthermore, the optimization challenges of the transmit laser beam size considering the interplay among beam divergence, beam clipping, and scattering are studied in detail. This paper also presents the effect of laser peak power, initial beam size, and communication distance on effective communication beam width to maintain a long-distance (more than 100 km) communication with SNR ≥ 10 dB at a data rate greater than 500 Mb/s.

This paper studies the air-to-ground ultra-wideband channel through propagation measurements between 3.1 GHz to 4.8 GHz using unmanned-aerial-vehicles (UAVs). Different line-of-sight (LOS) and obstructed-LOS scenarios and two antenna orientations were used in the experiments. Multipath channel statistics for different propagation scenarios were obtained, and the Saleh-Valenzuela model was found to provide a good fit for the statistical channel model. An analytical path loss model based on antenna gains in the elevation plane is provided for unobstructed UAV hovering and moving (in a circular path) propagation scenarios.

GNSS radio occultation (RO) signals have been demonstrated as a viable means to retrieve atmospheric profiles. Current GNSS-RO observations rely on open-loop (OL) processing of the signals, especially for signals propagating through the lower troposphere. The reason is that GNSS signals at low elevations are adversely affected by multipath effects due to propagation through lower troposphere structures and reflections and scattering from the Earth surface. The low-elevation RO signals are characterized by deep and fast amplitude fading and rapid signal carrier phase fluctuations, collectively referred to as signal scintillation. The conventional phase-lock loop (PLL) may lose lock of these signals. While OL tracking is known for its robustness, its accuracy is determined by the climatological models used to create the reference for the GNSS signal carrier tracking loop. The wide bandwidth typically associated with OL tracking also introduces large errors in signal parameters estimations. In this paper, we present an adaptive Kalman filter-based closed-loop (KFC) tracking method, which takes into consideration the tropospheric scintillation, platform vibration, and real-time C/N estimation of the RO signals. The KFC method has comparable robustness with and improved accuracy over the OL tracking, which are demonstrated through comparison using real GPS RO data collected on an airborne platform. Analysis of the excess Doppler estimation, retrieved bending angles and impact parameters also confirms the improved performances of the proposed algorithm over OL tracking.

A new information formulation of the Kalman filter is presented where the information matrix is parameterized as the product of an upper triangular matrix, a diagonal matrix, and the transpose of the triangular matrix (UDU factorization). The UDU factorization of the Kalman filter is known for its numerical stability, this work extends the technique to the information filter. A distinct characteristic of the new algorithm is that measurements can be processed as vectors, while the classic UDU factorization requires scalar measurement processing, i.e. a diagonal measurement noise covariance matrix.

Usually, bearing angle measurements are employed in triangulation methods to display the position of targets. However, in multi-radar and multi-target scenarios, triangulation approaches bring out ghosts that operate like real targets. This article proposes a target/ghost classifier that relies on the fact that the trajectory of a ghost is actually a function of trajectories of at least two targets and therefore, the complexity of a ghost trajectory is "greater" than the complexity of targets' trajectories.

This work intends to design, analyze and solve, from the systems control perspective, a complex, dynamic, and multiconstrained planning system for generating training plans for crew members of the NASA-led International Space Station. Various intelligent planning systems have been developed within the framework of artificial intelligence. These planning systems generally lack a rigorous mathematical formalism to allow a reliable and flexible methodology for their design, modeling, and performance analysis in a dynamical, time-critical, and multiconstrained environment. Formulating the planning problem in the domain of discrete-event systems under a unified framework such that it can be modeled, designed, and analyzed as a control system will provide a self-contained theory for such planning systems. This will also provide a means to certify various planning systems for operations in the dynamical and complex environments in space. The work presented here completes the design, development, and analysis of an intricate, large-scale, and representative mathematical formulation for intelligent control of a real planning system for Space Station crew training. This planning system has been tested and used at NASA-Johnson Space Center.