XAnalytix Systems
XA PORTFOLIO
XA PORTFOLIO
Mission Planning and Navigation
We develop capabilities for heterogeneous satellite constellations, in scenarios with missing or uncertain data. By leveraging advanced optimization techniques, data fusion, probabilistic models, and predictive analytics, we ensure efficient and autonomous satellite operations for varying mission scenarios.
Core Technologies:
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Optimization Algorithms: We develop state-of-the-art optimization models to enhance satellite coordination, resource allocation, and trajectory planning.
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Data Fusion: Integrate data from multiple sources—such as onboard sensors, ground stations, and inter-satellite communication—to generate accurate and reliable insights.
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Probabilistic Models: We utilize Bayesian inference and stochastic processes to handle uncertainty in satellite positioning, environmental conditions, and communication disruptions.
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Predictive Analytics: Machine learning-driven predictive models anticipate satellite behavior, space weather conditions, and orbital adjustments, enabling proactive decision-making.
Applications:
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Autonomous Mission Planning: Enabling coordinated multi-satellite operations with minimal human intervention.
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Resilient Navigation: Ensuring accurate positioning and navigation despite missing or degraded sensor data.
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Space Based Sensing (SBS): Enhancing the detection and tracking of objects through fused data sources.
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Optimized Communication Networks: Improving inter-satellite and ground communication by predicting optimal relay paths.
ACED
Altering Current-state to an Effective Desired-state
Active
SBIR: I, II
NAVWAR
CARDS
Collaborative Autonomy and Resilience of Distributed Satellites
Active
STTR : I, II
AFRL
NAVSEA
Passive Navigation Using a New Strapdown Vector Gravimeter
Active
SBIR: I, II
NAVSEA
CURE-C
Curating Uncertainty for Reliable Exploitation and Collaboration
Active
STTR: I, II
NASA
IAN-OSO
Improved Autonomous Navigation Through Robust Sensor Outlier Mitigation
Active
SBIR: I, STTR: II
NASA
Error Frame
Error-Frame Representation for Spacecraft Visual Relative Navigation
Complete
SBIR: I, II
NASA
MORSE
Multi-agent and Optimization Reasoner for Space Exploitation
Complete
STTR: I
NASA
SOAMI
Space Object Attitude Maneuver Indicator
STTR: I
Active
AFRL
CoOrbital
Co-Orbital Threat Prediction and Assessment
STTR: I, II
Active
AFRL
PIED
Photometric Inversion to Explicate Debris
SBIR: I, II
Active
AFRL
CROSS
Concepts of Resident Objects and Situations in Space
SBIR: I, II
Active
AFRL
PICASO
Predicated Intent Classification of Approaching Space Objects
SBIR: I
Complete
AFRL
Space Object Classification
We develop capabilities that advance space object classification solutions. Our mission is to transform raw observational data into precise, actionable intelligence by accurately characterizing space objects. We leverage cutting-edge uncertainty modeling, machine learning, and data fusion to assess object intent, predict behavior, and enhance space situational awareness (SSA).
Core Technologies:
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Uncertainty Representation: Utilizing probabilistic models to assess and quantify uncertainty, enabling robust predictions about object intent and behavior.
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Historical Behavior Analysis: Identifying patterns in past satellite movements to infer future actions and detect anomalies.
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Object Propagation Models: Predicting object trajectories with high precision, even in the presence of incomplete or noisy data.
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Machine Learning & Optimization: Leveraging supervised and unsupervised learning techniques to classify both labeled and unlabeled data efficiently.
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Data Fusion: Integrating hard (sensor-based) and soft (contextual or intelligence-based) data sources to improve classification accuracy.
Applications:
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Space Domain Awareness (SDA): Enhancing the detection, tracking, and classification of satellites and debris.
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Threat Assessment & Intent Prediction: Identifying potential adversarial maneuvers or unexpected behaviors.
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Autonomous Decision Support: Assisting defense, government, and commercial stakeholders in real-time space object classification.
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Collision Avoidance & Orbital Debris Monitoring: Providing predictive analytics to mitigate risks in increasingly congested orbits.
Enhancing Situational Awareness
We develop capabilities that provide decision-makers with advanced analytics, automated mission planning, and real-time threat assessment through cutting-edge solutions to improve Space Domain Awareness (SDA). By quantifying uncertainty, leveraging generative adversarial networks (GANs), and fusing multi-source intelligence, we empower analysts and operators to maintain security and operational advantage in an increasingly complex space environment.
Core Technologies:
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Uncertainty Quantification in Cislunar Space: Developing probabilistic models to account for sparse data and dynamic space conditions beyond geostationary orbit.
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Machine Decision-Making with Limited Data: Utilizing Conditional Generative Adversarial Networks (cGANs) to generate reliable inferences and predictions in data-scarce scenarios.
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Automated Mission Planning: Replacing manual processes with tools that optimize orbital maneuvers, asset allocation, and response strategies for operators.
Applications:
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Cislunar Space Domain Awareness: Enhancing tracking and characterization of objects in the Earth-Moon system.
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Autonomous Threat Detection & Assessment: Providing real-time analysis of potential threats based on observed and predicted behavior.
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Multi-INT Fusion for Decision Support: Integrating signals intelligence (SIGINT), imagery intelligence (IMINT), and other data sources for a comprehensive situational picture.
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Operational Planning & Automation: Reducing human workload by automating mission design, response strategies, and resource management.
EAGLE
Emerging Alerts with a General Learning Engine
Active
STTR: I
AFRL
HOSUM
High-Order Statistical Uncertainty Management
Active
SBIR: II
AFRL
RESCUE
Resilient Environment in Satellite Communication with Uninterrupted Effectiveness
Active
SBIR: II
AFRL
MULT2A
Multiple Threat Tracking and Analysis
Active
STTR: I
AFRL
NUANCE
Navigation Uncertainty Analysis in the Cislunar Environment
Active