How Vehicles and Drones Find Their Way Using Many Sensors

This patent describes a layered computer system that combines data from many different sensors and an Inertial Measurement Unit to precisely determine a mobile platform's position, velocity, and orientation, even in challenging environments.

What it covers

This patent describes an adaptive navigation system for mobile platforms like aircraft, ground vehicles, or even dismounted soldiers. It uses a computer onboard the platform with a specific layered architecture to combine various data sources. First, a sensing layer gathers signals from multiple sensors, such as cameras, GPS, or lidar (Claim 5). Next, a processing layer prepares this raw sensor data, potentially using pre-loaded maps or landmark databases (Claim 6). A measurement abstraction layer then converts this data into a standard format, making it independent of the specific sensor hardware. Finally, a fusion layer combines this processed sensor data with movement information from an Inertial Measurement Unit (IMU) to calculate the platform's exact position, velocity, and attitude (Claim 1). For example, an unmanned aerial vehicle (UAV) preparing to land on a moving ship could use this system to fuse its camera images of the ship, GPS data, and its own IMU readings to precisely calculate its relative position to the ship, ensuring a safe landing (Claim 11).

What it doesn't cover

• —Does not cover navigation systems that do not include an Inertial Measurement Unit (IMU) onboard the mobile platform.
• —Does not cover systems that lack the specific layered architecture of sensing, processing, measurement abstraction, and fusion layers as described in the claims.
• —Does not cover navigation systems that rely solely on a single type of sensor without fusing it with IMU data and other sensor measurements.
• —Does not cover systems where the fusion calculations do not also produce error estimates, such as IMU error or sensor alignment error (Claim 2).
• —Does not cover navigation systems that do not produce a full 'platform navigation state' including position, velocity, and attitude.

The clever bit

The novelty lies in its adaptive, hierarchical architecture that intelligently fuses diverse sensor data with IMU information, and critically, incorporates geo-registration to continuously correct for accumulating errors over time, ensuring sustained accuracy.

Why it matters

This technology is crucial for autonomous systems that need to operate reliably in environments where traditional GPS signals might be unavailable, jammed, or spoofed. By fusing diverse sensor inputs with IMU data, it provides a robust and accurate positioning, navigation, and timing (PNT) solution. This capability is vital for military applications, such as guiding airborne weapons or operating unmanned aerial vehicles, where precision and resilience against signal interference are paramount.

Real-world examples

• 1.Autonomous military drones
• 2.Precision-guided munitions
• 3.Advanced aircraft navigation systems
• 4.Robotic ground vehicles for reconnaissance
• 5.Unmanned underwater vehicles (UUVs)