Doppler Radar Motion Compensation Using Integrated GNSS and IMU
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Solution Overview
Problem
Conventional remote sensing systems, such as radar and sonar, suffer from measurement errors due to motion, leading to inaccurate data and imagery, particularly in dynamic environments, which can compromise navigation and situational awareness for mobile structures.
Innovation Solution
A remote sensing imagery system incorporating radar assemblies with integrated orientation and position sensors (OPS) and a logic device to provide accurate Doppler radar imagery by directly measuring antenna orientation and velocity relative to an inertial frame, using a GNSS compass to correct for bias and drift, and employing a combination of GNSS and IMU to reduce errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional remote sensing systems are used, then the system is simple and inexpensive, but measurement errors increase due to motion, reducing data reliability
Solution Approach 1:
The patent combines multiple sensor types (GPS for position, compass for orientation, accelerometers for motion detection) into an integrated sensor system that works together to compensate for motion effects. This merging of sensors allows the system to maintain high data reliability while managing complexity through coordinated operation of the combined components.
Solution Approach 2:
The patent introduces an intermediary processing system that receives raw sensor data from multiple sources, processes this data to calculate motion compensation parameters, and applies these corrections to the radar measurements. This intermediary layer isolates the complexity of motion compensation from both the raw sensors and the final imaging process.
2Measurement precision
If motion compensation techniques are applied, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent performs preliminary calculations of motion compensation parameters using GPS and compass data before the radar imaging process. By pre-calculating the vessel's position, orientation, and velocity, the system prepares correction data in advance, which simplifies the actual imaging process and reduces real-time processing complexity while maintaining high measurement precision.
Solution Approach 2:
The patent implements a feedback mechanism where motion sensor data continuously updates the compensation parameters during the radar imaging process. The system monitors changes in vessel motion and dynamically adjusts the Doppler compensation accordingly, ensuring high velocity measurement precision without requiring overly complex fixed algorithms.
3Reliability
If multiple sensors are integrated, then error reduction improves, but ease of operation decreases
Solution Approach 1:
The patent implements self-service functionality where the integrated sensor system automatically calibrates and compensates for motion effects without requiring manual intervention. The system autonomously processes sensor data, calculates compensation parameters, and applies corrections to radar images, eliminating the need for operators to manually adjust multiple sensor settings and reducing operational complexity despite the presence of multiple sensors.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides highly accurate and reliable remote sensing data and imagery, reducing errors and enhancing navigation by directly measuring target radial velocity and compensating for motion-related inaccuracies, thereby improving situational awareness for mobile structures.
Implementation Method 1
A remote sensing imagery system may include radar assemblies, other remote sensing assemblies, and logic devices in communication with the various assemblies
Implementation Method 2
using a GNSS compass to correct for bias and drift
Implementation Method 3
employing a combination of GNSS and IMU to reduce errors
Implementation Method 4
determine a target radial speed corresponding to the detected target, and then generate remote sensor image data based on the remote sensor returns and the target radial speed
Data Source
AI summary
Techniques are disclosed for systems and methods to provide remote sensing imagery for mobile structures. A remote sensing imagery system includes a radar assembly mounted to a mobile structure and a coupled logic device. The radar assembly includes an orientation and position sensor (OPS) coupled to or within the radar assembly and configured to provide orientation and position data associated with the radar assembly. The logic device is configured to receive radar returns corresponding to a detected target from the radar assembly and orientation and/or position data corresponding to the radar returns from the OPS, determine a target radial speed corresponding to the detected target, and then generate remote sensor image data based on the remote sensor returns and the target radial speed. Subsequent user input and/or the sensor data may be used to adjust a steering actuator, a propulsion system thrust, and/or other operational systems of the mobile structure.