Object Trajectory Simulation Using Camera and Radar Fusion
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Solution Overview
Problem
Conventional methods for simulating object trajectories are limited by inaccuracies in flight parameter determination due to unreliable image quality, over-reliance on radar data, and hardware limitations such as low-cost cameras with slow frame rates, leading to unreliable and computationally intensive processes.
Innovation Solution
A software-based approach that utilizes a camera and radar data to determine flight parameters, including speed, spin axis, and launch angle, by analyzing image sequences and incorporating neural networks for improved accuracy and flexibility across various devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional methods use limited flight parameter inputs from cameras, then device complexity is reduced, but measurement precision of flight parameters deteriorates
Solution Approach 1:
The patent combines multiple data sources (camera image sequences, radar data, accelerometer data) and processing methods (image processing, neural networks, flight physics models) into an integrated system. This merging allows the system to maintain relatively simple individual components while achieving high measurement precision through their coordinated interaction.
Solution Approach 2:
The system is designed to process multiple types of input data (visual data from cameras, radar data, accelerometer data) and perform multiple functions (trajectory simulation, flight parameter determination, landing position prediction). This multi-functionality allows a single system to achieve high precision across various measurement tasks without requiring separate specialized devices for each function.
2Reliability
If conventional methods rely on low-cost cameras with slow frame rates, then device cost is reduced, but reliability of trajectory simulation deteriorates
Solution Approach 1:
The patent introduces neural networks as intermediary processing layers that bridge the gap between low-frame-rate camera data and high-precision trajectory requirements. The neural networks interpolate and predict intermediate states, effectively mediating between the limited input data rate and the high reliability output requirements.
Solution Approach 2:
The system replaces reliance on high mechanical frame rates with computational methods. Instead of requiring cameras to capture every moment of flight through high frame rates, the system uses neural networks and flight physics models to computationally reconstruct the trajectory, substituting mechanical sampling with intelligent computation.
3Ease of operation
If conventional methods use erroneous flight parameter inputs, then ease of operation is improved, but measurement precision of trajectory simulation deteriorates
Solution Approach 1:
The system implements feedback mechanisms where neural networks continuously refine flight parameter estimates by comparing predicted trajectories with actual observed positions. This feedback loop allows the system to maintain operational simplicity while automatically correcting errors in flight parameter inputs through iterative refinement.
Solution Approach 2:
The system performs preliminary processing and validation of flight parameter inputs using neural networks before trajectory simulation. This preliminary action filters and corrects erroneous inputs in advance, ensuring that the main trajectory simulation receives high-quality data without requiring complex manual verification steps.
Data Source
AI summary
A method may include receiving a group of images taken by a camera over time in an environment, in which the camera may be oriented within the environment to capture images of an object in a substantially same direction as a launch direction of the object, and the group of images including a first image and a second image. The method may further include: identifying a first position of the object in the first image; identifying a second position of the object in the second image; generating a flight vector based on the first position of the object and the second position of the object; and determining one or more flight parameters using the flight vector. Additionally, the method may include: generating a simulated trajectory of the object based on the flight parameters; and providing the simulated trajectory of the object for presentation in a graphical user interface.