Arbitrary IQ Stream Injection with Doppler-Aware GNSS Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing GNSS receiver testing systems struggle to simulate complex, real-world scenarios with spatial awareness, particularly in motion, due to the limitations of FPGAs in generating high-bandwidth GNSS signals and the need for precise encoding and decoding, which are expensive and difficult to program, while also facing challenges from jamming, spoofing, and adjacent band interference.
Innovation Solution
The technology utilizes GPUs and FPGAs to synthesize IQ streams with spatial awareness, incorporating motion effects like Doppler shift and range, by configuring a test signal with a specified start time, and merging conditioned IQ signals with satellite constellations to simulate realistic scenarios for GNSS, Wi-Fi, and 5G receivers in motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If FPGAs are used to generate high-bandwidth GNSS signals, then signal generation capability is improved, but device complexity and programming difficulty increase
Solution Approach 1:
The patent uses GPUs to replicate the signal generation function that was previously performed by FPGAs. The GPU-based system copies the essential functionality of FPGA signal generation but leverages the GPU's parallel processing architecture to achieve high-bandwidth GNSS signal generation with simplified programming through standard CUDA or OpenGL APIs instead of complex FPGA verification languages.
Solution Approach 2:
The patent substitutes the FPGA hardware-based signal generation mechanism with a software-based GPU processing mechanism. This replacement transitions from fixed-function hardware logic to flexible software rendering pipelines, maintaining signal generation capability while reducing programming complexity through graphics API abstractions.
2Reliability
If adaptive antennas are tested in motion scenarios, then testing thoroughness is improved, but measurement precision requirements increase
Solution Approach 1:
The patent introduces a virtual simulation environment as an intermediary between the physical adaptive antenna system and the test measurements. The GPU-based virtual GNSS signal generator creates realistic signal scenarios including Doppler shift and spatial relationships, allowing thorough motion scenario testing while the virtual environment handles the complex spatial awareness calculations that would otherwise require extreme measurement precision.
Solution Approach 2:
The patent performs preliminary calculation of Doppler shift, spatial relationships, and signal characteristics in the virtual environment before the actual physical testing. By pre-computing these parameters based on known receiver trajectories and satellite positions, the system enables thorough motion testing without requiring the measurement system to calculate these values in real-time with extreme precision.
3Adaptability or versatility
If virtual signal sources are added to test environments, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent uses the GPU's existing graphics processing architecture to perform multiple functions: rendering virtual scenes, generating GNSS signals, applying Doppler effects, and managing signal injection. By leveraging the universal parallel processing capability of GPUs, the system achieves high adaptability for various test scenarios without adding dedicated hardware for each function, thus avoiding proportional increases in device complexity.
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
This approach enables thorough testing of adaptive antennas and CRPA systems, providing precise simulation of GNSS signals with motion effects, enhancing anti-jamming and anti-spoofing capabilities, and supporting various satellite constellations and interference scenarios.
Implementation Method 1
using distance and relative motion between the receiver and the IQ stream transmitter to determine delay and Doppler frequency shift between the IQ stream transmitter and the receiver in motion
Data Source
AI summary
Disclosed is incorporating an IQ stream into a test signal for a receiver in motion, configuring a path for the motion of the receiver during simulation, a period of the simulation, a transmitter constellation to emulate, and a path of at least one IQ stream transmitter. Also generating signals emulating the transmitter constellation and conditioning the stream to be merged with the signals, using distance and relative motion between receiver and transmitter to determine delay and Doppler shift between transmitter and receiver in motion, scheduling sampling of the signal, including interpolation among samples of the stream, based on delay and Doppler shift, and synthesizing a conditioned stream from the interpolation between the samples, taking into account signal level of the stream, in addition to delay and shift, and merging the conditioned signal with the signals emulating the transmitter constellation and supplying the merged signals to the receiver during the test.