GNSS Signal Decimation for Receiver Complexity Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
GNSS receivers face challenges in acquiring and processing positioning signals, especially in urban environments and within buildings, due to signal attenuation and multipath propagation, and traditional high-sensitivity receivers are impractical for devices with limited computing capacity.
Innovation Solution
The method involves using a first GNSS signal with lower encoded data rate to determine the decimation level for a second signal with higher data bandwidth requirements, based on signal-to-noise ratio (SNR) and other characteristics, to reduce data bandwidth and processing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional high-sensitivity receivers are used to overcome signal attenuation and multipath propagation, then positioning accuracy is improved, but device complexity and computational capacity requirements increase
Solution Approach 1:
The patent segments the signal processing task by dividing GNSS signals into multiple frequency components (e.g., L1, L2, L5 frequencies). Each frequency component is processed separately through dedicated filtering stages, allowing the system to achieve high positioning accuracy by combining results from multiple simplified processing paths rather than using a single complex high-sensitivity receiver architecture
Solution Approach 2:
The patent introduces intermediary processing stages including bandpass filters and decimation filters that act as mediators between the raw GNSS signals and the final positioning calculation. These intermediary components selectively process specific frequency bands and reduce data rates before further processing, thereby improving accuracy while keeping individual processing stages computationally manageable
2Measurement precision
If signals with higher encoded data rates are used to improve positioning precision, then measurement precision is improved, but processing complexity increases
Solution Approach 1:
The patent applies periodic decimation to signals with higher encoded data rates, selectively reducing the sampling rate at regular intervals while maintaining the essential positioning information. This periodic reduction in data rate decreases processing complexity while preserving the precision needed for accurate positioning through strategic sampling retention
Solution Approach 2:
The patent changes the frequency domain parameters of received signals by applying frequency-specific filtering and decimation. By transforming signals from high data rate formats at multiple frequencies into standardized lower data rate formats, the system maintains positioning precision while reducing the computational burden of processing high-encoded-rate signals
3Reliability
If multiple GNSS frequencies are processed to improve signal acquisition in difficult environments, then reliability is improved, but data bandwidth requirements increase
Solution Approach 1:
The patent segments the multi-frequency GNSS signal processing into distinct frequency channels, each handled by dedicated filtering and decimation stages. This segmentation allows reliable processing of multiple frequencies simultaneously while managing data bandwidth through organized, modular processing paths that prevent data overload at any single stage
Solution Approach 2:
The patent applies frequency-dependent decimation factors to convert signals from multiple high data rate frequencies into a unified lower data rate format. By changing the data rate parameter differently for each frequency based on its characteristics, the system maintains reliable signal acquisition across all frequencies while reducing the total aggregated data bandwidth to manageable levels
Data Source
AI summary
Data bandwidth reduction in positioning system signals. Specifically, a first, relatively easily acquired signal may be analyzed to determine if and/or to what extent to decimate a second signal. The second signal may comprise a higher encoded data rate (e.g., a chip rate). In turn, decimation of the second signal based on characteristics of the first signal may allow for more efficient processing of the second signal.