Frequency Domain Correlation for DSSS Signal Tracking
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Solution Overview
Problem
Current Fourier-based tracking algorithms for Direct Sequence Spread Spectrum (DSSS) signals require excessive memory bandwidth and storage, making them impractical for Field Programmable Gate Array (FPGA) implementations, especially when tracking multiple satellites in a GNSS constellation.
Innovation Solution
The proposed solution involves a method that transforms DSSS signals from the time domain to the frequency domain, multiplies them by a correlation operator, and integrates the results, using a moving average filter and modulation matrices to reduce storage requirements by storing replica code samples at a lower sampling rate, and sharing these matrices across multiple signal processing branches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Fourier-based tracking algorithm with high sample rate is used to obtain correlation function, then measurement precision of time-of-arrival is improved, but memory bandwidth and storage capacity requirements increase excessively
Solution Approach 1:
The patent divides the correlation computation into multiple segments or blocks that can be processed independently. Instead of computing the entire correlation function at once with full precision, the computation is segmented into manageable chunks that can be accumulated incrementally, reducing the peak memory requirements while maintaining overall measurement precision.
Solution Approach 2:
The patent transitions from time-domain correlation computation to frequency-domain computation using Fast Fourier Transform (FFT). By transforming the problem into the frequency domain, the correlation function can be computed more efficiently with reduced memory bandwidth requirements, as the FFT algorithm processes data in a manner that minimizes memory access patterns.
2Measurement precision
If high sample rate (50 MS/s) is used for correlation computation, then correlation accuracy is improved, but memory bandwidth requirement exceeds FPGA capabilities
Solution Approach 1:
The patent performs preliminary downsampling or decimation of the input signal before the correlation computation stage. By reducing the sample rate earlier in the processing chain while maintaining correlation accuracy through appropriate filtering and resampling techniques, the subsequent correlation computation can be performed with reduced memory bandwidth requirements that fit within FPGA capabilities.
Solution Approach 2:
The patent replaces direct time-domain correlation computation with frequency-domain computation using FFT algorithms. This substitution changes the computational mechanism from direct multiplication and summation in time domain to transform-based computation in frequency domain, which reduces the memory bandwidth requirements and makes the system implementable in FPGA devices with limited memory resources.
3Measurement precision
If replica code samples are stored at native sample rate, then correlation accuracy is maintained, but storage requirements become prohibitively large for multiple satellite tracking
Solution Approach 1:
The patent implements shared memory resources and correlation computation logic across multiple satellite tracking channels. Instead of each tracking channel having dedicated full-rate replica code storage, the system uses shared memory structures that can be multiplexed across channels, reducing the total storage requirements while maintaining correlation accuracy for all tracked satellites.
Solution Approach 2:
The patent divides the replica code storage into segmented or binned structures that can be reused across multiple tracking channels. By organizing the storage in a segmented manner that allows sharing between channels and using efficient memory access patterns, the system maintains correlation accuracy while significantly reducing the total storage capacity required for tracking multiple satellites.
Data Source
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AI summary
Apparatus and methods are disclosed for obtaining a correlation function between a Direct Sequence Spread Spectrum (DSSS) signal and a spreading code. A plurality of code replica samples are stored in memory at a relatively low sampling rate compared to the sampling rate at which the input DSSS signal is sampled. A correlation operator is obtained by reproducing the stored code replica samples, in order to obtain a correlation operator with the same number of samples as the block of DSSS signal samples currently being processed. For example, when the sampling rate of the DSSS signal is equal to an integer N multiple of the sampling rate used for the code replica samples, the stored replica samples are reproduced N times to obtain the correlation operator. The correlation operator is then multiplied by the block of DSSS signal samples and integrated to obtain a correlation function.