Timing Acquisition Sequence Optimization for Low-Sidelobe Correlation
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Solution Overview
Problem
Current timing acquisition methods using pseudo-noise (PN) sequences require longer sequences, increased signal power, and longer processing times, especially in spread spectrum applications, limiting system range and efficiency, and fail to separate PN sequence detection from timing acquisition.
Innovation Solution
A method involving an initial sequence of complex numbers with optimized autocorrelation properties, using a Frank sequence and a Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm to generate a phase-adjusted autocorrelation function, allowing for reduced timing uncertainty and separation of PN sequence detection and timing acquisition within a defined window.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire PN sequence is used for timing acquisition, then timing acquisition reliability is improved, but system overhead and processing time increase
Solution Approach 1:
The patent divides the PN sequence processing into two distinct stages: PN sequence detection stage and timing acquisition stage. This segmentation allows each stage to use optimized processing methods - the detection stage uses shorter sequences while the timing acquisition stage uses the full sequence, thereby reducing overall processing time while maintaining reliability
Solution Approach 2:
The patent performs PN sequence detection as a preliminary action before timing acquisition. By detecting and removing the PN sequence first, the subsequent timing acquisition process only needs to process the remaining data, significantly reducing the processing time and computational load while maintaining accurate timing synchronization
2Measurement precision
If longer PN sequences are used for timing acquisition, then correlation gain is improved, but system overhead and processing complexity increase
Solution Approach 1:
The patent segments the correlation process into two parts: initial correlation using a shortened PN sequence to achieve coarse synchronization, followed by fine correlation using the full PN sequence. This segmentation reduces the computational complexity and overhead while maintaining the necessary correlation gain for accurate timing acquisition
Solution Approach 2:
The patent applies partial action by using a shortened PN sequence for the initial detection phase, which provides sufficient correlation gain for that stage without requiring the full sequence length. This partial use of the PN sequence reduces system overhead while maintaining adequate performance for the detection phase
3Ease of operation
If PN sequence detection and timing acquisition are combined, then process simplicity is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent clearly segments the combined process into two distinct phases: PN sequence detection phase and timing acquisition phase. Each phase has its own optimized processing algorithm and resource requirements, making the overall process more efficient while maintaining operational simplicity through clear phase separation
Solution Approach 2:
The patent performs PN sequence detection as a preliminary action that simplifies the subsequent timing acquisition process. By detecting and removing the PN sequence first, the timing acquisition phase deals with simpler data, reducing processing time and resource consumption while maintaining process simplicity through a clear two-stage approach
Data Source
AI summary
A method of processing a timing synchronization signal includes selecting an initial sequence of complex numbers and modifying the initial sequence based upon a metric applied to the autocorrelation function to enhance its autocorrelation properties within a predetermined window about the main autocorrelation peak determined by the timing uncertainty of the system. This two-step optimization process produces a new complex sequence used for timing acquisition. It is applied by transmitting the sequence through a medium and correlating the received signal against a known error-free sequence. Only correlation within the window of the bounded timing uncertainty is performed, thus saving valuable computational cycles. Also, because the sidelobe levels of the autocorrelated function are significantly lower within the timing uncertainty window than the sidelobe levels of a non-optimized autocorrelation function of a signal, the likelihood of finding a peak for the wrong timing signal is greatly reduced.