Sliding Window DFT Radar Detector Signal Processing
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Solution Overview
Problem
Existing radar detectors face challenges in efficiently detecting police radar signals in real-time due to limitations in signal processing speed and accuracy, particularly when dealing with pulsed signals and multiple interference sources.
Innovation Solution
The use of sliding window discrete Fourier transform (SWDFT) techniques allows for faster and more efficient frequency spectrum calculation, enabling real-time analysis by utilizing prior results to incrementally analyze signal segments, thereby identifying pulsed signals and issuing alerts when magnitudes exceed predetermined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional fast Fourier transform is used for signal analysis, then frequency spectrum can be calculated, but the calculation speed is insufficient for real-time signal analysis
Solution Approach 1:
The patent pre-calculates and stores the complex exponential terms e^(-j2πkn/N) in a lookup table before signal analysis. During real-time SWDFT operation, these pre-computed values are directly retrieved and reused, eliminating the need for repeated complex exponential calculations and significantly accelerating the transformation process
Solution Approach 2:
The patent divides the continuous signal into overlapping segments using a sliding window approach, where each segment is processed independently through the SWDFT algorithm. This segmentation enables parallel processing of multiple signal portions simultaneously, increasing overall calculation throughput and real-time processing capability
2Productivity
If SWDFT is used to improve calculation speed, then real-time analysis is enabled, but the device complexity increases
Solution Approach 1:
The patent transforms the standard DFT algorithm into SWDFT by modifying the computational parameters and structure. Specifically, it changes the input-output relationships and utilizes a recursive formulation that reuses previous calculation results, optimizing the computational parameters to achieve faster processing while managing complexity
Solution Approach 2:
The patent creates and stores copies of the complex exponential lookup table in memory, allowing rapid retrieval of pre-computed values during SWDFT operations. This copying approach eliminates repeated calculations of the same mathematical constants, significantly improving processing speed while the one-time memory cost is offset by the repeated operational benefits
3Measurement precision
If multiple target frequencies are analyzed simultaneously, then detection accuracy improves, but false alarms from interference sources increase
Solution Approach 1:
The patent applies different processing characteristics to different frequency regions by analyzing each target frequency independently through separate SWDFT channels. Each frequency channel can have its own threshold and detection criteria optimized for local conditions, allowing precise detection at specific frequencies while filtering out interference from other frequency regions
Solution Approach 2:
The patent replaces traditional analog filtering mechanisms with digital SWDFT-based frequency analysis. This substitution enables more precise frequency discrimination through mathematical transformation, accurately distinguishing between genuine radar signals and interference sources based on their spectral characteristics rather than relying on broad analog filter bands
Data Source
AI summary
In one embodiment, a police activity detector is provided. The detector includes a receiver section and a warning section. The receiver section is configured to receive signals generated in the context of law enforcement activity. The warning section is configured to respond to a pulsed signal received by the receiver section and provide an alert if a received signal correlates to a law enforcement signal. The warning section also includes a sliding window discrete Fourier transform (SWDFT) module configured to receive a plurality of time series of data corresponding to sampling a received pulsed signal at a set of sample rates corresponding to a plurality of target frequencies, perform a SWDFT determination on each of the plurality of time series of data to determine a magnitude of the received signal at each of the targeted frequencies, which reveals the presence of a received pulsed signal when the magnitude is elevated, and issue an alert if the magnitude of the received signal corresponding to at least one of the targeted frequencies is greater than or equal to a predetermined threshold.