Radar Pulse Detection and Estimation with Adaptive Noise Floors
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Solution Overview
Problem
Detecting and estimating radar signals in time-varying noisy environments is challenging due to diverse radar system parameters, low signal-to-noise ratios, and time-varying noise floors, which complicates the detection and estimation of radar pulse parameters, especially in passive surveillance systems.
Innovation Solution
A method involving pre-processing, noise floor estimation, detection statistics calculation, rising and falling edge detection, time of arrival and departure estimation, pulse width, amplitude, center frequency, and bandwidth estimation, followed by wrapping these parameters into pulse descriptor words, designed for implementation in hardware platforms like FPGA.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If threshold-based edge detection methods are used, then detection performance is good for short high-energy pulses, but they fail to work with long low-energy pulses
Solution Approach 1:
The patent transforms the radar detection problem from time domain to frequency domain by applying Short-Time Fourier Transform (STFT). This parameter transformation allows the detection method to effectively handle both short high-energy pulses and long low-energy pulses by analyzing their spectral characteristics rather than temporal thresholds, thereby resolving the contradiction between detection performance for different pulse types.
2Reliability
If CUSUM-based schemes are used, then detection performance is good in low SNR environments, but calculation requires exact information about noise statistics and transient parameters
Solution Approach 1:
The patent implements adaptive noise floor estimation that automatically tracks and updates the noise characteristics in real-time without requiring pre-known noise statistics. The system serves itself by continuously monitoring the signal environment and adapting its detection thresholds, thereby achieving good performance in low SNR conditions while eliminating the need for exact prior knowledge of noise parameters.
3Measurement precision
If classical CUSUM techniques are used for TOA and TOD estimation, then detection is achieved, but estimation is not consistent between low and high SNR levels
Solution Approach 1:
The patent transitions the estimation problem from time domain to frequency domain using STFT. By analyzing the spectral peaks and their corresponding time-frequency representations, the system achieves consistent TOA and TOD estimation across different SNR levels. This dimensional transformation provides more robust estimation characteristics that are less sensitive to noise variations compared to time-domain CUSUM methods.
Data Source
AI summary
Radar signal detection and parameter estimation is central in passive surveillance systems, providing inputs for many information processing modules in order to detect, localize, indentify and intercept hostile targets. The proposed method for detecting radar signals and estimating their intra-pulse parameters in time-varying noisy environments consists of several stages: magnitude-squared envelopes calculation, adaptive noise floor estimation, detection statistics calculation, rising edge detection, time of arrival estimation, falling edge detection, time of departure estimation, pulse width estimation, amplitude estimation and center frequency and bandwidth estimation. Estimated intra-pulse parameters are wrapped into pulse descriptor words (PDWs) for information processing tasks, where each PDW consists of time of arrival, time of departure, pulse width, pulse amplitude, center frequency, signal bandwidth, noise floor level and additional useful information. The method is sequential, implemented in hardware platforms for real-time surveillance applications. The proposed method yielded much better performance than classical threshold-based edge (TED) detection methods.