Monopulse Signal Extraction Using Polynomial Models
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Solution Overview
Problem
Existing monopulse systems face challenges in accurately detecting and resolving closely spaced targets due to merged angle of arrival data, which is affected by phase differences and radar/sonar cross section ratios, leading to biased trajectory measurements and increased system complexity and cost.
Innovation Solution
A method and system that extract relative signals from monopulse scan data using cubic and linear polynomial models to fit merged azimuth angle values, determining relative signals through polynomial coefficients and exponential parameters, thereby isolating the azimuth angles and radar cross section ratios of closely spaced targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard monopulse data processing is used, then the system is simple and cost-effective, but the measurement precision of target angles deteriorates due to merged angle of arrival data
Solution Approach 1:
The patent segments the merged monopulse data into multiple polynomial models (linear, quadratic, cubic) corresponding to different target scenarios. By selecting appropriate polynomial orders based on target characteristics, the system achieves high measurement precision without requiring complex additional hardware, thus resolving the contradiction between precision and complexity
Solution Approach 2:
The patent changes the mathematical parameter representation by fitting monopulse data to polynomial functions of different orders. This transformation allows extraction of precise target parameters (angles, velocities, accelerations) from merged data through coefficient analysis, improving measurement precision while maintaining system simplicity
2Measurement precision
If additional hardware is added to resolve closely spaced targets, then the measurement precision improves, but the device complexity and cost increase
Solution Approach 1:
The patent replaces physical hardware modifications with mathematical signal processing techniques. By applying polynomial fitting and coefficient analysis to the existing monopulse data, the system achieves enhanced target resolution capability without adding any physical components, thus resolving the contradiction between resolution and hardware complexity
3Measurement precision
If polynomial fitting is applied to extract relative signals, then the measurement precision of target parameters improves, but the computational complexity increases
Solution Approach 1:
The patent applies partial polynomial fitting by selecting specific polynomial orders (linear, quadratic, or cubic) based on the actual target scenario. This selective approach extracts only the necessary target parameters with appropriate precision while avoiding unnecessary computational overhead from higher-order fitting, thus resolving the contradiction between accuracy and computational complexity
Data Source
AI summary
Systems and methods are provided for extracting relative signal parameters representing two closely spaced targets from monopulse scan data. A maximum quadrature angle value from the scan data is compared with a threshold quadrature value representing a noise level. A linear polynomial model is utilized if the maximum quadrature angle exceeds the threshold value. The linear polynomial model fits a function of the azimuth angle values and quadrature angle values to a linear function of an exponential parameter derived from the boresight angles to produce polynomial coefficients and determines the relative signal parameters from the polynomial coefficients. A cubic polynomial model is utilized if the maximum quadrature angle fails to exceed the threshold value. The cubic polynomial model fits azimuth angle values to a cubic function of corresponding boresight angles to produce a set of polynomial coefficients and determines the relative signal parameters from the set of polynomial coefficients.