Radar information processing method used for restraining false-alarm objects of rotary blades of wind driven generators

Abstract

The invention belongs to the technical field of radar information processing, particularly relates to a radar information processing method used for restraining false-alarm objects of rotary blades of wind driven generators on the ground during areal detection of the radar. The radar information processing method includes the steps of 1, acquiring a pulse transmitting frequency of the radar, accumulating pulse counts, and calculating a Doppler frequency through a conjugated product vector; 2, determining a main clutter position on a Doppler spectrum according to the Doppler frequency of the main clutter; 3, acquiring angular spin rates and the largest strength of the rotary blades of the wind driven generators, and calculating width of spectrum filter corresponding to the tail ends of the rotary blades of the wind driven generators; 4, acquiring distance gate numbers and power of detecting points, average noise power of the frequency spectrum and semi-diameter of broadening of the main clutter, and according to the judged threshold coefficient, acquiring a two-value vector distribution of the signal local power, with the main clutter as the center and the spectrum broadening of the rotary blades as the semi-diameter, on a single distance gate; 5, judging and removing the false alarms.

Description

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AI Technical Summary

Benefits of technology

This technology helps detect objects without affecting their ability to communicate with other devices by utilizing its own unique characteristic that allows signals from nearby sources like radio waves or light beams to pass through while being blocked out at certain angles during rotation. By doing this, these systems are able to accurately locate small drones near them even when they may have multiple antennas located around themselves.

Problems solved by technology

Technological Problem: Current Airborne Radar Detection Methods have limitations due to their ability to accurately track objects without being affected by external factors like weather conditions. These methods require multiple scans over an entire space to achieve accurate object identification but they cannot work well during adverse atmospheric events caused by strong turbulence. Additionally, current methods rely heavily upon fixed threshold techniques, leading to increased errors compared to dynamic range imagery systems.

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