Real-Time Radar Pulse Tracking via Feedforward Data Flow
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional radar systems are unable to process complex pulse patterns in real time due to their reliance on Von Neumann processors, which require multiple memory writes and are limited in scalability, making them non-real-time and inefficient for tracking radar sources.
Innovation Solution
A feedforward/data flow process is employed to identify pulse patterns and track radar sources in real time, utilizing pulse filters to analyze pulse repetition intervals, time difference of arrival, and incorporating jitter and variance, allowing for real-time tracking without time-consuming memory writes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Von Neumann processors are used to collect and store received pulse data for processing, then pulse patterns can be detected, but the processing time increases to many milliseconds and real-time operation is not achieved
Solution Approach 1:
The system segments the pulse train processing into discrete time slots, with each slot handling a specific pulse or group of pulses. This segmentation allows parallel processing of multiple pulses simultaneously, eliminating the sequential processing bottleneck of conventional Von Neumann architectures and enabling real-time operation while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-configuring multiple pulse filters with different pulse repetition interval parameters before processing begins. When pulses arrive, the pre-configured filters can immediately process them without requiring sequential configuration, significantly reducing processing time while maintaining the ability to detect complex pulse patterns.
2Loss of information
If multiple memory writes are performed to store received pulse data for processing, then pulse patterns can be analyzed, but the system becomes limited in scalability and cannot operate in real time
Solution Approach 1:
The system introduces pulse filters as intermediary processing elements that directly analyze incoming pulse trains without requiring data to be written to memory first. These filters act as mediators between the pulse receiver and the tracking system, enabling real-time pattern recognition while maintaining complete pulse data analysis capability.
Solution Approach 2:
The system replaces the mechanical memory write/read process with a direct signal processing path using pulse filters. Instead of writing pulses to memory and then reading them back for analysis, the filters process the pulse signals directly in the data stream, eliminating the time-consuming memory access operations and enabling real-time processing with high throughput.
3Measurement precision
If complex pulse patterns are analyzed using conventional processing methods, then accurate emitter identification can be achieved, but the system is limited in handling arbitrary complexity patterns in real time
Solution Approach 1:
The system achieves universality by designing pulse filters that can handle multiple pulse repetition interval parameters simultaneously. Each filter is configured with specific PRI parameters, and the combination of filters can detect a wide variety of pulse patterns including simple repeating patterns, staggered patterns, and arbitrary complexity patterns, all in real-time without requiring different processing methods for different pattern types.
Data Source
AI summary
Method and apparatus for tracking pulse trains from one or more emitters. For a valid pulse, PRI parameters are measured. A pulse pattern count is incremented and checked to determine if a track is acquired for the emitter.