Radar Signal Data Processor Using Queuing Theory
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Solution Overview
Problem
Phased array radar systems require expensive and specialized signal processing hardware and software to meet real-time requirements, leading to high capital and integration costs due to the need for custom-built equipment and complex parallel processing toolkits.
Innovation Solution
A scalable radar signal processing system using commercial off-the-shelf hardware and software, comprising detector signal data processors with data processing units that execute independently and in parallel, utilizing queuing theory to distribute processing across multiple processor cores or computers, and an aggregator to generate plot information from detection data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If custom-built and specialized high-end equipment is used for radar signal processing, then processing capability and real-time performance are improved, but capital outlay and integration costs increase
Solution Approach 1:
The patent replaces expensive, specialized radar signal processing equipment with commercial off-the-shelf (COTS) computers and components. Instead of investing in custom-built specialized hardware, the system uses readily available, inexpensive COTS devices that can be easily replaced or upgraded, thereby significantly reducing capital outlay while maintaining processing capability through software-based signal processing.
Solution Approach 2:
The patent creates a virtual copy of the radar signal processing system using software running on COTS hardware. Rather than physically building specialized processing equipment, the system replicates the functionality of radar signal processors through software implementations, allowing multiple virtual processors to run on standard hardware platforms and reducing the need for expensive specialized equipment.
2Productivity
If specialized parallel processing tool kits are used to distribute processing elements, then real-time processing requirements are met, but integration and validation costs increase
Solution Approach 1:
The patent employs a universal software-based processing framework that can run on standard COTS hardware platforms without requiring specialized parallel processing tool kits. The system uses通用的 programming interfaces and software architectures that work across different hardware platforms, eliminating the need for complex, platform-specific integration tools and reducing validation costs while maintaining real-time processing capability.
Solution Approach 2:
The patent replaces the mechanical/system-level complexity of specialized parallel processing hardware with software-based processing on COTS systems. Instead of physically distributing processing elements across specialized hardware using complex interconnection systems, the patent uses software threads and processes running on standard multi-core processors, thereby substituting mechanical complexity with software flexibility and reducing integration burden.
3Productivity
If data is distributed across multiple processors using specialized tool kits, then processing throughput is improved, but programming complexity and expertise requirements increase
Solution Approach 1:
The patent implements a self-service parallel processing architecture where the system automatically manages data distribution and processing allocation across multiple COTS processors without requiring complex manual configuration. The software framework handles load balancing, data partitioning, and processor coordination automatically, allowing programmers to write simpler code that leverages standard programming languages and interfaces rather than requiring expertise in specialized parallel processing tool kits.
Data Source
AI summary
A system provides high-throughput radar signal processing without resorting to costly parallel processing tool kits. The system breaks the signal processing chain into a series of independent data processing units (DPUs) that execute independently and in parallel. Queuing theory is used to efficiently distribute processing across multiple processor cores and/or computers. The system scales to an arbitrary number of cores/computers. The independent nature of the DPUs readily allows addition/replacement of new filters into the system.