Distributed Multi-Node Low-Frequency Radar for Degraded Visual Environments
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Solution Overview
Problem
Conventional radar systems face challenges in providing high-resolution maps in inclement weather due to backscatter and absorption issues, especially at low altitudes where aircraft must navigate through obstacles and adverse weather conditions.
Innovation Solution
A distributed, multi-node low-frequency radar system that uses a network of receivers to process radar signals, combining time delay, doppler shift, and phase comparison calculations to refine range and angular position data, filtering out false returns and backscatter, thereby generating accurate radar maps even in degraded visual environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional radar systems operate in inclement weather, then they can detect obstacles, but backscatter and absorption reduce measurement precision
Solution Approach 1:
The radar system is divided into multiple distributed receivers that independently process radar returns. Each receiver captures signals from different spatial perspectives, allowing the system to segment the overall detection task and combine results to improve precision while maintaining reliability in inclement weather
Solution Approach 2:
Multiple radar receivers are combined into a distributed network that processes signals collectively. By merging data from multiple receivers and applying coalescing algorithms, the system achieves higher measurement precision and resolves backscatter issues that affect conventional single-receiver systems
2Reliability
If low-frequency radar signals are used, then penetration through inclement weather improves, but angular resolution deteriorates
Solution Approach 1:
The system transitions from single-receiver angular measurement to multi-receiver spatial dimension analysis. By distributing receivers across multiple locations and analyzing phase differences across this spatial dimension, the system recovers angular resolution that would otherwise be lost due to low-frequency operation
Solution Approach 2:
The system changes the operational parameters by using low-frequency radar signals that can penetrate inclement weather, then compensates for the resulting angular resolution loss through distributed receiver geometry and phase comparison algorithms, effectively adapting the system to maintain precision despite frequency changes
3Measurement precision
If a distributed network of receivers is used, then radar map accuracy improves, but device complexity increases
Solution Approach 1:
The complex processing task is segmented and distributed across multiple receivers, with each receiver performing independent signal processing. This segmentation allows the system to achieve high accuracy through collective processing while managing complexity by dividing the overall function into manageable independent units
Solution Approach 2:
Each receiver in the distributed network is designed as a universal, multi-functional unit capable of receiving, processing, and transmitting radar signals independently. This universality simplifies the overall system architecture by using identical standardized components rather than specialized unique elements, reducing complexity while maintaining precision
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves high-resolution radar mapping in inclement weather conditions by filtering out inaccuracies and backscatter, providing improved navigation for low-altitude aircraft operations, similar to high-frequency radar systems but without their weather-related limitations.
Implementation Method 1
Radar systems transmit radio frequencies and receive the radio signal and use the time delay and doppler shift of the radio signal to determine a location and/or velocity of objects in the environment of the aircraft operating the radar system
Implementation Method 2
use the time delay and doppler shift of the radio signal to determine a location and/or velocity of objects
Implementation Method 3
determine a first range and a first angular position of a background point based on a return time detected at each radar receiver
Implementation Method 4
refine the first and second angular position of the background point by at least coalescing the return time, the constant doppler calculations and phase shift calculations
Data Source
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AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for distributed, multi-node, low frequency radar systems for degraded visual environments. An example system includes a transmitter to transmit a radar signal. The example system includes a distributed network of radar receivers to receive the radar signal at each receiver. The example system includes a processor to determine a first range and a first angular position of a background point based on return time, wherein the first range and the first angular position are included in first data; determine a second range and a second angular position of the background point based on doppler shift, wherein the second range and the second angular position are included in second data; determine a refined range and a refined angular position, wherein the refined range and refined angular position are included in third data, and generate a radar map based on third data.