Radar Range Resolver Lookup Table for Ambiguity Resolution
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Solution Overview
Problem
Medium and high pulse repetition frequency (PRF) radars face challenges in resolving range ambiguities, leading to incorrect range calculations for targets beyond the maximum unambiguous range, which limits their ability to detect and track multiple targets in dense environments.
Innovation Solution
A method utilizing a multi-dimensional lookup table to efficiently map prior coherent processing interval and range bin information to reduce computational complexity in the M-of-N range resolver, allowing for linear scaling of computation instead of quadratic, thereby increasing throughput and enabling accurate range resolution for multiple targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple PRFs are used to resolve range ambiguities using traditional M-of-N range resolver, then range resolution accuracy is improved, but computational complexity increases quadratically
Solution Approach 1:
The patent pre-calculates and stores range coincidence information in a lookup table before radar operation. This preliminary action converts the quadratic computational problem into a linear lookup operation, resolving range ambiguities with O(N) complexity instead of O(N²), thereby maintaining measurement precision while dramatically reducing computational complexity
Solution Approach 2:
The patent creates a simplified copy of the range resolution problem by storing pre-computed coincidence data in a lookup table. Instead of performing complex real-time calculations, the system copies relevant historical coincidence patterns into a searchable structure, enabling fast range ambiguity resolution through table lookup rather than exhaustive computation
2Measurement precision
If traditional M-of-N range resolver is used in dense target environments, then range ambiguities are resolved, but target detection throughput is limited
Solution Approach 1:
By pre-computing and storing range coincidence data in a lookup table before radar operation, the system transforms the computational burden from quadratic to linear. This preliminary preparation enables the radar to process multiple targets in dense environments at high throughput rates without sacrificing range ambiguity resolution accuracy
3Device complexity
If small scan areas are used to reduce target density, then computational load is reduced, but radar coverage and target detection capability are limited
Solution Approach 1:
The patent pre-calculates range coincidence information for all possible target ranges and stores it in a lookup table. This allows the radar to maintain full scan coverage area while keeping computational load linear rather than quadratic, eliminating the need to restrict scan areas to manage computational complexity
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
This approach significantly enhances the radar system's ability to detect and track multiple targets by reducing computational complexity, allowing it to operate effectively in dense target environments with improved range resolution and velocity determination.
Implementation Method 1
producing a brief radio frequency (RF) pulse
Implementation Method 2
the receiver to sample echoes
Implementation Method 3
uses the Doppler effect of the returned signal to determine the target's velocity
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
System and method for determining range to targets using an M-of-N range resolver includes transmitting multiple coherent processing interval (CPI) signals with different pulse repetition frequencies (PRFs) towards the targets, receiving and storing threshold hits from prior N-1 CPIs; converting the threshold hits from the current CPI and prior N-1 CPIs to range unfolded threshold hits; generating a lookup table of the plurality of range unfolded threshold hits from the prior N-1 CPIs; determining the number of the prior N-1 CPIs in which a range unfolded threshold hit from the current CPI has at least one range coincident range unfolded threshold hit from a prior CPI utilizing the lookup table; generating a range resolved threshold hit when the number is greater than or equal to M-1; accumulating range resolved threshold hits; and determining the range to the targets.