Coastal Radar Target Range Profile Extraction
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Solution Overview
Problem
Modern coastal surveillance radars face inaccuracies in determining target range profiles due to noise interference, leading to length mismatches and reduced effectiveness in target recognition and classification.
Innovation Solution
A system comprising a coarse determination cluster and a smoothing and truncated cluster to accurately extract target range profiles, involving memory blocks, averaging by azimuth, thresholding, and a proposed algorithm to refine the profiles, reducing noise impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical window-based methods are used to determine target range profiles, then the processing is simple, but the measurement precision of target length is poor due to noise interference
Solution Approach 1:
The patent divides the target range profile determination into two distinct clusters: coarse determination cluster and smoothing and truncated cluster. The coarse determination cluster performs initial target detection and range profile extraction, while the smoothing and truncated cluster refines the results by removing noise and filling gaps. This segmentation allows each cluster to specialize in specific tasks, improving overall measurement precision without overwhelming complexity.
Solution Approach 2:
The coarse determination cluster performs preliminary processing by extracting initial range profiles and identifying target regions before the smoothing and truncated cluster refines them. This preliminary action prepares the data in advance, allowing the second cluster to focus on precision refinement rather than raw data processing, thereby improving final measurement accuracy.
2Measurement precision
If averaging over multiple scans is performed, then the measurement precision improves, but the processing time increases
Solution Approach 1:
The patent applies partial averaging by selectively processing only certain scans rather than averaging all available scans. The smoothing and truncated cluster identifies and processes specific scan data that contributes most to accuracy, applying averaging operations selectively to reduce noise while maintaining acceptable processing time. This partial action approach achieves improved precision without the full time cost of comprehensive averaging.
Solution Approach 2:
The patent extracts and processes only the essential scan data needed for accurate range profile determination. The smoothing and truncated cluster identifies relevant scans containing target information and extracts them for averaging, while excluding redundant or less useful scan data. This extraction approach maintains measurement precision while reducing the total processing time by focusing computational resources on critical data.
Data Source
AI summary
Determining a target's range profiles is an important issue for coastal surveillance radars because it can give us the knowledge about the target, for example, target's type, target's structure and its length along radial direction. Some modern radars nowaday are equipped with the feature of target's range profile extraction, but the results are not accurate due to limitations in processing algorithms. The invention “system and method of determining target's range profiles for coastal surveillance radars” solves the above problem in the direction of proposing a system of technical solutions and associated algorithm improvements.