Mode S Radar Anti-Reflection Algorithm for False Track Elimination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing Mode S radar systems face challenges in discriminating between real and false targets due to reflections from natural or artificial obstacles, leading to unreliable track initiation and potential harm from non-univocal air codes and incorrect pilot inputs, which traditional algorithms fail to effectively address.
Innovation Solution
A method involving the creation of a raw video map subdivided into cells with associated probability of false replies, identifying reply clusters, extracting plots, and using a radar tracker to calculate and update tracks, focusing on avoiding false track initiation by determining initial points of false tracks and updating the map based on probability calculations, with integration of ADS-B reports for accurate reflector mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional radar algorithms are used for track initiation, then the system can detect targets, but false tracks are generated due to reflections from natural or artificial obstacles
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing probability maps of false replies in different cells before track initiation. When a new track is detected, the system checks the pre-computed probability values for the corresponding cell to determine whether to initiate the track, thereby preventing false tracks from being generated in the first place rather than cleaning them up after creation.
Solution Approach 2:
The patent introduces an intermediary mechanism - the probability map of false replies - that mediates between the raw radar data and the track initiation decision. This intermediary structure allows the system to evaluate the likelihood of false replies before committing to track initiation, effectively filtering out false tracks while maintaining legitimate target detection.
2Productivity
If the radar system operates in high-traffic environments with increased air traffic, then surveillance coverage is improved, but the complexity of discriminating between real and false targets increases
Solution Approach 1:
The patent segments the radar surveillance area into multiple cells, each with its own probability map of false replies. This segmentation allows the system to handle high-traffic environments by processing information locally in each cell rather than globally, reducing the computational complexity of target discrimination while maintaining comprehensive surveillance coverage across the entire area.
Solution Approach 2:
The system dynamically updates the probability maps of false replies based on detected false tracks and changing operational conditions. This dynamic adaptation allows the radar system to maintain effective target discrimination in high-traffic environments by continuously learning from recent false track patterns and adjusting its decision criteria accordingly, without requiring complex static algorithms.
3Speed
If the radar system uses fixed track initiation thresholds, then the processing speed is maintained, but the system cannot adapt to changing detection situations and traffic density
Solution Approach 1:
The patent implements dynamic track initiation thresholds that automatically adjust based on the probability maps of false replies and current operational conditions. Instead of using fixed thresholds, the system retrieves pre-computed probability values for different cells and uses these to dynamically determine appropriate initiation thresholds, enabling adaptation to changing traffic density and detection situations while maintaining efficient processing speeds through pre-computation.
Solution Approach 2:
The system performs preliminary computation of probability maps for various cells under different conditions and stores them in advance. When actual track initiation is needed, the system retrieves the pre-computed probability values rather than performing complex real-time calculations, thereby maintaining fast processing speeds while achieving adaptability to changing situations through the pre-prepared probabilistic data.
Data Source
AI summary
A Mode S anti-reflection method for eliminating false tracks due to reflected replies in ground radar systems, wherein the information contribution of the replies (at the level of raw video) is analyzed with the aim of calculating the position of the reflectors. The possible presence of ADS-B reports can be used, otherwise it will be effectuated a geometrical analysis of the distribution of the replies and will be compared with the plot(s) extracted by the radar sensor. The possibility of correlating along time the moving of the plots, their place of origin and average duration of the tracks generated by them will allow to understand whether the plot is relevant to a reflection or not. In the case of “reflection”, a reflectors map is updated automatically in order to avoid the enabling of the initialization of the track in that area.