Aircraft Detection Probability Map for Mission Planning
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Solution Overview
Problem
Existing methods for detecting terrestrial targets from an aircraft are hindered by factors such as aircraft altitude, sensor sensitivity, and geographic obstructions like mountains, making it difficult to predict the probability of target detection before a mission, especially in mountainous regions where visibility and sensor range are compromised.
Innovation Solution
A computer-implemented digital image processing method that analyzes aircraft routes and sensor capabilities to determine line-of-sight visibility and probability of target detection on segments of lines of communication, providing a visual representation of these probabilities through color-coding to enhance mission planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the aircraft altitude is increased to provide more direct lines of sight over more terrestrial surface area, then the coverage area is improved, but the sensor sensitivity is degraded due to greater distance from the area of interest
Solution Approach 1:
The system performs preliminary calculations of detection probabilities for multiple altitude scenarios before the mission. By computing visibility percentages and detection probabilities in advance for different altitude levels, the system enables mission planners to select the optimal altitude that balances coverage area and sensor sensitivity, rather than discovering this tradeoff during the actual mission.
Solution Approach 2:
The system transforms the single-dimension problem of altitude selection into a multi-dimensional analysis by incorporating detection probability, visibility percentage, coverage area, and sensor sensitivity as separate evaluative dimensions. This allows planners to visualize and compare multiple factors simultaneously on a map interface, making the complex tradeoff manageable.
2Measurement precision
If the aircraft flies at lower altitude to maintain sensor sensitivity, then the detection precision is improved, but the coverage area is reduced
Solution Approach 1:
The system enables dynamic adjustment of mission parameters including altitude, aircraft speed, and sensor field-of-view angles. By allowing planners to modify these parameters and immediately see the impact on detection probability and coverage area through recalculation, the system finds optimal parameter combinations that balance precision and coverage for specific mission requirements.
3Productivity
If the mission is conducted without advance probability analysis, then the operational flexibility is maintained, but the mission cost and time consumption increase
Solution Approach 1:
The system performs all necessary probability calculations, visibility analyses, and detection assessments during the planning phase before the mission executes. By pre-computing detection probabilities for different routes, altitudes, and sensor configurations, the system eliminates the need for time-consuming adjustments during the actual mission, thereby improving operational efficiency without significant planning time investment.
4Adaptability or versatility
If geographic features like mountains are present in the area of interest, then the terrain complexity is increased, but the line of sight visibility is degraded
Solution Approach 1:
The system analyzes and evaluates detection probability for different geographic regions independently, taking into account local terrain features such as mountains, valleys, and elevation changes. By segmenting the area of interest and assessing each segment's specific visibility conditions, the system provides localized detection probability information that reflects actual terrain conditions rather than applying uniform assumptions across the entire mission area.
Data Source
AI summary
An exemplary computer implemented digital image processing method conveys probabilities of detecting terrestrial targets from an observation aircraft. Input data defining an observation aircraft route relative to the geographical map with lines of communications (LOC) disposed thereon are received and stored as well as input data associated an aircraft sensor's targeting capabilities and attributes related to the capability of targets to be detected. Percentages of time for line-of-sight visibility from the aircraft of segments of LOC segments are determined. Probability percentages that the sensor would detect a terrestrial target on the segments are determined. The segments are color-coded with visibility and sensor detection information. A visual representation of the map with the color-coded segments is provided to enhance the ability to select appropriate observation mission factors to achieve a successful observation mission.