Frequency Signature Mapping for Low-Cost Moving Target Mitigation
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Solution Overview
Problem
Existing solutions for tracking and mitigating the movement of small objects like insects are limited by the need for expensive equipment and lack accuracy, especially when using digitizers with lower maximum sampling rates.
Innovation Solution
A method and system that utilize frequency signature mapping to classify movement behaviors of targets by adjusting pulse application, sampling rates, and scanning algorithms, allowing for accurate tracking and mitigation using less expensive digitizers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive equipment with high sampling rates is used, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent changes the parameter of sampling rate by using variable sampling rates adapted to the movement speed of targets. When targets move slowly, lower sampling rates are used; when targets move quickly, sampling rates are increased. This allows accurate tracking of small moving objects without requiring continuously high sampling rates, thereby reducing equipment costs while maintaining measurement precision.
Solution Approach 2:
The system dynamically adjusts the sampling rate based on the detected movement characteristics of targets. The sampling rate is not fixed but varies in response to target behavior, allowing the system to optimize between accuracy and resource usage in real-time. This dynamic adaptation enables accurate tracking with lower average sampling rates compared to static high-rate systems.
2Device complexity
If lower sampling rate digitizers are used, then device cost is reduced, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary detection to identify potential targets and their movement characteristics before applying the full measurement protocol. By pre-characterizing target behavior and predicting movement patterns, the system can allocate sampling resources more efficiently, allowing lower sampling rate digitizers to achieve adequate precision by focusing measurements on relevant time periods and spatial regions.
Solution Approach 2:
The patent implements variable sampling rates that change based on target movement characteristics. Instead of using a fixed high sampling rate, the system adapts the sampling rate to match the actual movement dynamics, allowing lower sampling rate digitizers to achieve the same effective measurement precision by concentrating sampling effort when targets are moving and reducing sampling when targets are stationary.
3Measurement precision
If high sampling rates are used to track fast-moving targets, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system dynamically adjusts the sampling rate based on the detected movement speed of targets. When targets are moving quickly, the sampling rate is increased to maintain tracking accuracy. When targets are moving slowly or stationary, the sampling rate is reduced. This dynamic adaptation allows the system to maintain measurement precision for fast-moving targets only when necessary, significantly reducing overall energy consumption compared to continuous high-rate sampling.
Solution Approach 2:
The patent implements periodic scanning with variable intervals between scans. Instead of continuous high-rate sampling, the system performs scans at periodic intervals that are adjusted based on target movement characteristics. This periodic action with adaptive timing allows the system to capture necessary data for tracking accuracy while minimizing energy consumption by avoiding continuous operation at high sampling rates.
Data Source
AI summary
Systems and methods for mitigating moving targets using frequency signature mapping. A method includes mapping a plurality of electronic signatures of a target to a three-dimensional (3D) model into an electronic signature mapping, wherein the 3D model includes 3D features representing a plurality of historical frequency signatures; classifying a movement behavior of the target into at least one classification based on the electronic signature mapping and a plurality of learned behaviors for respective classifications of historical movement behavior; and performing at least one mitigation action to interfere with the classified behavior, wherein the at least one mitigation action is determined based on the at least one classification.