Flying Object Crash Detection Using High-Rate Signal Filtering
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Solution Overview
Problem
Existing crash detection systems for flying objects lack reliability and accuracy in abnormality detection due to inadequate data collection speed and noise differentiation, leading to potential malfunctions and safety concerns.
Innovation Solution
A crash detection device equipped with a sensor and controller that uses a high sampling frequency (1 kHz to 10 kHz) to distinguish between signal and noise, ensuring accurate data interpretation and timely parachute or paraglider deployment, and integrating a sensor abnormality detection mechanism to prevent false activations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electromagnetic shielding is added to improve reliability, then reliability is improved, but weight and cost increase
Solution Approach 1:
The patent replaces electromagnetic shielding (physical barrier) with signal processing methods (digital/filtering techniques) to achieve the same reliability goal. The controller distinguishes crash signals from noise through algorithmic processing rather than physical shielding, eliminating the need for additional electromagnetic shielding materials and reducing overall system weight.
Solution Approach 2:
The patent changes the sampling frequency parameter to 1 kHz or higher to capture crash signals accurately while enabling differentiation from noise through spectral analysis. This parameter change allows the system to achieve reliable crash detection without electromagnetic shielding by utilizing frequency-domain signal processing to separate genuine crash signals from electromagnetic noise.
2Reliability
If electromagnetic shielding is added to improve reliability, then reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent substitutes complex electromagnetic shielding structures with software-based signal processing algorithms implemented in the controller. This replacement reduces mechanical/electrical complexity while maintaining or improving reliability through intelligent differentiation of crash signals from electromagnetic noise using digital filtering and pattern recognition.
Solution Approach 2:
The system performs self-diagnosis and self-differentiation of signals through onboard processing. The controller automatically distinguishes between crash signals and electromagnetic noise using integrated algorithms, eliminating the need for external shielding components and reducing overall system complexity while maintaining reliability.
3Measurement precision
If sampling frequency is increased to 1 kHz to distinguish signal from noise, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent implements periodic sampling at 1 kHz specifically during periods when crash detection is critical, rather than continuous high-frequency sampling. This periodic action allows the system to capture sufficient data for accurate crash detection while reducing overall power consumption by lowering sampling frequency during normal operation or using duty-cycled processing.
Solution Approach 2:
The patent applies excessive sampling frequency (1 kHz) only when necessary for accurate signal differentiation, rather than continuously. The system uses this high sampling rate selectively during periods when crash signals are most likely or when noise differentiation is critical, balancing measurement precision requirements with energy consumption constraints.
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
Enhances the reliability and safety of flying object systems by accurately differentiating between signal and noise, reducing the risk of malfunctions and eliminating the need for electromagnetic shielding, thereby reducing weight and cost while ensuring proper deployment.
Implementation Method 1
a sensor that actually measures a flying state of the flying object and a controller that reads data that has been actually acquired by the sensor
Data Source
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AI summary
Provided is a crash detection device, a method of detecting a crash of a flying object, a parachute or paraglider deployment device, and an airbag device that can improve the reliability in terms of safety. A device detecting a crash of a flying object includes a detection part capable of detecting a flying state of the flying object, a calculation section capable of determining whether the flying state of the flying object is abnormal based on data on the flying state of the flying object acquired by the detection part, and an abnormal signal output section capable of outputting an abnormal signal to the outside when the calculation section determines that the flying state of the flying object is abnormal. The calculation section acquires data from the detection part at a sampling frequency of 1 kHz or more, determines whether the data is data indicating that the flying state of the flying object is abnormal or noise that is unnecessary data when the data is equal to or greater than a predetermined threshold value, determines that the flying state of the flying object is abnormal when the data is determined to be the data indicating that the flying state of the flying object is abnormal.