RF Object Detection Matrix Energy Summing for Low-Profile Targets
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Solution Overview
Problem
Existing object detection technologies, particularly those using radio frequency (RF) signals, struggle to accurately detect low-profile objects and are prone to false alarms due to background noise and signals from objects beneath the surface.
Innovation Solution
A movable device equipped with an RF signal transmitter and receiver, along with a processor that digitizes RF signals, forms a matrix from these signals, and calculates an energy value by summing samples. This energy value is then compared to a threshold to detect low-profile objects, while filtering techniques are used to remove background noise and signals from objects beneath the surface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RF signal transmission is used for object detection, then detection capability is provided, but false alarms occur due to background noise and signals from objects beneath the surface
Solution Approach 1:
The patent extracts and removes harmful components (background noise and signals from objects beneath the surface) from the received RF signals through filtering operations. The processor identifies and eliminates these interfering signals to isolate only the relevant object detection signals, thereby improving detection accuracy while reducing false alarms.
Solution Approach 2:
The patent converts the harmful background noise and subsurface object signals into useful information by analyzing their characteristics. The system uses the energy values and temporal patterns of these signals to distinguish between actual obstacles and false alarm sources, transforming what was previously harmful interference into a discriminative feature for improved detection reliability.
2Measurement precision
If conventional RF detection methods are used, then high-profile objects can be detected, but low-profile objects cannot be detected
Solution Approach 1:
The patent changes the detection parameters by analyzing the energy values of received RF signals over multiple time points. Instead of relying on single-point detection that misses low-profile objects, the system accumulates and compares energy values across different time intervals, using threshold-based detection to identify the subtle reflections from low-profile objects that conventional methods overlook.
3Reliability
If multiple RF signals are processed to improve detection accuracy, then detection reliability increases, but processing complexity increases
Solution Approach 1:
The patent segments the complex signal processing task into distinct operational stages: receiving multiple RF signals, converting them to energy values, filtering harmful components, comparing against thresholds, and making detection decisions. This segmentation allows the system to process multiple signals reliably while managing complexity through structured, modular processing steps that can be efficiently implemented in the processor.
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
The proposed solution effectively increases the accuracy of object detection by specifically targeting low-profile objects and mitigating false alarms, thereby enhancing the reliability of the movable device's navigation and operation.
Implementation Method 1
an RF signal receiver receiving radio frequency signals reflected by an object
Data Source
AI summary
An object detection method performed by a movable device is provided. The object detection method includes emitting radio frequency signals, receiving radio frequency signals reflected by an object, obtaining N sample sequences by digitizing N radio frequency signals, N being a natural number greater than or equal to 2, from among the received radio frequency signals, obtaining a matrix including each of the N sample sequences as a column, in an order in which the radio frequency signals are received, obtaining a resulting signal by summing samples for each row of the matrix and obtaining an energy value from the resulting signal, and detecting the object based on the obtained energy value being greater than or equal to a certain threshold value.