Ghost Echo Elimination in Reflection Signal Analysis
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Solution Overview
Problem
Existing driver assistance systems face issues with identifying false reflection signals, known as ghost echoes, which are virtual reflection points that do not correspond to actual physical objects, leading to misidentification of objects in the vehicle's surroundings.
Innovation Solution
A method and apparatus for analyzing reflection signals that determine time-dependent vectorial distances between reflection point candidates and classify signals as false based on specific conditions, including equal absolute values and mirror symmetry relative to a time-dependent plane, to distinguish between physical objects and ghost objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If time-of-flight analysis is used to identify reflection points, then distance measurement capability is improved, but false signals from ghost echoes cannot be distinguished from real objects
Solution Approach 1:
The system uses feedback from multiple reflection signals and their geometric relationships to continuously refine object identification. By analyzing the spatial patterns and time dependencies of multiple echoes, the system feedback-adjusts its interpretation to distinguish real objects from ghost echoes based on consistent geometric patterns.
Solution Approach 2:
The patent introduces geometric relationship analysis as an intermediary step between raw time-of-flight data and final object identification. This intermediary layer processes the reflection signals through geometric consistency checks, using the spatial relationships between multiple reflection points as a mediator to filter out false signals before final object classification.
2Reliability
If multiple reflection signals are analyzed to improve object identification, then reliability is improved, but computational complexity increases
Solution Approach 1:
The analysis process is segmented into distinct stages: first identifying individual reflection signals, then grouping them into candidate objects, and finally applying geometric consistency checks. This segmentation allows the system to process multiple signals systematically without overwhelming computational burden at any single stage.
Solution Approach 2:
The system applies geometric consistency checks selectively to reflection signal patterns that show potential object characteristics, rather than processing every possible signal combination. This partial action approach focuses computational resources on promising candidates while ignoring clearly spurious signals.
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
This approach effectively eliminates false signals, increasing the accuracy of object identification and preventing misidentification of non-existing objects, thereby enhancing the reliability of driver assistance systems.
Implementation Method 1
The electromagnetic waves will not always propagate on a direct path between the object and the sensor. Rather, the electromagnetic wave may be reflected by one or more mirroring surfaces along its path.
Data Source
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AI summary
The invention provides a method for analyzing reflection signals. Reflection signals corresponding to reflection point candidates (RPC1, RPC2, RPC3) are received (S1) by a sensor (S). For first to third reflection signals corresponding to respective first to third reflection point candidates (RPC1, RPC2, RPC3), distances (d1, d2, d3, x1, x2) are determined (S2). A first distance (d1) extends between the first and second reflection point candidates (RPC1, RPC2), a second distance (d2) extends between the second and third reflection point candidates (RPC2, RPC3), a third distance (d3) extends between the first and third reflection point candidates (RPC1, RPC3), and respective sensor distances (x1, x3) extend between the first or third reflection point candidate (RPC1, RPC3) and the sensor (S). It is checked (S3) if the time dependencies of the absolute value of the first distance (d1) and the absolute value of the second distance (d2) are equal, and if the time dependencies of the first distance (d1) and the second distance (d2) are mirror symmetric relative to a plane (P) which is orthogonal to the third distance (d3) and extends through the second reflection point candidate (RPC2). If this is the case, the reflection signal out of the first reflection signal and the third reflection signal corresponding to the reflection point candidate (RPC3) having a larger sensor distance (x3) is classified (S4) as a false signal.