Pulse-Echo Signal Processing Using Kalman Filter Track Velocity
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Solution Overview
Problem
Pulse-echo measurement systems face challenges in distinguishing true measurement echoes from false echoes caused by obstacles, leading to incorrect readings when unwanted echoes rise above the threshold or true echoes fall below, causing devices to lock onto obstacles instead of the material level.
Innovation Solution
The method employs a recursive Kalman filter to track echoes by estimating velocity and predicting positions, selecting echoes with non-null velocity closest to the transmitter, and using a nearest neighbor procedure with a gating mechanism to discriminate between candidates, thereby identifying moving targets amidst clutter and maintaining tracks efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a time-varying threshold (TVT) is used to discriminate echoes, then false echoes from obstacles are reduced, but the device may lock onto unwanted echoes that rise above the threshold or lose true echoes that dip below it
Solution Approach 1:
The system transitions from a static TVT-based discrimination approach to a dynamic track-based approach where echo characteristics are continuously monitored and updated. Tracks are created and maintained based on velocity estimation and position prediction, allowing the system to adapt to changing echo patterns over time while maintaining consistent discrimination criteria.
Solution Approach 2:
The invention changes the discrimination parameter from simple amplitude thresholding (TVT) to a composite parameter including velocity, position, and track continuity. By estimating velocity and predicting position, the system creates a multi-dimensional discrimination space that makes it harder for false echoes to satisfy all criteria simultaneously.
2Ease of operation
If a simple discrimination window is used to track echoes, then the device can identify echoes within a positional range, but it cannot distinguish between moving material echoes and stationary obstacle echoes
Solution Approach 1:
The system introduces velocity estimation and position prediction to transform static echo tracking into dynamic target tracking. By calculating velocity from position changes over time and predicting future positions, the system can distinguish moving material echoes from stationary obstacle echoes based on their motion characteristics.
Solution Approach 2:
The track maintenance mechanism uses feedback from velocity estimation and position prediction to continuously update echo characteristics. The system compares predicted positions with actual echo positions, and uses this feedback to maintain accurate tracks and identify deviations that indicate false echoes.
3Measurement precision
If multiple tracks are maintained with velocity estimation, then moving targets can be identified amongst clutter, but the data processing complexity increases
Solution Approach 1:
The system segments the echo processing into distinct tracks, each representing a potential target. By dividing the complex multi-echo signal into separate tracked entities with individual velocity and position parameters, the system manages complexity through organization and modular processing of each track independently.
Solution Approach 2:
The invention extracts key characteristics (velocity, position, track continuity) from the raw echo signals and maintains only these essential parameters in the tracks. This extraction approach reduces data complexity by focusing on the most discriminating features while discarding redundant information.
Data Source
AI summary
Pulse echo signals containing false echoes are processed by forming tracks of multiple received echoes and monitoring these tracks by a recursive filter such as a Kalman filter. A track velocity is estimated for each track, and the position of each the next echo on the track is predicted.