Position Estimation Using Machine Learning Bisection
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Solution Overview
Problem
Existing position estimation systems require multiple receivers in each area, leading to instability in position estimation, especially in environments with multiple reflections, and necessitate a receiver in each receiving area, which is inefficient.
Innovation Solution
A position estimation system using a control unit connected to multiple receivers via wired or wireless connections, employing machine learning to estimate the transmitter's position by narrowing down its presence range step-by-step using bisection method with data from all receivers, eliminating the need for receivers in each receiving area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the area is divided into smaller receiving areas for position estimation, then the position estimation precision is improved, but the number of receivers required increases
Solution Approach 1:
The monitoring area is divided into multiple receiving areas, and each receiving area is further divided into multiple zones. This hierarchical segmentation allows for finer position estimation without requiring a proportional increase in receivers across the entire area, as receivers can be strategically placed to cover multiple zones.
Solution Approach 2:
A single receiver is designed to perform multiple functions: it can detect radio waves from multiple transmitters simultaneously and determine positions in multiple zones within its coverage area. This multi-functionality reduces the total number of receivers needed while maintaining high position estimation precision across the entire divided area.
2Reliability
If multiple receivers are deployed in each receiving area to improve position estimation reliability, then the reliability is improved, but the device complexity increases
Solution Approach 1:
Multiple receiving areas are merged into a single integrated system where a receiver in one area can contribute data for position estimation in adjacent areas. This merging reduces redundancy and simplifies the overall system architecture while maintaining reliable position estimation through collaborative data processing across zones.
Solution Approach 2:
A central control device acts as an intermediary that collects data from multiple receivers and performs the position estimation calculation. This intermediary approach allows the system to achieve high reliability through data aggregation without requiring each receiving area to have multiple receivers, thereby reducing device complexity.
3Measurement precision
If receivers are placed in each receiving area to maintain estimation accuracy, then the measurement precision is maintained, but the ease of operation deteriorates
Solution Approach 1:
The system transitions from a two-dimensional grid of receivers covering the entire area to a more efficient spatial arrangement where receivers are positioned to optimize coverage of multiple zones. This dimensional optimization allows accurate position estimation while reducing the number of deployment points, thereby improving ease of operation.
Data Source
AI summary
A position detection system (1) includes a plurality of receivers (3) that receive a radio wave from a transmitter (2) and a control unit (4) connected to these receivers (3). The control unit (4) estimates the presence position of the transmitter (2) using machine learning from advance data obtained beforehand. The advance data is data based on a radio wave received by the receiver (3) in a state where a transmitter (11) is placed at a position where its coordinates are clear within an area. The control unit (4) narrows down a presence range of the transmitter (2) in a step-by-step manner by the bisection method using data of the radio waves received by all the receivers (3) and determines one area from among a plurality of areas divided in advance within the area as the presence position of the transmitter (2).