Target Count Estimation Using Multi-Access-Point WLAN Optimization
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Solution Overview
Problem
Conventional target number estimation techniques using mathematical optimization with a single sensor type, such as wireless LAN, do not leverage the advantages of multi-sensor configurations, limiting accuracy in estimating the number of targets.
Innovation Solution
A target number estimation device that utilizes mathematical optimization processing on data from multiple wireless LAN access points to estimate the number of people or devices in a geographical area, employing methods like RSSI, TOA, and CSI to enhance accuracy, even in scenarios with a single sensor type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If mathematical optimization is applied using only one sensor type (e.g., wireless LAN), then the device complexity is reduced, but the measurement precision of target number estimation deteriorates
Solution Approach 1:
The patent segments the sensor system into multiple independent sensor types (wireless LAN, Bluetooth, RFID, etc.), each operating independently to observe targets in their respective measurement ranges. This segmentation allows the system to process data from diverse sensor modalities without increasing overall system complexity, thereby improving estimation accuracy through multi-source information fusion.
Solution Approach 2:
The patent implements a universal mathematical optimization framework that can process data from multiple sensor types simultaneously. The estimation device is designed to handle various sensor data formats and measurement ranges uniformly, applying the same optimization algorithm regardless of sensor type, thus achieving multi-functionality without proportionally increasing device complexity.
2Measurement precision
If multiple sensor types are used in a multi-sensor configuration, then the measurement precision of target number estimation is improved, but the device complexity increases
Solution Approach 1:
The patent merges data from multiple sensor types into a unified estimation process. The mathematical optimization unit combines observation data from wireless LAN, Bluetooth, RFID and other sensors, along with their respective measurement ranges and error characteristics, to jointly estimate the target number. This merging approach improves precision while managing complexity through integrated processing.
Solution Approach 2:
The patent changes the parameters of the optimization problem to accommodate multiple sensor types. It formulates simultaneous equations that incorporate measurement ranges, error variables, and observation data from different sensor types as adjustable parameters. By dynamically adjusting these parameters based on available sensors, the system achieves high precision without being constrained by fixed complex configurations.
3Ease of operation
If only one sensor type is available in actual operation, then the ease of operation is improved, but the advantage of mathematical optimization is lost
Solution Approach 1:
The patent implements a dynamic sensor configuration system where the mathematical optimization algorithm adapts to the number and types of sensors actually deployed. The system can operate with a single sensor type or multiple sensor types, dynamically adjusting the optimization process based on available resources. This dynamic adaptability maintains ease of operation while preserving optimization benefits regardless of sensor availability.
Solution Approach 2:
The patent uses parameter changes to enable the optimization system to function effectively with varying sensor configurations. By formulating the optimization problem with flexible parameters that can be set based on actual sensor deployment, the system maintains its precision advantages whether one or multiple sensor types are used, without requiring complex pre-configuration.
Data Source
AI summary
According to one embodiment, a target number estimation device acquires first and second signals. The first signal includes information of a set of a first area and the number of targets of the first area. The second signal includes information of a set of a second area and the number of targets of the second area. The target number estimation device applies mathematical optimization processing on the number of targets of the first areas and the number of targets of the second areas to generate a third signal including information of a set of a third area and the number of targets of the third area. The first signal is generated based on first information of a first wireless system. The second signal is generated based on second information of the first wireless system.