Positioning Algorithm Using Discriminant Function Optimization

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional positioning systems using pattern-matching algorithms face inefficiencies in locating objects due to exhaustive calculations and lack of spatial correlation utilization, leading to slower search times and higher error rates.

Innovation Solution

The proposed algorithm constructs a continuous differentiable discriminant function using signal strength data from beacons and training locations, leveraging spatial correlation to speed up positioning by integrating signal vectors and using numerical optimization techniques like gradient descent and secant line search to find the minimum discriminant value, thereby reducing calculations and improving accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If exhaustive calculations are performed to compare signal strength fingerprints with all training locations, then positioning accuracy is improved, but positioning time increases significantly

Engineering Contradiction:

Improvepositioning accuracyVSAvoidpositioning time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary actions by constructing a discriminant function during the training phase using signal strength data from multiple training locations and beacons. This discriminant function is pre-computed and stored, allowing the positioning phase to directly evaluate the function rather than performing exhaustive comparisons with all training locations, thus reducing positioning time while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces the mechanical exhaustive search process with a mathematical discriminant function that encapsulates the spatial correlation relationships. Instead of mechanically comparing signal fingerprints with all stored training data, the system uses the discriminant function to directly compute positioning results, substituting computational brute-force with optimized mathematical evaluation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If pattern-matching algorithms are used to compare signal fingerprints, then positioning can be achieved, but the system lacks utilization of spatial correlation leading to higher error rates

Engineering Contradiction:

Improvepositioning capabilityVSAvoidpositioning error rate

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent changes the fundamental parameter of the positioning approach from simple pattern-matching (comparing signal fingerprints) to discriminant function evaluation that incorporates spatial correlation. The discriminant function uses parameters including signal strengths from multiple beacons, training location coordinates, and spatial correlation weights to compute positioning results, thereby improving precision by leveraging spatial relationships.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces a discriminant function as an intermediary that mediates between the raw signal strength data and the final positioning result. This intermediary function processes the spatial correlation information and signal data together, producing accurate positioning results without requiring direct comparison with all training locations, thus reducing error rates while maintaining ease of operation.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Device complexity

If feature vectors are constructed from signal strength data, then positioning data can be stored and compared, but the system does not fully utilize spatial correlation between training locations

Engineering Contradiction:

Improvedata structure complexityVSAvoidspatial correlation utilization

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent merges the construction of feature vectors with the incorporation of spatial correlation information into a unified discriminant function. Instead of storing separate feature vectors and coordinates for later comparison, the system combines all necessary parameters (signal strengths, coordinates, spatial correlation weights) into the discriminant function construction process, thereby utilizing spatial correlation fully while managing complexity through integrated processing.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS8102315B2Algorithm of collecting and constructing training location data in a positioning system and the positioning method therefor

Publication Date: 2012.01.24 IND TECH RES INST

AI summary

An algorithm of collecting and constructing training location data is provided as it is applied to a test space of a plurality of beacons and training locations. The signal patterns of beacons adjacent to each training location are detected. The signal pattern is converted into a signal vector and each signal vector is integrated for calculating a feature vector of each training location. The coordinate and the feature vector of each training location, after being recorded, are introduced into a numerical data fitting model for constructing the signal pattern function of each beacon. For positioning, the current signal patterns of the beacons adjacent to the user location are detected and converted to a discriminant function. Thereafter, the minimum of the discriminant function is computed so as to find the position of the user location.