Indoor Visible Light Fingerprint Location Method Based on Marine Predator Algorithm-Optimized Least Squares Support Vector Machine
Abstract
:1. Introduction
2. Method
2.1. Indoor Visible Light Localization Model
2.2. Channel Model
2.3. LSSVM
2.4. MPA
- (1)
- Initialization phase. To prepare for the optimization phase, the MPA initializes the position of the prey within the search space, a process that is completely randomized by the following formula:
- (2)
- Optimization phase. MPA is divided into three stages: high speed ratio (), equal speed ratio (), and low speed ratio (), where t is the current number of iterations and T is the maximum number of iterations. For each phase, MPA defines different search rules to complete the optimization process.
- (3)
- FADS effect. The fish aggregation device effect parameter (FADS) temporarily alters the foraging behavior of marine predators. Consideration of this effect enables the MPA to avoid the problem of becoming stuck in local extremes during the optimization process.
- (4)
- Marine memory. Marine predators can remember regions rich in sources of prey misses. Therefore, after performing the optimization phase of the algorithm and updating the prey matrix and predator matrix, the fitness of each solution in the current iteration is compared with its equivalent in the previous iteration, and if it is more fit, the current solution replaces the previous solution, resulting in the optimal solution.
2.5. Positioning Methods
- (1)
- Perform (0, 1) normalization of the signal strength received by the illuminance meter. The specific formula is as follows:
- (2)
- Determine the parameters of the MPA algorithm, including the number of prey , the maximum number of iterations , the FADS, and the initial position of the prey . Let the optimal position be .
- (3)
- Define the fitness function. The fitness function chosen in this paper is as follows:
- (4)
- Iterative optimization is performed in each of the three different phases to update the predator’s position iteratively. Calculate the fitness value of the prey position and store the marine memory, integrate the effect of FADS on the fitness value, calculate the optimal predator position based on the prey position and behavior, and finally determine the current optimal position , and from this, obtain [].
- (5)
- The iteration termination condition is determined. When the iteration proceeds to the maximum number of times or when the predator’s fitness value reaches the preset accuracy, the iteration terminates and outputs the optimal parameter set [], otherwise return to step 4.
- (6)
- The computed global optimal parameters of the MPA are substituted into the LSSVM to complete the model building for fingerprint localization.
3. Results
3.1. Experimental Platforms
3.2. Experimental Results
3.3. Algorithm Comparison
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Value |
---|---|
Voltage/V | 220 |
Power/W | 3 |
Initial light effect/(Lm/W) | 70 |
Luminous flux/Lm | 250 |
Illumination | Parameter |
---|---|
Measuring range/Lux | 1–200,000 |
Working voltage/V | 4.5 |
Illumination accuracy | ±3% rdg |
Localization Algorithm | Max Location Error/m | Average Localization Error/m |
---|---|---|
MPA-LSSVM | 0.1213 | 0.041 |
LSSVM | 0.2289 | 0.094 |
Location Algorithm | Max Localization Error/m | Average Localization Error/m | Iteration Times |
---|---|---|---|
MPA-LSSVM | 0.1213 | 0.041 | 21 |
GA-LSSVM | 0.1391 | 0.057 | 37 |
PSO-LSSVM | 0.1702 | 0.085 | 45 |
The Experimental Environment | Average Localization Error/m | 90% of the Positioning Points | |
---|---|---|---|
One LED | A | 0.049 | 0.039 |
Two LEDs | A, B | 0.046 | 0.037 |
A, C | 0.043 | 0.033 | |
Three LEDs | A, B, C | 0.042 | 0.031 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Mei, Y.; Deng, Y. Indoor Visible Light Fingerprint Location Method Based on Marine Predator Algorithm-Optimized Least Squares Support Vector Machine. Appl. Sci. 2024, 14, 7416. https://doi.org/10.3390/app14167416
Mei Y, Deng Y. Indoor Visible Light Fingerprint Location Method Based on Marine Predator Algorithm-Optimized Least Squares Support Vector Machine. Applied Sciences. 2024; 14(16):7416. https://doi.org/10.3390/app14167416
Chicago/Turabian StyleMei, Yuanjia, and Yong Deng. 2024. "Indoor Visible Light Fingerprint Location Method Based on Marine Predator Algorithm-Optimized Least Squares Support Vector Machine" Applied Sciences 14, no. 16: 7416. https://doi.org/10.3390/app14167416