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A Parking Detection Algorithm Based on Multitransitory Finite-State Machine Using Magnetic Wireless Sensor Network
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DOI：10.1109/JIOT.2023.3319340.png)
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摘要
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Due to the advantages of low-cost, easy use, and high-sensitivity, magnetic sensors are being widely used in parking detection. However, the magnetic signals contain a lot of noise, which can affect the detection performance. In this article, a parking detection algorithm based on a magnetic wireless sensor network is proposed, which adopts the translation-invariant wavelet denoising method to preprocess magnetic signals, aiming to reduce the impact of noise and meet the detection requirements. Considering the problems of weak magnetic vehicles and adjacent vehicle interference in practical applications, a multitransitory finite state machine based on variance signal is designed for parking detection. The experimental results in a standard parking lot show that the proposed algorithm has a significant improvement in accuracy compared with the classical algorithms, and provides theoretical support for building smart parking systems and improving vehicle management efficiency. In addition, the proposed algorithm can also accurately detect the special case of weak magnetic vehicles and has a wider range of applicability.
Keyword:
Detection algorithms
Perpendicular magnetic anisotropy
Magnetic anisotropy
Noise reduction
Magnetic sensors
Interference
Magnetic analysis
Finite state machine
magnetic sensor
parking detection
wavelet denoising
wireless sensor network
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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Parking Detection Method Based on Finite-State Machine and Collaborative Decision-Making.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
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