A Parking Detection Algorithm Based on Multitransitory Finite-State Machine Using Magnetic Wireless Sensor Network.

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 paper, 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 multi-transitory 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.