Compensating GPS Signal Deception Error by Using Wavelet Transform Based on PSO Algorithm in Receiver Acquisition Section

The spoofing attack is one of the most serious interferences in the Global Positioning System (GPS). By propagating a signal structurally similar to the original GPS signal, the spoofers try to influence the function of different parts of the receiver and force it to make a wrong positioning. This study focus on the acquisition stage. During the acquisition process, GPS receivers estimate the values of Doppler frequency and Pseudo Random Noise (PRN) code phase of the received signal, which are necessary for tracking the GPS satellite signals. One of the effects of the spoofing signal in the acquisition unit of the receiver is to increase the interactions in the Quadrate correlation taps (Q-correlation tap). In 2018, adding a denoising unit on the Q-correlation tap in the acquisition stage to reduce the interactions mentioned above was presented as a spoofing mitigation method. In this paper, the mentioned method is placed as the primary basis of the work. Here, by using powerful methods of evolutionary computing, the denoising unit added in the Q-correlation tap is tried to be optimally adjusted to mitigate the spoofing attack. Specifically, to achieve a more efficient denoising method for spoofing mitigation, the Particle Swarm Optimization (PSO) algorithm is proposed to determine the critical parameters of the Discrete Wavelet Transform (DWT) based on the Haar wavelet. In order to evaluate the proposed method, first, the noise reduction performance of the algorithm is measured on four benchmark signals, namely Blocks, Bumps, Heavy Sine, and Doppler. Then, compared to four traditional methods, namely, Rigrsure, Heursure, Sqtwolog, and Minimaxi, the developed de-nosing method outperformed the former methods by 47.3%, 38.4%, 47,3%, and 30%, respectively. Finally, the proposed algorithm was placed in the Q-correlation tap of the GPS receiver acquisition stage, and its performance in reducing the spoof effects was investigated. The results show that the proposed algorithm is 37.74% more efficient compared to the method that was considered the primary method.