چكيده به لاتين
In the last decade, spoofing attacks have been identified as the most dangerous deliberate interference on the GPS. The spoofer purpose is to force the GPS receiver to generate a false navigation solution. In fact, spoofing is one of the most important threats to GPS signals, which routine methods can not cope with. This thesis has been developed to identify the spoofing attack and deal with it. Spoofing countermeasures consists of two stages of identification and compensation. To deal spoofing with deception, various methods are proposed and implemented in different sectors of the receiver. So that a spoofing attack occurs anywhere in the receiver, the proposed comprehensive method is able to identify and reduce it. In order to test the proposed methods, four different spoofing scenarios were designed and implemented.
In the acquisition section, the real-time identification and reduction algorithm based on the wavelet transform is proposed, in which decomposition disturbances are represented as large coefficients of wavelet. After deciphering the satellite signal, we will deceive the identification and retrieval of the destroyed satellites. A discrete wavelet transform based on a dual tree is also used for this purpose. In the tracking section, the spoofing effect is presented as decay region of the complex correlation function. Thus, identification of spoofing in the track loop is performed by introducing a exhaustive metric based on spoofing samples from the complex correlation function. The proposed metric examines the output of imaginary and real parts of correlation output of several bbranchs. Deciding whether or not a signal with a proposed metric is valid or fake is performed by statistical hypotheses tests in the proposed identification algorithm.
In order to compensate for the error caused by deception in the tracing loop, an expanded comparative Kalman filter has been proposed, which is applied as a predictor to the PLL track loop. In the spoof mitigation algorithm, a new way of simultaneously estimating the variance matrix of the measurement vector and process noise vector is presented in accordance with the quality of the GPS signal. By implementing the proposed algorithm, the error from deception in all types of data dropped by an average of 90%.
