This paper proposes a novel real-time spoofing detection method for Global Navigation Satellite System (GNSS) using three low-cost collinear antennas. Traditional multi-antenna spoofing detection methods are limited in application scenarios and have high hardware costs. The proposed method leverages the collinearity information of the antennas to constrain the observation equation, improving the estimation accuracy of the pointing vector. A binary statistical detection model based on the sum of squares (SSE) between the observed and estimated values of the pointing vector is employed for real-time spoofing signal detection. Simulation results confirm the efficacy of the proposed model, with the skewness coefficient error not exceeding 0.026. Experimental results demonstrate that the collinear antenna-based method reduces the standard deviation of the angle deviation of the pointing vector by over 55.62% in the presence of spoofing signals, achieving 100% spoofing detection with a 1 m baseline. The method is cost-effective and scalable, making it suitable for practical applications.This paper proposes a novel real-time spoofing detection method for Global Navigation Satellite System (GNSS) using three low-cost collinear antennas. Traditional multi-antenna spoofing detection methods are limited in application scenarios and have high hardware costs. The proposed method leverages the collinearity information of the antennas to constrain the observation equation, improving the estimation accuracy of the pointing vector. A binary statistical detection model based on the sum of squares (SSE) between the observed and estimated values of the pointing vector is employed for real-time spoofing signal detection. Simulation results confirm the efficacy of the proposed model, with the skewness coefficient error not exceeding 0.026. Experimental results demonstrate that the collinear antenna-based method reduces the standard deviation of the angle deviation of the pointing vector by over 55.62% in the presence of spoofing signals, achieving 100% spoofing detection with a 1 m baseline. The method is cost-effective and scalable, making it suitable for practical applications.