Real-time satellite visibility deception detection method and system based on satellite trajectory estimation

Abstract

The invention relates to the technical field of satellite trajectory calculation, and discloses real-time satellite visibility deception detection based on satellite trajectory estimation, which comprises the following steps of: calculating a visible satellite list at the position and time through a satellite trajectory model according to the geographic position and the current time of a receiver; detecting and analyzing the received satellite signal, and judging whether the satellite signal belongs to a visible satellite list or not; and analyzing and judging whether the received satellite signal is real or not based on the historical satellite data and the analyzed satellite signal. By adopting an advanced signal analysis technology and a machine learning algorithm, normal signals and forged signals can be quickly identified and distinguished, and the method is particularly excellent in performance in complex and dynamic environments. In addition, the detection method based on real-time satellite trajectory estimation enables the system to respond to potential threats in time, and the overall safety is improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite trajectory calculation technology, and in particular to a real-time satellite visibility deception detection method and system based on satellite trajectory estimation. Background Art

[0002] In recent years, global navigation satellite system (GNSS) technology has developed rapidly, becoming an indispensable infrastructure in modern society. With the successive implementation of systems such as GPS, Beidou, and GLONASS, satellite navigation has demonstrated tremendous application potential in transportation, agriculture, military, and personal positioning. Simultaneously, continuous advancements in related technologies have made the acquisition, processing, and application of satellite signals increasingly accurate and efficient. In particular, the development of high-precision positioning technology has provided strong support for cutting-edge applications such as autonomous driving and intelligent transportation systems. However, with the widespread adoption of satellite navigation systems, security and reliability issues have also emerged, particularly the risk of spoofing and false signals.

[0003] While existing spoofing signal detection methods have improved the security of satellite navigation systems to a certain extent, they still have many shortcomings. First, existing methods often rely on traditional signal analysis techniques and lack adaptability to real-time dynamic environments, resulting in poor detection results in complex environments. Furthermore, many detection algorithms have limited ability to extract signal features and are unable to effectively distinguish between normal signals and spurious signals, especially in situations of poor signal quality. Furthermore, existing technologies suffer from low computational efficiency when processing large amounts of real-time data, making it difficult to meet the high real-time requirements. These shortcomings make existing technologies inadequate for modern, complex application scenarios. Therefore, achieving more efficient and accurate real-time satellite signal spoofing detection has become a pressing issue for current technologies. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem addressed by this invention is the accuracy and real-time performance of spoofing signal detection in satellite navigation systems. Existing technologies struggle to effectively identify spoofed signals in complex environments and are insufficient in signal feature extraction, failing to meet the demands of modern high-precision positioning applications.

[0006] To solve the above technical problems, the present invention provides the following technical solution: a real-time satellite visibility deception detection method based on satellite trajectory estimation, comprising: According to the receiver's geographical location and current time, the satellite trajectory model is used to calculate the list of visible satellites at that location and current time; Detect and analyze received satellite signals to determine whether they belong to the visible satellite list; Based on historical satellite data and analyzed satellite signals, it is analyzed and determined whether the received satellite signals are real.

[0007] As a preferred embodiment of the real-time satellite visibility deception detection method based on satellite trajectory estimation of the present invention, the geographical location includes the longitude, latitude and altitude of the location of the receiver; The current time includes the receiver's current coordinated universal time and epoch information.

[0008] As a preferred embodiment of the real-time satellite visibility deception detection method based on satellite trajectory estimation described in the present invention, calculating the visible satellite list includes accessing the latest satellite almanac data; using Kepler's law to calculate the position of each satellite at a given time, and using an atmospheric refraction model to adjust the propagation path of the satellite signal to ensure calculation accuracy; Calculate the elevation and azimuth of each satellite relative to the receiver, determine whether the satellite is within the field of view, and record all satellites that meet the conditions in the visible satellite list; The satellite almanac data includes orbital parameters and timestamp information of each satellite.

[0009] As a preferred embodiment of the real-time satellite visibility deception detection method based on satellite trajectory estimation of the present invention, detecting and analyzing received satellite signals includes converting the received satellite signals into digital signals, demodulating the digital signals, extracting key features of the digital signals by fast Fourier transform, and marking the digital signals as valid signals if the key features of the digital signals match the features of visible satellites; If the signal is not in the list of visible satellites, it is marked as a suspicious signal; The key features include frequency features, intensity features, and phase features.

[0010] As a preferred embodiment of the real-time satellite visibility spoofing detection method based on satellite trajectory estimation of the present invention, the historical satellite data includes key features of normal signals and key features of known spoofing signals; Analyzing and determining whether the received satellite signal is genuine includes using key features of a normal signal and key features of a known spoofing signal to construct a key feature vector of the normal signal and a key feature vector of the known spoofing signal, respectively, and training a prediction model; The prediction model is a support vector machine and a random forest. The outputs of the two models are combined and the final prediction result is obtained through weighted voting. The formula is expressed as: Output of the SVM model: , in, represents the key feature vector of the input, Represents the prediction function of the SVM model; Output of the Random Forest model: , in, represents the key feature vector of the input, Represents the prediction function of the random forest model; , , in, The final prediction result is represented by represents the predicted output of the SVM model, represents the prediction output of the random forest model; represents the weight of the SVM model, represents the weight of the random forest model, ; represents the entropy of the input feature vector, Representation characteristics The probability distribution of represents the total number of features; represents the normalization function; Represents the adjustment parameter of information entropy.

[0011] As a preferred embodiment of the real-time satellite visibility deception detection method based on satellite trajectory estimation of the present invention, the analysis and determination of whether the received satellite signal is genuine further includes converting the prediction result into a probability of authenticity as follows: , in, The final prediction result is represented by Indicates the adjustment parameters, Indicates adjustment parameters; Set a probability threshold ,when , then the satellite signal is a real signal; when , the satellite signal is a spoofing signal.

[0012] As a preferred solution of the real-time satellite visibility deception detection method based on satellite trajectory estimation described in the present invention, the key features of the digital signal are used to form a key feature vector, the key feature vector is input into a prediction model for analysis, the prediction model outputs the authenticity probability of the satellite signal, and determines whether the satellite signal is real based on a set probability threshold.

[0013] As a preferred solution of the real-time satellite visibility deception detection method based on satellite trajectory estimation of the present invention, wherein: the geographic location and time module obtains the geographic location and current time of the receiver; Satellite trajectory calculation module, calculates the list of visible satellites at a specific location and time; Signal reception and analysis module: detects and analyzes received satellite signals to determine whether they belong to the visible satellite list; Feature extraction and judgment module, extracts the key features of the signal and constructs the feature vector; The spoofing signal detection module analyzes the authenticity of received signals based on historical data and machine learning models.

[0014] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0015] A computer-readable storage medium stores a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods of the present invention.

[0016] Beneficial Effects of the Invention: The real-time satellite visibility spoofing detection method based on satellite trajectory estimation, provided by this invention, utilizes advanced signal analysis techniques and machine learning algorithms to rapidly identify and distinguish legitimate signals from spoofed ones, particularly in complex and dynamic environments. Furthermore, this detection method, based on real-time satellite trajectory estimation, enables the system to promptly respond to potential threats, improving overall security. This innovation not only enhances the reliability of satellite navigation but also provides a solid technical foundation for applications in areas such as autonomous driving and intelligent transportation, thereby promoting the development and security of related industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is an overall flow chart of a real-time satellite visibility deception detection method based on satellite trajectory estimation provided by the first embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0020] Example 1, reference Figure 1 , as one embodiment of the present invention, provides a real-time satellite visibility deception detection method based on satellite trajectory estimation, comprising: S1: Based on the receiver's geographic location and current time, the satellite trajectory model is used to calculate the list of visible satellites at that location and current time.

[0021] The geographical location includes the longitude, latitude and altitude of the receiver's location.

[0022] The current time includes the receiver's current Coordinated Universal Time and epoch information.

[0023] Calculating the visible satellite list includes accessing the latest satellite almanac data; using Kepler's laws to calculate the position of each satellite at a given time; and employing an atmospheric refraction model to adjust the propagation path of satellite signals to ensure calculation accuracy.

[0024] Furthermore, it ensures that the satellite navigation system maintains high-precision positioning in complex environments, reduces navigation errors caused by signal interference or loss, and improves user safety and trust.

[0025] S2: Detect and analyze the received satellite signal to determine whether it belongs to the visible satellite list.

[0026] Calculate the elevation and azimuth of each satellite relative to the receiver, determine whether the satellite is within the field of view, and record all satellites that meet the conditions in the visible satellite list.

[0027] Satellite almanac data includes orbital parameters and timestamp information for each satellite.

[0028] Furthermore, the introduction of efficient signal feature extraction technology combined with machine learning models for real-time analysis can quickly distinguish normal signals from deceptive signals and improve detection accuracy.

[0029] S3: Based on historical satellite data and analyzed satellite signals, analyze and determine whether the received satellite signal is real.

[0030] Detecting and analyzing received satellite signals includes converting the received satellite signals into digital signals, demodulating the digital signals, extracting key features of the digital signals through fast Fourier transform, and marking the digital signals as valid signals if the key features of the digital signals match the features of visible satellites.

[0031] If the signal is not in the list of visible satellites, it is marked as suspicious.

[0032] The key features include frequency features, intensity features, and phase features.

[0033] The historical satellite data includes key features of normal signals and key features of known deceptive signals.

[0034] Analyzing and determining whether the received satellite signal is genuine includes using key features of a normal signal and key features of a known spoofing signal to construct a key feature vector of the normal signal and a key feature vector of the known spoofing signal, respectively, and training a prediction model.

[0035] The prediction model is a support vector machine and a random forest. The outputs of the two models are combined and the final prediction result is obtained through weighted voting. The formula is expressed as: Output of the SVM model: , in, represents the key feature vector of the input, Represents the prediction function of the SVM model.

[0036] Output of the Random Forest model: , in, represents the key feature vector of the input, Represents the prediction function of the random forest model.

[0037] , , in, The final prediction result is represented by represents the predicted output of the SVM model, represents the prediction output of the random forest model; represents the weight of the SVM model, represents the weight of the random forest model, ; represents the entropy of the input feature vector, Representation characteristics The probability distribution of represents the total number of features; represents the normalization function; Represents the adjustment parameter of information entropy.

[0038] Analyzing and judging whether the received satellite signal is real also includes converting the prediction result into the probability of authenticity using the formula: , in, The final prediction result is represented by Indicates the adjustment parameters, Indicates adjustment parameters.

[0039] Set a probability threshold ,when , then the satellite signal is a real signal.

[0040] when , the satellite signal is a spoofing signal.

[0041] Probability threshold It is dynamic and the formula is: , in, represents the dynamic threshold, represents the entropy of the input feature vector, The final prediction result is represented by represents the weight, represents the exponential coefficient, Represents the adjustment parameters to control the shape of the Sigmoid function. represents the amplitude coefficient, and Represents the order of polynomials and product terms.

[0042] The value range is from 0 to positive infinity.

[0043] The key features of the digital signal are used to form a key feature vector, which is then input into the prediction model for analysis. The prediction model outputs the authenticity probability of the satellite signal and determines whether the satellite signal is real based on the set probability threshold.

[0044] Furthermore, by combining machine learning algorithms such as support vector machines and random forests, the ability to judge the authenticity of satellite signals is improved, and it can adapt and learn in a constantly changing environment, thereby optimizing the recognition process.

[0045] Example 2, an embodiment of the present invention, provides a real-time satellite visibility deception detection system based on satellite trajectory estimation, including: The geographic location and time module obtains the geographic location and current time of the receiver.

[0046] Satellite trajectory calculation module calculates the list of visible satellites at a specific location and time.

[0047] Signal reception and analysis module: detects and analyzes the received satellite signal to determine whether it belongs to the visible satellite list.

[0048] Feature extraction and judgment module extracts the key features of the signal and constructs the feature vector.

[0049] The spoofing signal detection module analyzes the authenticity of received signals based on historical data and machine learning models.

[0050] Example 3, an embodiment of the present invention, is different from the previous two embodiments in that: If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0051] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0052] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0053] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0054] In Example 4, experiments were conducted in four typical geographical environments: urban, rural, mountainous, and aquatic environments, simulating real-world scenarios of varying complexity. In each environment, normal satellite signals and spoofed signals of varying strengths were simulated to evaluate the detection performance of the method under these conditions.

[0055] The system is equipped with a high-precision GNSS receiver, ensuring stable reception of satellite signals in all environments. The receiver's geographic location parameters (longitude, latitude, and altitude) and current time (Coordinated Universal Time and epoch information) are accurately recorded for subsequent calculation of the visible satellite list using a satellite trajectory model. Using the latest satellite almanac data, Kepler's laws and atmospheric refraction models are used to accurately calculate the position of each satellite at a given time, and determine its elevation and azimuth relative to the receiver, thereby generating a visible satellite list.

[0056] During the signal detection and analysis phase, received satellite signals are converted to digital signals and subjected to a Fast Fourier Transform (FFT) to extract frequency, intensity, and phase features. These key features are constructed into feature vectors and input into a support vector machine (SVM) and random forest (RF) model for classification and prediction. The outputs of the SVM and RF models are combined using a weighted voting mechanism to generate the final prediction. A dynamic threshold formula is used to adjust the threshold based on the entropy of the input feature vector, and the prediction result is converted into a probability of authenticity using a sigmoid function.

[0057] During the experiment, we set various signal strengths and simulated legitimate and spoofed signals of varying strengths by adjusting the transmit power. Each signal type was received and analyzed 20 times in each environment to ensure data reliability and statistical significance. All experimental data was systematically recorded in Table 1 for subsequent analysis and comparison. The experimental results are shown in Table 1.

[0058] Table 1 Experimental data table , Table 1 analyzes the experimental data presented above, showing the recognition rates of traditional spoofing signal detection methods and our method under varying signal strength conditions in four different geographic environments. The data clearly demonstrates that our method significantly outperforms existing methods across all environments and signal types, as evidenced by improvements in both genuine and spoofing signal recognition rates.

[0059] First, in urban environments, the traditional method's recognition rate for normal signals was 92%, while the method of the present invention increased this to 98%, demonstrating significantly higher accuracy. This improvement is primarily attributed to the dual-model fusion mechanism combining SVM and random forest, as well as the introduction of dynamic thresholding, which effectively reduces false positives and false negatives. For spoofed signals, the recognition rates of the traditional method were 88% and 90%, while the method of the present invention increased these to 95% and 97%, significantly enhancing its ability to detect forged signals.

[0060] In a rural environment, the traditional method had an 89% recognition rate for normal signals and 85% and 88% for spoofed signals, while the method of the present invention improved these rates to 95% and 93%, respectively. This demonstrates that the method of the present invention can maintain efficient detection performance even under conditions of low signal quality and less environmental interference. Data from mountainous and aquatic environments further validated the robustness of the method of the present invention. In mountainous areas, the traditional method had a recognition rate of 85% and 80% for normal signals and 90% and 92% for spoofed signals, respectively. In aquatic environments, the traditional method had a recognition rate of 90% and 87%, while the method of the present invention achieved 97% and 96%, respectively. These results demonstrate that the method of the present invention can provide more reliable detection results, regardless of whether the signal strength is low or the environment is complex.

[0061] Data analysis further shows that traditional methods have high false positive and false negative rates when processing spoofed signals, especially in conditions of low signal strength or high environmental interference, where detection performance degrades significantly. However, the proposed method, through dynamic thresholding and multi-model fusion, not only improves recognition rates but also effectively reduces false positive and false negative rates, enhancing the overall reliability and security of the system.

[0062] Furthermore, by incorporating information entropy as a modulating factor for the dynamic threshold, the method can adaptively adjust detection sensitivity based on the complexity and uncertainty of input features. This innovative design enables the method to maintain efficient and accurate detection performance across a variety of application scenarios, significantly outperforming traditional methods that rely on fixed thresholds and single classification models.

[0063] In this embodiment, the trained support vector machine (SVM) and random forest (RF) models, combined with a weighted voting mechanism and dynamic threshold adjustment, significantly improve the accuracy of signal authenticity judgment. Specifically, when the SVM model is used alone, the recognition rate reaches 92% and 90% in urban and aquatic environments; when the RF model is used alone, the recognition rate is 88% and 87%, respectively. However, when combined with the dual-model fusion and dynamic threshold mechanism of the present invention, the recognition rate increases to 98% and 97%, respectively, demonstrating a significant performance improvement.

[0064] Furthermore, the algorithm's operational efficiency has been optimized for practical applications. Through parallel computing and optimized algorithmic structure, the proposed method is able to rapidly respond and accurately determine the authenticity of satellite signals in applications requiring high real-time performance, such as autonomous driving and intelligent transportation systems, ensuring high system reliability and security. In contrast, traditional methods, due to their low computational efficiency in complex environments, struggle to meet real-time requirements, limiting their potential for application in highly dynamic applications.

[0065] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A real-time satellite visibility deception detection method based on satellite trajectory estimation, characterized in that: include: According to the receiver's geographical location and current time, the satellite trajectory model is used to calculate the list of visible satellites at that location and current time; Detect and analyze received satellite signals to determine whether they belong to the visible satellite list; Based on historical satellite data and analyzed satellite signals, it is analyzed and determined whether the received satellite signals are real.

2. The real-time satellite visibility deception detection method based on satellite trajectory estimation according to claim 1, characterized in that: The geographical location includes the longitude, latitude and altitude of the location of the receiver; The current time includes the receiver's current coordinated universal time and epoch information.

3. The real-time satellite visibility deception detection method based on satellite trajectory estimation according to claim 2, characterized in that: Calculating the visible satellite list includes accessing the latest satellite almanac data; Use Kepler's laws to calculate the position of each satellite at a given time, and employ an atmospheric refraction model to adjust the propagation path of satellite signals to ensure calculation accuracy. Calculate the elevation and azimuth of each satellite relative to the receiver, determine whether the satellite is within the field of view, and record all satellites that meet the conditions in the visible satellite list; The satellite almanac data includes orbital parameters and timestamp information of each satellite.

4. The real-time satellite visibility deception detection method based on satellite trajectory estimation according to claim 3, characterized in that: Detecting and analyzing received satellite signals includes converting the received satellite signals into digital signals, demodulating the digital signals, extracting key features of the digital signals through fast Fourier transform, and marking the digital signals as valid signals if the key features of the digital signals match the features of visible satellites; If the signal is not in the list of visible satellites, it is marked as a suspicious signal; The key features include frequency features, intensity features, and phase features.

5. The real-time satellite visibility deception detection method based on satellite trajectory estimation according to claim 4, characterized in that: The historical satellite data includes key features of normal signals and key features of known deceptive signals; Analyzing and determining whether the received satellite signal is genuine includes using key features of a normal signal and key features of a known spoofing signal to construct a key feature vector of the normal signal and a key feature vector of the known spoofing signal, respectively, and training a prediction model; The prediction model is a support vector machine and a random forest. The outputs of the two models are combined and the final prediction result is obtained through weighted voting. The formula is expressed as: Output of the SVM model: ， in, represents the key feature vector of the input, Represents the prediction function of the SVM model; Output of the Random Forest model: ， in, represents the key feature vector of the input, Represents the prediction function of the random forest model; ， ， in, The final prediction result is represented by represents the predicted output of the SVM model, represents the prediction output of the random forest model; represents the weight of the SVM model, represents the weight of the random forest model, ; represents the entropy of the input feature vector, Representation characteristics The probability distribution of represents the total number of features; represents the normalization function; Represents the adjustment parameter of information entropy.

6. The real-time satellite visibility deception detection method based on satellite trajectory estimation according to claim 5, characterized in that: Analyzing and judging whether the received satellite signal is real also includes converting the prediction result into the probability of authenticity using the formula: ， in, The final prediction result is represented by Indicates the adjustment parameters, Indicates adjustment parameters; Set a probability threshold ,when , then the satellite signal is a real signal; when , the satellite signal is a spoofing signal.

7. The real-time satellite visibility deception detection method based on satellite trajectory estimation according to claim 6, characterized in that: The key features of the digital signal are used to form a key feature vector, which is then input into the prediction model for analysis. The prediction model outputs the authenticity probability of the satellite signal and determines whether the satellite signal is real based on the set probability threshold.

8. A real-time satellite visibility deception detection system based on satellite trajectory estimation, using the real-time satellite visibility deception detection method based on satellite trajectory estimation according to any one of claims 1 to 7, characterized in that: Geographic location and time module, obtains the geographic location and current time of the receiver; Satellite trajectory calculation module, calculates the list of visible satellites at a specific location and time; Signal reception and analysis module: detects and analyzes received satellite signals to determine whether they belong to the visible satellite list; Feature extraction and judgment module, extracts the key features of the signal and constructs the feature vector; The spoofing signal detection module analyzes the authenticity of received signals based on historical data and machine learning models.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the real-time satellite visibility deception detection method based on satellite trajectory estimation according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the real-time satellite visibility deception detection method based on satellite trajectory estimation according to any one of claims 1 to 7 are implemented.

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