An ADS-B signal authenticity detection method and device

Abstract

The application discloses an ADS-B signal authenticity detection method and device, and the method comprises the following steps: acquiring an ADS-B signal received by a ground receiving device and extracting corresponding target position information; judging whether the target position is located in a space exceeding a preset height threshold according to the target position information; if the target position exceeds the space exceeding the preset height threshold, a first detection method is controlled to be used to detect the authenticity of the ADS-B signal, otherwise, a second detection method is controlled to be used to detect the authenticity of the ADS-B signal; the first detection method is used to perform track prediction on the received ADS-B signal, and the authenticity of the ADS-B signal is judged according to the convergence state of the track prediction result; and the second detection method is a detection method different from the first detection method. The application has the advantages of simple implementation method, low detection cost and complexity, high detection efficiency and precision, wide application range and the like.

Description

Technical Field

[0001] This invention relates to the field of ADS-B (Automatic Dependent Surveillance-Broadcast) technology, and in particular to a method and apparatus for detecting the authenticity of ADS-B signals. Background Technology

[0002] ADS-B technology uses a broadcast data link to enable targets (including aircraft or ground vehicles, also known as monitored objects) to actively transmit their own identification and precise four-dimensional position data, thus achieving passive surveillance. Compared with traditional radar surveillance technology, ADS-B not only has a faster data refresh rate (data interval can reach 1 second, while traditional radar data is 4-12 seconds) and more accurate positioning (ADS-B uses GNSS positioning, with accuracy down to the meter level, while the accuracy of traditional radar is only a few hundred meters or more), but also has advantages such as lower cost, simpler installation, and the ability for aircraft to monitor each other. Therefore, ADS-B technology is currently receiving widespread attention.

[0003] However, because the ADS-B system uses an open data format, all data is transmitted in plaintext over the wireless link, and the operating frequency of 1090MHz is publicly fixed. The ground station surveillance system receives and parses the aircraft data according to the corresponding protocol. This means that the information displayed by the ground station surveillance system terminal is entirely dependent on the received messages. This implies that if the message format is obtained, various deceptive and jamming messages can be forged and sent. Furthermore, ADS-B technology uses a passive surveillance mode where the monitor sends data based on a completely trusted target, thus posing significant security risks. The ground station surveillance system terminal may receive a large number of false targets and struggle to distinguish the real target. Deceptive jamming messages cause both the ground station surveillance system and the aircraft's onboard systems to receive incorrect interference data. Therefore, a method for verifying the authenticity of ADS-B signals is needed to confirm the positional relationship between the aircraft position information provided by the ADS-B signal and the ground station, thereby identifying deceptive and jamming signals.

[0004] In existing technologies, the following three methods are typically used for ADS-B signal authenticity detection:

[0005] 1. A method of collaborative detection using multiple direction-finding base stations is adopted. That is, the target position is measured, and then the antenna receiving position of each direction-finding base station is adjusted according to the target position. The authenticity of the ADS-B signal is determined by the number of direction-finding base stations that can receive the target information.

[0006] For example, Chinese patent application CN102323567A discloses a method for detecting false ADS-B targets. This method involves measuring the target's location, then using a servo device at a direction-finding base station to first point a directional antenna towards the target's location. The ADS-B receiver receives the target information from that direction, and the data processing center analyzes the results from all direction-finding base stations. If two or more direction-finding stations can receive the target information, it indicates that the target is real; otherwise, it indicates that it is false.

[0007] However, this type of method not only requires the additional construction of direction-finding base stations, increasing implementation costs, but also suffers from problems such as complex detection processes and slow signal determination speed.

[0008] 2. Signature authentication method: The sender digitally signs the ADS-B message, and the receiver verifies the signature validity after receiving the ADS-B message to determine the legitimacy of the ADS-B message.

[0009] For example, Chinese patent application CN110177002A discloses an ADS-B message authentication method based on certificateless short signatures. This scheme involves the aircraft calculating its own public and private keys using a portion of the private key corresponding to its identity ID before takeoff. During flight, this is encapsulated into an ADS-B message, digitally signed, and then periodically broadcast. Upon receiving the ADS-B message, nearby aircraft or ground stations verify the validity of the signature using the aircraft's published public key. Messages that pass verification are used, while those that fail are discarded.

[0010] However, this type of verification method will damage the general data format of ADS-B, such as requiring the redefinition of field meanings or the extension of messages. Not all ADS-B receiving devices can follow the specific parsing mode. Aircraft that do not use the above verification mechanism cannot recognize the ADS-B messages generated by the above mechanism, which will lead to incompatibility, limited use cases, and security issues.

[0011] 3. Multi-channel ADS-B signal reception method, which means that multiple ADS-B ground stations receive ADS-B signals separately, and then combine the ADS-B signals received by each ADS-B ground station to achieve target positioning, so as to prevent ADS-B spoofing.

[0012] For example, Chinese patent application CN110988865A discloses a solution for preventing spoofing based on a four-channel ADS-B ground station. This solution uses a four-channel ground receiving device to receive ADS-B signals, selects a trajectory processing mode based on the received signal, and processes the trajectory of the transmitting target using its azimuth and range. By combining the four-channel pulse amplitude direction finding method and the signal amplitude measurement method, the target is located, thus solving the ADS-B spoofing problem.

[0013] However, this type of method requires the use of multi-channel receiving equipment, which is complex and costly to implement. Each device needs to use four independent antennas with the same radiation pattern function to uniformly cover the 360° azimuth, which requires high channel consistency. At the same time, it is also necessary to measure the target's transmission azimuth and calculate the distance to the transmitting target to determine the target's geometric position. The calculation process is complex and prone to causing large measurement errors. Summary of the Invention

[0014] The technical problem to be solved by the present invention is to provide an ADS-B signal authenticity detection method and device that is simple to implement, has low detection cost and complexity, high detection efficiency and accuracy, and wide applicability, in response to the technical problems existing in the prior art.

[0015] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0016] A method for detecting the authenticity of ADS-B signals, comprising the following steps:

[0017] Acquire the ADS-B signal received by the ground receiving equipment and extract the corresponding target location information;

[0018] Based on the target location information, determine whether the target location is located in an airspace exceeding a preset height threshold;

[0019] If the target location exceeds the preset height threshold in the airspace, the first detection method is used to detect the authenticity of the ADS-B signal; otherwise, the second detection method is used to detect the authenticity of the ADS-B signal.

[0020] The first detection method performs trajectory prediction on the received ADS-B signal and determines the authenticity of the ADS-B signal based on the convergence state of the trajectory prediction result. The second detection method is a different detection method from the first detection method.

[0021] Furthermore, the extraction of the corresponding target location information includes: demodulating and decoding the ADS-B signal received by the ground receiving device according to a preset frame format to obtain the original ADS-B message, decoding the original ADS-B message, and extracting the target location information.

[0022] Furthermore, the step of determining the authenticity of the ADS-B signal based on the convergence state of the trajectory prediction result includes: comparing the predicted trajectory result with the trajectory parsed from the original ADS-B message; if the predicted result deviates continuously from the trajectory point parsed from the original ADS-B message starting from the target point A, it is determined that the prediction result is not converged, and the ADS-B signal after the target point A is determined to be a false signal, wherein the original ADS-B message is parsed from the received ADS-B signal.

[0023] Furthermore, the second detection method determines the authenticity of the ADS-B signal by analyzing the changing time differences between the arrival times of multiple consecutive ADS-B signals at the ground receiving device.

[0024] Furthermore, if the time difference between multiple consecutive ADS-B signals arriving at the ground receiving device changes in an increasing trend or a decreasing trend, then the corresponding multiple ADS-B signals are determined to be real signals. If the time difference between multiple consecutive ADS-B signals arriving at the ground receiving device is equal or the difference is within a preset range, then the corresponding multiple ADS-B signals are determined to be false signals.

[0025] Furthermore, the first detection method specifically uses a Kalman filter algorithm to predict the trajectory of the received ADS-B signal.

[0026] Furthermore, the step of using the Kalman filter algorithm to predict the trajectory of the received ADS-B signal includes:

[0027] Multiple three-dimensional position and velocity information of the target are extracted from the received ADS-B signals and used as elements of the Kalman filter observation sequence.

[0028] Kalman filtering is performed on the input observation sequence. Initialization of the Kalman filter is achieved using the observations from the first two time steps, resulting in an initial estimate. This initial estimate is then fed into a one-step state prediction to obtain an estimated value. Measurement prediction is performed, and the innovation equation is calculated using the predicted value and the measurement equation. A one-step prediction matrix of the covariance is calculated, and the result is used to estimate the gain matrix of the Kalman filter. The state update equation is calculated based on the innovation equation and the gain matrix, ultimately yielding the predicted value. The estimated value is updated using the predicted value and the observed value to update the target's state information. This Kalman filtering process is repeated until the filtering is complete.

[0029] The target's state information, continuously updated during the Kalman filtering process, yields the target's trajectory tracking prediction results.

[0030] An ADS-B signal authenticity detection device, comprising:

[0031] The target location extraction module is used to acquire the ADS-B signal received by the ground receiving equipment and extract the corresponding target location information;

[0032] The judgment control module is used to determine whether the target position is located in an airspace that exceeds a preset height threshold based on the target position information. If so, it controls the first detection module to perform authenticity detection on the ADS-B signal; otherwise, it controls the second detection module to perform authenticity detection on the ADS-B signal.

[0033] The first detection module performs trajectory prediction on the received ADS-B signal and determines the authenticity of the ADS-B signal based on the convergence state of the trajectory prediction result. The second detection module uses a different detection method than the first detection module.

[0034] A computer device includes a processor and a memory, the memory being used to store a computer program, and the processor being used to execute the computer program to perform the method described above.

[0035] A computer-readable storage medium storing a computer program that, when executed, implements the method described above.

[0036] Compared with the prior art, the advantages of the present invention are as follows:

[0037] 1. This invention employs different detection methods for ADS-B spoofing types that may occur in different scenarios, so as to fully consider the characteristics of different ADS-B spoofing types and achieve authenticity detection, ensuring detection accuracy. For scenarios where the target position exceeds a preset altitude threshold in the airspace, the authenticity of the ADS-B signal is first determined by using trajectory prediction based on the ADS-B signal and then by judging the convergence state of the trajectory prediction results. This not only enables fast and accurate determination of ADS-B signal authenticity, but also eliminates the need for additional hardware equipment and changes to the ADS-B data format, effectively reducing implementation costs and complexity, and improving the adaptability and robustness of detection.

[0038] 2. This invention further improves the efficiency, accuracy and robustness of prediction by detecting the authenticity of ADS-B signals in the airspace of the flight path, using the Kalman filter algorithm to predict the trajectory, and then judging the authenticity by the convergence state of the prediction results.

[0039] 3. Furthermore, this invention uses the change relationship of arrival time difference of ADS-B signals received by the target within the airport airspace to determine the authenticity of the signal. It can make full use of the time difference characteristics of the target receiving ADS-B signals within the airport airspace, effectively reducing the complexity of detection. It only needs to parse and obtain the position information, speed information and arrival time of the signal received by the ground station contained in the message. It does not require large-scale modification of existing equipment and has good compatibility with existing equipment, which can greatly reduce the implementation cost. Attached Figure Description

[0040] Figure 1 This is a schematic diagram illustrating the implementation process of the ADS-B signal authenticity detection method in this embodiment.

[0041] Figure 2 This is a schematic diagram of the ADS-B frame format defined by the 1090ES data link in this embodiment.

[0042] Figure 3 This is a schematic diagram of the trajectory prediction process based on Kalman filtering in this embodiment.

[0043] Figure 4 This is a schematic diagram illustrating the comparison between the actual target track and the track formed by the location information of the false message in a specific application embodiment of the present invention.

[0044] Figure 5 This is a comparative schematic diagram of the ADS-B signal trajectory prediction results based on Kalman filtering obtained in a specific application embodiment of the present invention.

[0045] Figure 6 This is a schematic diagram illustrating the implementation process of ADS-B signal authenticity detection based on the time difference of arrival method in this embodiment. Detailed Implementation

[0046] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0047] When ADS-B is used as an independent surveillance system, there are two main methods of ADS-B target location spoofing: message tampering spoofing and target simulation spoofing. Message tampering spoofing (illegally altered information mid-flight) occurs when a jamming station receives ADS-B information from a real aircraft in the air, obtains the aircraft's address in real time, and then forges a fake message with the same address, altering some information before broadcasting it. The ADS-B ground station receives and processes both the parameters broadcast by the real target and the parameters of the fake signal broadcast by the jamming station, resulting in an abnormal flight track status output to the display terminal. This type of message tampering spoofing typically occurs in scenarios involving flight along flight paths. Target simulation spoofing (transmitting false flight information) refers to a jamming station forging ADS-B messages conforming to the protocol's format and broadcasting false location information. The ground station receives and parses the false information, displays it as normal data alongside or associates it with the real target in the air, and outputs it to the user terminal. This type of message tampering spoofing typically occurs in scenarios involving aircraft approaching and departing from airports (including approach and tower control phases).

[0048] This invention considers the different types of ADS-B spoofing that may occur in the aforementioned scenarios and employs different detection methods for ADS-B authenticity detection. This allows for the full consideration of the characteristics of different ADS-B spoofing types to achieve authenticity detection, ensuring detection accuracy. For scenarios where the target position exceeds a preset altitude threshold in the airspace (i.e., the airway airspace), the authenticity of the ADS-B signal is first determined by using trajectory prediction based on the ADS-B signal. The authenticity of the ADS-B signal is then judged based on the convergence state of the trajectory prediction results (i.e., the degree of matching between the prediction results and the actual ADS-B trajectory). This not only enables rapid and accurate determination of the authenticity of ADS-B signals tampered with by message, but also eliminates the need for additional hardware devices and changes to the ADS-B data format. This effectively reduces implementation costs and complexity, and significantly improves the adaptability and robustness of the detection.

[0049] like Figure 1 As shown, the specific steps of the ADS-B signal authenticity detection method in this embodiment include:

[0050] Step S01. Acquire the ADS-B signal received by the ground receiving equipment and extract the corresponding target location information;

[0051] Step S02. Determine whether the target location is located in an airspace exceeding a preset height threshold based on the target location information;

[0052] Step S03. If the target position exceeds the preset altitude threshold in the airspace, the first detection method is used to detect the authenticity of the ADS-B signal; otherwise, the second detection method is used to detect the authenticity of the ADS-B signal. The first detection method predicts the trajectory of the received ADS-B signal and determines the authenticity of the ADS-B signal based on the convergence state of the trajectory prediction result. The second detection method is a different detection method from the first detection method.

[0053] In this embodiment, step S01, extracting the corresponding target location information, includes: demodulating and decoding the ADS-B signal received by the ground receiving equipment according to a preset frame format (specifically, the frame format defined by the 1090ES data link) to obtain the corresponding message, i.e., obtaining the original ADS-B message; decoding the original ADS-B message to extract the target location information; and thus calculating the relative position relationship between the target and the ground station. Figure 2 As shown, the frame format defined by the 1090ES data link includes a header, downlink format, performance indicator, address field, message field, and checksum. The message field includes aircraft identification code, position information, positioning accuracy, altitude position information, track angle position information, ground speed, vertical speed, and emergency indication field.

[0054] In step S02 of this embodiment, if it is determined that the target location is in the airspace above the airport exceeding a preset altitude threshold (e.g., 6000 meters), i.e., the target is in the airway airspace, then the first detection method is used to predict the trajectory of the received ADS-B signal and determine the authenticity of the ADS-B signal based on the convergence state of the trajectory prediction result. If the target location is in the airspace between the airport and the preset altitude threshold, i.e., the target is in the airport airspace, then the second detection method is used to ensure that an appropriate detection method is used for different types of ADS-B signal deception, thereby ensuring the accuracy of the detection.

[0055] In this embodiment, the first detection method in step S03 specifically uses the Kalman filter algorithm to predict the trajectory of the received ADS-B signal. For example... Figure 3 As shown, the specific steps for predicting the trajectory using the Kalman filter algorithm on the received ADS-B signal include:

[0056] Multiple three-dimensional position and velocity information of the target are extracted from the received ADS-B signals and used as elements of the Kalman filter observation sequence.

[0057] Kalman filtering is performed on the input observation sequence. Initialization of the Kalman filter is achieved using the observations from the first two time steps, resulting in an initial estimate. This initial estimate is then fed into a one-step state prediction to obtain an estimated value. Measurement prediction is performed, and the innovation equation is calculated using the predicted value and the measurement equation. A one-step prediction matrix of the covariance is calculated, and the result is used to estimate the gain matrix of the Kalman filter. The state update equation is calculated based on the innovation equation and the gain matrix, ultimately yielding the predicted value. The estimated value is updated using the predicted value and the observed value to update the target's state information. This Kalman filtering process is repeated until the filtering is complete.

[0058] The target's state information, continuously updated during the Kalman filtering process, yields the target's trajectory tracking prediction results.

[0059] In this embodiment, determining the authenticity of the ADS-B signal based on the convergence state of the trajectory prediction result specifically includes: comparing the predicted trajectory result with the trajectory parsed from the original ADS-B message; if the predicted result continuously deviates from the trajectory point parsed from the original ADS-B message starting from target point A, the prediction result is determined to be non-converged, and the ADS-B signal after target point A is determined to be a false signal. The original ADS-B message is parsed from the received ADS-B signal. That is, based on several positional information from previous moments, a Kalman filter algorithm is used for trajectory prediction. If, starting from a certain point A, the prediction result continuously deviates from the trajectory point parsed from the actual received message, such as... Figure 4 As shown, the prediction result does not converge, and the ADS-B signal after point A is determined to be a false deception signal. For ADS-B signals in the airway airspace that are subject to message tampering deception, the predicted trajectory based on this ADS-B signal will deviate from the actual trajectory due to message tampering. This embodiment uses a Kalman filter algorithm to predict the trajectory of ADS-B signals in the airway airspace, and by analyzing the convergence state of the prediction results, it can fully utilize the characteristics of message tampering deception ADS-B signals to determine the authenticity of the ADS-B signal, while also exhibiting good adaptability and robustness.

[0060] In a specific application embodiment, the detailed steps for ADS-B signal authenticity detection based on Kalman filtering are as follows:

[0061] Step 301. Extract a series of three-dimensional position and velocity information of the target from several received ADS-B messages. The information corresponding to each message is used as an element of the Kalman filter observation sequence, where the three-dimensional position information is represented as follows: Speed ​​information is represented as ;

[0062] Step 302. Determine the system's state vector as follows: ,in The observation time is; the state equation is: ,in Here is the state transition matrix. The process noise distribution matrix is... For system process noise, With zero mean and variance matrix The Gaussian random sequence; the measurement equation is: ,in For the measurement matrix, With zero mean and covariance matrix The system uses Gaussian white noise. Initialization is performed using the observations from the first two time steps. The resulting initial estimate is fed into the first-step state prediction. After obtaining the estimate, measurement prediction is performed. The innovation equation is calculated using the measurement prediction and the measurement equation. Simultaneously, the first-step prediction matrix of the covariance is calculated, and the result is used to estimate the Kalman gain matrix. The state update equation is calculated using the innovation equation and the gain matrix. Finally, the estimated value is updated using the predicted value and the observation value. This filtering process is repeated until completion. Through this continuous calculation, the latest received measurement data is recursively filtered to continuously update the target's state information, thus achieving target trajectory tracking and prediction.

[0063] Step 303. Compare the track prediction result of the Kalman filter with the track obtained by parsing the received original ADS-B message. If the predicted value deviates from the parsed message track from a certain point A, it indicates that the prediction result does not converge. Then, the ADS-B messages after a certain point A are judged to be false messages and these original messages are discarded; otherwise, the ADS-B messages are judged to be real and valid.

[0064] In specific application examples, the ADS-B signal trajectory prediction results based on Kalman filtering are obtained, for example... Figure 5 As shown, by Figure 5 It can be seen that the Kalman filter predicts the trajectory and the target planned route changes in the same direction. However, after the tampering at point B, the trajectory of the false message will deviate from the Kalman filter predicts the trajectory after point B. Therefore, it can be determined that the signal received after point B is a false signal.

[0065] It is understandable that, in addition to the Kalman filter algorithm mentioned above, other filtering or prediction methods can be used to achieve trajectory prediction according to actual needs.

[0066] In this embodiment, the second detection method in step S03 is to determine the authenticity of the ADS-B signal by comparing the changing relationship of the time differences between the arrival times of multiple consecutive ADS-B signals at the ground receiving equipment. This is essentially a time difference-based method for detecting the authenticity of ADS-B signals. For a series of ADS-B signals received by the ground receiving equipment, since the distance between the aircraft and the ground station varies (generally tending to increase or decrease), the arrival delay of the transmitted ADS-B signals should also continuously increase or decrease. However, the jamming station emitting the deception signal is usually approximately stationary relative to the ground station, so the arrival time difference of its transmitted deception signal is equal at all times. This embodiment utilizes this characteristic to determine the authenticity of the received ADS-B signal by comparing the changing relationship of the arrival time differences of a series of received signals at the ground station. Specifically, if the time difference between multiple consecutive ADS-B signals arriving at the ground receiving device changes in an increasing or decreasing trend, then the corresponding multiple ADS-B signals are determined to be real signals. If the time difference between multiple consecutive ADS-B signals arriving at the ground receiving device is equal or the difference is within a preset range, then the corresponding multiple ADS-B signals are determined to be false signals.

[0067] In specific application embodiments, such as Figure 6 As shown, the detailed steps of the method for detecting the authenticity of ADS-B signals based on the time difference of arrival method are as follows:

[0068] a. Ground receiving equipment (such as a ground station) receives ADS-B messages from several targets within a time period T and measures the arrival time of each message. Assume that the received messages... Each message has an arrival time sequence recorded as follows: .

[0069] b. Calculate the time difference of arrival, i.e., calculate... The difference between adjacent elements in a sequence, denoted as the arrival time difference sequence. .according to Determine the relationship between the elements in the equation; if each element tends to increase or decrease, then determine the relationship. A single ADS-B message is considered valid; if all elements are equal or the difference is within a certain tolerance, then this message is considered valid. The ADS-B message is a spoof message.

[0070] This embodiment utilizes the time difference in ADS-B signal reception when the target is located within airport airspace to achieve accurate authenticity detection. The method is simple, requiring only the parsing of the message to obtain the location information, speed information, and arrival time of the signal received by the ground station. It does not require costly modifications to existing equipment and has good compatibility with existing equipment, which can greatly reduce implementation costs.

[0071] It is understandable that, in addition to the above-mentioned method based on the time difference of arrival, the second detection method can also adopt other detection methods according to actual needs, so as to form different combinations with the first detection method.

[0072] This embodiment extracts the target location from the ADS-B signal to determine the area where the target is located. If the target is located in the airway airspace, a Kalman filter-based method is used. This method uses Kalman filtering to predict whether the track converges to detect the target and utilizes the difference in the time difference between the arrival of real and false signals at the ground station. This ensures detection efficiency and accuracy while improving adaptability and robustness. If the target is located in the airport airspace, a time difference-based method is used. This method determines the authenticity of the ADS-B signal based on the changing relationship of the time differences between the arrival of multiple consecutive ADS-B signals at the ground receiving equipment. This method can efficiently and quickly achieve authenticity detection while reducing implementation costs and complexity.

[0073] The ADS-B signal authenticity detection device in this embodiment includes:

[0074] The target location extraction module is used to acquire the ADS-B signal received by the ground receiving equipment and extract the corresponding target location information;

[0075] The judgment control module is used to determine whether the target position is located in an airspace that exceeds a preset height threshold based on the target position information. If so, it controls the first detection module to perform authenticity detection on the ADS-B signal; otherwise, it controls the second detection module to perform authenticity detection on the ADS-B signal.

[0076] The first detection module performs trajectory prediction on the received ADS-B signal and determines the authenticity of the ADS-B signal based on the convergence state of the trajectory prediction results. The second detection module uses a different detection method than the first detection module.

[0077] The ADS-B signal authenticity detection device in this embodiment corresponds one-to-one with the ADS-B signal authenticity detection method described above. Specifically, the target location extraction module corresponds to step S01, the judgment control module corresponds to step S02, the first detection module corresponds to the first detection method in step S03, and the second detection module corresponds to the second detection method in step S03. These will not be described in detail here.

[0078] This embodiment also provides a computer device, including a processor and a memory, the memory for storing a computer program, and the processor for executing the computer program to perform the method as described above.

[0079] This embodiment also provides a computer-readable storage medium storing a computer program, which, when executed, implements the method described above.

[0080] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A method for detecting the authenticity of ADS-B signals, characterized in that the steps include... include: Acquire the ADS-B signal received by the ground receiving equipment and extract the corresponding target location information; Based on the target location information, determine whether the target location is located in an airspace exceeding a preset height threshold; If the target location exceeds the preset height threshold in the airspace, the first detection method is used to detect the authenticity of the ADS-B signal; otherwise, the second detection method is used to detect the authenticity of the ADS-B signal. The first detection method performs trajectory prediction on the received ADS-B signal and determines the authenticity of the ADS-B signal based on the convergence state of the trajectory prediction result. The second detection method is a different detection method from the first detection method.

2. The ADS-B signal authenticity detection method according to claim 1, characterized in that, The step of extracting the corresponding target location information includes: demodulating and decoding the ADS-B signal received by the ground receiving device according to a preset frame format to obtain the original ADS-B message, decoding the original ADS-B message, and extracting the target location information.

3. The ADS-B signal authenticity detection method according to claim 1, characterized in that, The method of determining the authenticity of the ADS-B signal based on the convergence state of the trajectory prediction result includes: comparing the predicted trajectory result with the trajectory parsed from the original ADS-B message; if the predicted result deviates from the trajectory point parsed from the original ADS-B message starting from the target point A, it is determined that the prediction result is not converged, and the ADS-B signal after the target point A is determined to be a false signal. The original ADS-B message is parsed from the received ADS-B signal.

4. The ADS-B signal authenticity detection method according to claim 1, characterized in that, The second detection method is to determine the authenticity of the ADS-B signal by analyzing the changing time difference between the arrival times of multiple consecutive ADS-B signals at the ground receiving device.

5. The ADS-B signal authenticity detection method according to claim 4, characterized in that, If the time difference between multiple consecutive ADS-B signals arriving at the ground receiving device changes in an increasing or decreasing trend, then the corresponding multiple ADS-B signals are determined to be real signals. If the time difference between multiple consecutive ADS-B signals arriving at the ground receiving device is equal or the difference is within a preset range, then the corresponding multiple ADS-B signals are determined to be false signals.

6. The ADS-B signal authenticity detection method according to any one of claims 1 to 5, characterized in that, The first detection method specifically uses the Kalman filter algorithm to predict the trajectory of the received ADS-B signal.

7. The ADS-B signal authenticity detection method according to claim 6, characterized in that, The process of predicting the trajectory using the Kalman filter algorithm on the received ADS-B signal includes: Multiple three-dimensional position and velocity information of the target are extracted from the received ADS-B signals and used as elements of the Kalman filter observation sequence. Kalman filtering is performed on the input observation sequence. Initialization of the Kalman filter is achieved using the observations from the first two time steps, resulting in an initial estimate. This initial estimate is then fed into a one-step state prediction to obtain an estimated value. Measurement prediction is performed, and the innovation equation is calculated using the predicted value and the measurement equation. A one-step prediction matrix of the covariance is calculated, and the result is used to estimate the gain matrix of the Kalman filter. The state update equation is calculated based on the innovation equation and the gain matrix, ultimately yielding the predicted value. The estimated value is updated using the predicted value and the observed value to update the target's state information. This Kalman filtering process is repeated until the filtering is complete. The target's state information, continuously updated during the Kalman filtering process, yields the target's trajectory tracking prediction results.

8. An ADS-B signal authenticity detection device, characterized in that, include: The target location extraction module is used to acquire the ADS-B signal received by the ground receiving equipment and extract the corresponding target location information; The judgment control module is used to determine whether the target position is located in an airspace that exceeds a preset height threshold based on the target position information. If so, it controls the first detection module to perform authenticity detection on the ADS-B signal; otherwise, it controls the second detection module to perform authenticity detection on the ADS-B signal. The first detection module performs trajectory prediction on the received ADS-B signal and determines the authenticity of the ADS-B signal based on the convergence state of the trajectory prediction result. The second detection module uses a different detection method than the first detection module.

9. A computer device comprising a processor and a memory, the memory being used to store a computer program, characterized in that, The processor is used to execute the computer program to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the method as described in any one of claims 1 to 7.

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