Identification method and device for satellite interference signal and storage medium

Abstract

The invention discloses an identification method and device for a satellite interference signal and a storage medium, and the method comprises the steps: collecting a first sample signal, carrying out the spectrum analysis of the first sample signal, and determining a first spectrum feature pair corresponding to the first sample signal; collecting a plurality of second sample signals, performing spectrum analysis on the plurality of second sample signals, and determining a plurality of second spectrum feature pairs corresponding to each second sample signal; comparing each second spectrum feature pair with the first spectrum feature pair, and determining a plurality of third spectrum feature pairs; training a pre-constructed signal identification model by using the plurality of third spectrum feature pairs; and receiving a to-be-detected signal, identifying the to-be-detected signal by using the signal identification model, and determining whether the to-be-detected signal contains an interference signal. Therefore, the technical effects of accurately identifying the interference signal and improving the detection rate of the interference signal are achieved.

Description

Technical Field

[0001] The present application relates to the field of satellite positioning technology, and in particular to a method, device and storage medium for identifying satellite interference signals. Background Art

[0002] With the continuous development of satellite positioning technology, satellites can provide continuous and high-precision location information to users around the world in real time, thus meeting the positioning needs of users. However, with the continuous development of interference technology, interference signals have put forward severe challenges to the positioning accuracy of satellite positioning technology.

[0003] Interference signals are a form of intelligent interference, which mainly send fake signals similar to real signals to the receiver of the terminal device, and then the receiver of the terminal device generates wrong position information, speed information and time information based on the fake signals, thus finally making wrong positioning.

[0004] Figure 1 This is a schematic diagram of how existing interference sources use interference signals to interfere with terminal equipment. Figure 1 As shown, the system includes: a satellite 10, a terminal device 20 and an interference source 30. The terminal device 20 is equipped with a receiver, and the receiver and the satellite 10 are connected to each other in communication, so that the terminal device 20 can receive the communication signal transmitted by the satellite 10 through the receiver. The terminal device 20 is also connected to the interference source 30 in communication through the receiver, so that the receiver of the terminal device 20 can receive the signal transmitted by the interference source 30. That is to say, when the receiver of the terminal device 20 receives the signal transmitted by the interference source, the terminal device will make an incorrect positioning.

[0005] At present, the identification technologies for interference signals mainly include the following: RF fingerprint recognition technology, deep learning classification algorithm, GPS interference detection and interference mitigation technology. Among them, RF fingerprint recognition technology refers to identifying the hardware characteristics of the transmitter by extracting unique features from satellite signals. Deep learning classification algorithms mainly identify the hardware of the transmitter by detecting and identifying unique features embedded in radio signals. GPS interference detection mainly identifies interference signals by analyzing the characteristics and parameters of the signal. Interference mitigation technology mainly protects the receiver to ensure that the receiver captures reliable data.

[0006] From the above content, it can be seen that the current interference signal recognition technology is mainly applied to hardware, but there is a lack of a method that can accurately identify interference signals and distinguish interference signals from real signals.

[0007] The publication number is CN115480268A, and the name is a method for identifying satellite navigation system deception interference signals in the frequency domain. It includes: based on the characteristics of the satellite navigation system frequency domain signal and the deception interference frequency domain signal in the frequency domain, combining signal enhancement and classification recognition algorithms to process the frequency domain signal, obtain a reconstructed feature signal, and thus determine whether it is a satellite navigation system deception interference signal.

[0008] The publication number is CN114201988A, and the name is satellite navigation composite interference signal identification method and system. The method includes: preprocessing the composite interference signal to be identified, and obtaining the multi-domain features of the preprocessed composite interference signal to be identified; inputting the obtained multi-domain features into a pre-trained deep learning neural network model based on their corresponding dimensions, inputting the one-dimensional sequence features into a one-dimensional sequence feature extraction module, and inputting the multi-dimensional sequence features into a multi-dimensional sequence feature extraction module, and then extracting domain features of different dimensions respectively; inputting the extracted domain features of different dimensions into the feature fusion layer and the fully connected layer of the pre-trained deep learning neural network model to obtain the classification and recognition results of the composite interference signal.

[0009] With respect to the technical problems existing in the above-mentioned prior art that interference signals cannot be accurately identified, the detection rate of interference signals cannot be improved, and the interference signals cannot be distinguished from real signals, no effective solution has been proposed so far. Summary of the invention

[0010] The embodiments of the present disclosure provide a method, device and storage medium for identifying satellite interference signals, so as to at least solve the technical problems existing in the prior art that interference signals cannot be accurately identified, the detection rate of interference signals cannot be improved, and the interference signals cannot be distinguished from real signals.

[0011] According to one aspect of an embodiment of the present disclosure, a method for identifying satellite interference signals is provided, including: collecting a first sample signal, and performing spectrum analysis on the first sample signal to determine a first spectrum feature pair corresponding to the first sample signal, wherein the first sample signal is used to indicate a real signal transmitted by the satellite; collecting multiple second sample signals, and performing spectrum analysis on the multiple second sample signals to determine multiple second spectrum feature pairs corresponding to each second sample signal, wherein the second sample signal is used to indicate a signal received by a receiver; comparing each second spectrum feature pair with the first spectrum feature pair, and determining multiple third spectrum feature pairs, wherein the third spectrum feature pairs correspond to interference signals; training a pre-constructed signal recognition model using the multiple third spectrum feature pairs; and receiving a signal to be detected, and identifying the signal to be detected using the signal recognition model to determine whether the signal to be detected contains an interference signal.

[0012] According to another aspect of an embodiment of the present disclosure, a storage medium is further provided, the storage medium including a stored program, wherein when the program is running, a processor executes any one of the methods described above.

[0013] According to another aspect of an embodiment of the present disclosure, there is also provided an identification device for satellite interference signals, including: a first signal acquisition module, used to acquire a first sample signal, and perform spectrum analysis on the first sample signal, so as to determine a first spectrum feature pair corresponding to the first sample signal, wherein the first sample signal is used to indicate a real signal transmitted by the satellite; a second signal acquisition module, used to acquire multiple second sample signals, and perform spectrum analysis on the multiple second sample signals, so as to determine multiple second spectrum feature pairs corresponding to each second sample signal, wherein the second sample signal is used to indicate a signal received by a receiver; a first feature comparison module, used to compare each second spectrum feature pair with the first spectrum feature pair, and determine multiple third spectrum feature pairs, wherein the third spectrum feature pairs correspond to interference signals; a model training module, used to train a pre-constructed signal recognition model using multiple third spectrum feature pairs; and a signal detection module, used to receive a signal to be detected, and identify the signal to be detected using the signal recognition model, so as to determine whether the signal to be detected contains an interference signal.

[0014] According to another aspect of an embodiment of the present disclosure, there is also provided an identification device for satellite interference signals, comprising: a processor; and a memory connected to the processor, for providing the processor with instructions for processing the following processing steps: collecting a first sample signal, and performing a spectrum analysis on the first sample signal, so as to determine a first spectrum feature pair corresponding to the first sample signal, wherein the first sample signal is used to indicate a real signal transmitted by the satellite; collecting multiple second sample signals, and performing a spectrum analysis on the multiple second sample signals, so as to determine multiple second spectrum feature pairs corresponding to each second sample signal, wherein the second sample signal is used to indicate a signal received by a receiver; comparing each second spectrum feature pair with the first spectrum feature pair, and determining multiple third spectrum feature pairs, wherein the third spectrum feature pairs correspond to interference signals; training a pre-constructed signal recognition model using the multiple third spectrum feature pairs; and receiving a signal to be detected, and identifying the signal to be detected using the signal recognition model, so as to determine whether the signal to be detected contains an interference signal.

[0015] The present application provides a method for identifying satellite interference signals. First, the processor collects a first sample signal and performs spectrum analysis on the first sample signal to determine a first spectrum feature pair corresponding to the first sample signal. Then, the processor collects multiple second sample signals and performs spectrum analysis on the multiple second sample signals to determine multiple second spectrum feature pairs corresponding to each second sample signal. Furthermore, the processor compares each second spectrum feature with the first spectrum feature pair and determines multiple third spectrum feature pairs. The processor then trains a pre-constructed signal recognition model using multiple third spectrum feature pairs. Finally, the processor receives a signal to be detected and uses the signal recognition model to identify the signal to be detected to determine whether the signal to be detected contains an interference signal.

[0016] With reference to the above contents, it can be known that, since the present application collects a first sample signal (used to indicate a real signal transmitted by a satellite) and performs spectrum analysis on the first sample signal, a first spectrum feature pair corresponding to the first sample signal can be determined. Furthermore, since the present application collects a plurality of second sample signals (i.e., including both real signals and interference signals) and performs spectrum analysis on the plurality of second sample signals, a second spectrum feature pair corresponding to the second sample signal can be determined.

[0017] Therefore, the multiple second spectrum feature pairs can be further compared with the first spectrum feature pair, and the multiple third spectrum features generated after the comparison can be used to train the signal recognition model, so as to use the trained signal recognition model to identify whether there is an interference signal in the signal to be detected.

[0018] Since the present application aims to distinguish and identify similar features (i.e., frequency features and amplitude features) between interference signals and real signals, and trains a signal recognition model based on the features of the interference signals (i.e., frequency features and amplitude features), the signal recognition model can accurately identify interference signals and improve the detection rate of interference signals.

[0019] This solves the technical problems in the prior art of being unable to accurately identify interference signals, improving the detection rate of interference signals, and distinguishing interference signals from real signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings: Figure 1 It is a schematic diagram of the existing interference source using interference signals to interfere with the positioning of terminal equipment; Figure 2Ais a schematic diagram of the hardware architecture of the satellite according to Embodiment 1 of the present application; Figure 2B is a schematic diagram of the hardware architecture of a receiver of a terminal device according to Embodiment 1 of the present application; Figure 3 is a schematic diagram of a satellite interference signal identification system according to Embodiment 1 of the present application; Figure 4 is a flowchart of a method for identifying satellite interference signals according to Embodiment 1 of the present application; Figure 5 is a schematic diagram of a signal recognition model according to Example 1 of the present application; Figure 6 is a schematic diagram of a device for identifying satellite interference signals according to Embodiment 2 of the present application; and Figure 7 It is a schematic diagram of a device for identifying satellite interference signals according to Example 3 of the present application. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0023] Example 1 According to this embodiment, an embodiment of a method for identifying satellite interference signals is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0024] Figure 2A 1 is a schematic diagram of the hardware architecture of the satellite 10 according to Embodiment 1 of the present application. Figure 2A As shown, the satellite 10 includes an integrated electronic system, which includes: a processor, a memory, a bus management module and a communication interface. The memory is connected to the processor, so that the processor can access the memory, read the program instructions stored in the memory, read data from the memory or write data to the memory. The bus management module is connected to the processor and is also connected to a bus such as a CAN bus. Therefore, the processor can communicate with the satellite-borne peripherals connected to the bus through the bus managed by the bus management module. In addition, the processor is also connected to communication devices such as cameras, star sensors, measurement and control transponders, and data transmission equipment via the communication interface. It can be understood by those skilled in the art that Figure 2A The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 2A More or fewer components as shown, or with Figure 2A Different configurations are shown.

[0025] Figure 2B is a schematic diagram of the hardware architecture of the receiver of the terminal device 20 according to Embodiment 1 of the present application. Figure 2B As shown, the receiver of the terminal device 20 may include one or more processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, a transmission device for communication functions, and an input / output interface. The memory, the transmission device, and the input / output interface are connected to the processor via a bus. In addition, it may also include: a display, a keyboard, and a cursor control device connected to the input / output interface. A person skilled in the art can understand that Figure 2B The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 2B More or fewer components as shown, or with Figure 2B Different configurations are shown.

[0026] It should be noted that Figure 2A and Figure 2BThe one or more processors and / or other data processing circuits shown in the figure may generally be referred to as "data processing circuitry" herein. The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other elements in the computing device. As involved in the embodiments of the present disclosure, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0027] Figure 2A and Figure 2B The memory shown in the figure can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for identifying satellite interference signals in the embodiment of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the method for identifying satellite interference signals of the above-mentioned application program is realized. The memory may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.

[0028] It should be noted that, in some optional embodiments, the above Figure 2A and Figure 2B The devices shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. Figure 2A and Figure 2B This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the above described apparatus.

[0029] Figure 3 is a schematic diagram of a satellite interference signal identification system according to an embodiment of the present application. Figure 3 As shown, the system includes: a signal acquisition module, a spectrum analysis module, a comparison module, a model training module and a recognition module.

[0030] Among them, the signal acquisition module is connected in communication with the spectrum analysis module, and is used to collect the first sample signal and the second sample signal, and send the first sample signal and the second sample signal to the spectrum analysis module. The spectrum analysis module is connected in communication with the comparison module, and is used to perform spectrum analysis on the first sample signal and determine the first spectrum feature pair; it is also used to perform spectrum analysis on the second sample signal and determine the second spectrum feature pair. Among them, the first spectrum feature pair and the second spectrum feature pair are composed of frequency and amplitude. The comparison module is connected in communication with the model training module, and is used to compare each second spectrum feature pair with the first spectrum feature, and determine multiple third spectrum feature pairs. The model training module is connected in communication with the recognition module, and is used to pre-build a signal recognition model, and train the signal recognition model using multiple third spectrum feature pairs. The recognition module is connected in communication with the signal acquisition module through the spectrum analysis module and the comparison module, and is used to receive the signal to be detected, call and use the signal recognition model to identify the signal to be detected, so as to determine whether the signal to be detected contains an interference signal.

[0031] Under the above operating environment, according to the first aspect of this embodiment, a method for identifying satellite interference signals is provided. The method comprises: Figure 3 The system implementation shown in . Figure 4 A schematic diagram showing the process of the method is shown in FIG. Figure 4 As shown, the method includes: S402: collecting a first sample signal and performing spectrum analysis on the first sample signal to determine a first spectrum feature pair corresponding to the first sample signal, wherein the first sample signal is used to indicate a real signal transmitted by a satellite; S404: collecting a plurality of second sample signals, and performing spectrum analysis on the plurality of second sample signals, so as to determine a plurality of second spectrum feature pairs corresponding to the respective second sample signals, wherein the second sample signals are used to indicate signals received by the receiver; S406: Compare each second spectrum feature pair with the first spectrum feature pair, and determine a plurality of third spectrum feature pairs, wherein the third spectrum feature pairs correspond to interference signals; S408: Training a pre-built signal recognition model using a plurality of third spectrum feature pairs; and S410: receiving a signal to be detected, and identifying the signal to be detected using a signal recognition model, thereby determining whether the signal to be detected contains an interference signal.

[0032] Specifically, first, the signal acquisition module acquires the first sample signal sent by the satellite 10, and sends the first sample signal to the spectrum analysis module. Figure 1As shown, the first sample signal is a real signal transmitted by the satellite 10, and it is worth noting that the real signal does not contain the interference signal transmitted by the interference source 30. Then the spectrum analysis module performs spectrum analysis on the first sample signal and determines a first spectrum feature pair corresponding to the first sample signal (S402). In this embodiment, the spectrum analysis module can, for example, perform Fourier transform on the first sample signal to obtain a first spectrum feature pair corresponding to the first sample signal in the frequency domain representation. For example, the first sample signal is , then with the first sample signal The corresponding first spectral feature pair is .in, Indicates that the first sample signal The corresponding frequency, Indicates that the first sample signal The corresponding amplitude.

[0033] At the same time, the signal acquisition module acquires a plurality of second sample signals and sends the plurality of second sample signals to the spectrum analysis module. Figure 1 As shown, the second sample signal is a signal received by the receiver of the terminal device 20. Then, the spectrum analysis module performs spectrum analysis on the plurality of second sample signals, and determines a plurality of second spectrum feature pairs corresponding to the plurality of second sample signals (S404). In this embodiment, the spectrum analysis module may, for example, perform Fourier transform on the plurality of second sample signals, thereby obtaining a plurality of second spectrum feature pairs corresponding to each second sample signal in a frequency domain representation.

[0034] For example, the second sample signals are . Then with the second sample signal The corresponding second spectral feature pair is ,in Indicates that the second sample signal The corresponding frequency, Indicates that the second sample signal The corresponding amplitude; and the second sample signal The corresponding second spectral feature pair is ,in Indicates that the second sample signal The corresponding frequency, Indicates that the second sample signal The corresponding amplitude; and the second sample signal The corresponding second spectral feature pair is ,in Indicates that the second sample signal The corresponding frequency, Indicates that the second sample signal The corresponding amplitude; and so on; with the second sample signal The corresponding second spectral feature pair is ,in Indicates that the second sample signal The corresponding frequency, Indicates that the second sample signal The corresponding amplitude.

[0035] Afterwards, the spectrum analysis module sends the first spectrum feature pair corresponding to the first sample signal and the multiple second spectrum feature pairs corresponding to the multiple second sample signals to the comparison module. Thus, the comparison module compares each second spectrum feature pair with the first spectrum feature pair, and determines multiple third spectrum feature pairs (S406). Referring to the above content, it can be seen that since the signal acquisition module also acquires multiple second sample signals, and the second sample signal corresponds to the signal received by the receiver, the signal is a superimposed signal, and the signal should contain a real signal and an interference signal. Among them, the real signal is the signal corresponding to the satellite 10, and the interference signal is the signal corresponding to the interference source 30.

[0036] Therefore, when the signal acquisition module acquires multiple second sample signals, it is necessary to remove the real signals from the multiple second sample signals and retain only the interference signals to ensure that the signal recognition model can be obtained through subsequent training.

[0037] For example, the comparison module receives each second spectrum feature pair , and the first spectral feature pair In the case of Subtract the first spectrum feature from the first spectrum feature, and determine a plurality of third spectrum feature pairs .

[0038] Among them, with the second sample signal The corresponding calculation formula for the third spectrum feature pair is as follows:

[0039] in, Indicates that the second sample signal The frequency corresponding to the interference signal in Indicates that the second sample signal The corresponding frequency, Indicates that the first sample signal The corresponding frequency.

[0040]

[0041] in, Indicates that the second sample signal The amplitude of the interference signal in Indicates that the second sample signal The corresponding amplitude, Indicates that the first sample signal The corresponding amplitude.

[0042] With the second sample signal The corresponding calculation formula for the third spectrum feature pair is as follows:

[0043] in, Indicates that the second sample signal The frequency corresponding to the interference signal in Indicates that the second sample signal The corresponding frequency, Indicates that the first sample signal The corresponding frequency.

[0044]

[0045] in, Indicates that the second sample signal The amplitude of the interference signal in Indicates that the second sample signal The corresponding amplitude, Indicates that the first sample signal The corresponding amplitude.

[0046] And so on.

[0047] With the second sample signal The corresponding calculation formula for the third spectrum feature pair is as follows:

[0048] in, Indicates that the second sample signal The frequency corresponding to the interference signal in Indicates that the second sample signal The corresponding frequency, Indicates that the first sample signal The corresponding frequency.

[0049]

[0050] in, Indicates that the second sample signal The amplitude of the interference signal in Indicates that the second sample signal The corresponding amplitude, Indicates that the first sample signal The corresponding amplitude.

[0051] When the comparison module determines multiple third spectrum feature pairs, the comparison module sends the multiple third spectrum feature pairs to the model training module. Thus, the model training module can train the pre-built signal recognition model using the multiple third spectrum feature pairs (S408). The signal recognition model pre-built by the model training module includes an RNN model, a fully connected layer, and a softmax classifier.

[0052] Therefore, it can be known from the above content that since multiple third spectrum feature pairs correspond to interference signals, after the signal recognition model is trained using multiple third spectrum feature pairs, the signal recognition model can recognize whether the signal to be detected contains interference signals.

[0053] Finally, when the signal acquisition module acquires the signal to be detected, the signal to be detected is sent to the spectrum analysis module, and the spectrum analysis module is used to perform spectrum analysis on the signal to be detected, so as to determine the fourth spectrum feature pair. Then, the comparison module compares the fourth spectrum feature pair corresponding to the signal to be detected with the first spectrum feature pair, and determines the fifth spectrum feature pair. Finally, the recognition module calls the signal recognition model to recognize the fifth spectrum feature pair, and determines whether the signal to be detected contains an interference signal (S410).

[0054] Furthermore, when the identification module identifies that the signal to be detected contains an interference signal, an early warning is issued to the terminal device 20, thereby ensuring that the terminal device 20 does not use the signal to be detected containing the interference signal for navigation and positioning.

[0055] As described in the background technology, the current interference signal recognition technology is mainly applied to hardware, but there is a lack of a method that can accurately identify the interference signal and distinguish the interference signal from the real signal.

[0056] In view of this, the present application provides a method for identifying satellite interference signals. Referring to the above-mentioned contents, it can be seen that since the present application collects a first sample signal (used to indicate a real signal transmitted by a satellite) and performs spectrum analysis on the first sample signal, a first spectrum feature pair corresponding to the first sample signal can be determined. Furthermore, since the present application collects multiple second sample signals (i.e., including both real signals and interference signals) and performs spectrum analysis on the multiple second sample signals, a second spectrum feature pair corresponding to the second sample signal can be determined.

[0057] Therefore, the multiple second spectrum feature pairs can be further compared with the first spectrum feature pair, and the multiple third spectrum features generated after the comparison can be used to train the signal recognition model, so as to use the trained signal recognition model to identify whether there is an interference signal in the signal to be detected.

[0058] Since the present application aims to distinguish and identify similar features (i.e., frequency features and amplitude features) between interference signals and real signals, and trains a signal recognition model based on the features of the interference signals (i.e., frequency features and amplitude features), the signal recognition model can accurately identify interference signals and improve the detection rate of interference signals.

[0059] This solves the technical problems in the prior art of being unable to accurately identify interference signals, improving the detection rate of interference signals, and distinguishing interference signals from real signals.

[0060] Optionally, the operation of receiving a signal to be detected and identifying the signal to be detected using a signal recognition model to determine whether the signal to be detected contains an interference signal includes: receiving the signal to be detected and performing spectral analysis on the signal to be detected to determine a fourth spectral feature pair corresponding to the signal to be detected; comparing the fourth spectral feature pair with the first spectral feature pair to determine a fifth spectral feature pair; and inputting the fifth spectral feature pair into the signal recognition model to determine whether the signal to be detected contains an interference signal.

[0061] Specifically, refer to Figure 3 As shown, the signal acquisition module receives the signal to be detected. The signal to be detected includes a real signal and a non-real signal. The non-real signal can be an interference signal or other noise signal that has no positioning effect.

[0062] Thus, first, the spectrum analysis module performs spectrum analysis on the signal to be detected and determines a fourth spectrum feature pair corresponding to the signal to be detected. . Then the signal to be detected The corresponding fourth spectral feature pair is ,in Indicates the signal to be detected The corresponding frequency, Indicates the signal to be detected The corresponding amplitude.

[0063] Then, the comparison module compares the fourth spectrum feature pair with the first spectrum analyzer feature pair and determines the fifth spectrum feature pair. In the case of With the first spectrum feature Subtract and determine the fifth spectral feature pair .

[0064] Among them, The corresponding calculation formula for the fifth spectrum feature pair is as follows:

[0065] in, Indicates the signal to be detected The frequency corresponding to the non-real signal in Indicates the signal to be detected The corresponding frequency, Indicates that the first sample signal The corresponding frequency.

[0066]

[0067] in, Indicates the signal to be detected The amplitude corresponding to the non-real signal in is Indicates the signal to be detected The corresponding amplitude, Indicates that the first sample signal The corresponding amplitude.

[0068] Therefore, the comparison module can determine the signal to be detected based on the above operation. The fifth characteristic pair corresponding to the interference signal in .

[0069] It is worth noting that although the signal to be detected contains real signals and unreal signals, the unreal signals in the signal to be detected do not necessarily affect the navigation positioning of the terminal device 20. And in the case where the unreal signal does not affect the navigation positioning of the terminal device 20, in order to improve the efficiency of navigation positioning, the unreal signal may not be eliminated. Therefore, it is necessary to identify the unreal signal and determine whether the unreal signal will affect the navigation positioning of the terminal device 20. That is, whether the unreal signal is an interference signal.

[0070] Finally, the comparison module sends the fifth spectrum feature corresponding to the non-real signal in the signal to be detected to the recognition module. The recognition module calls the signal recognition model, uses the signal recognition model and determines whether the signal to be detected contains an interference signal according to the fifth spectrum analyzer feature. The above content will be described in detail later, so it will not be repeated here.

[0071] Optionally, the signal recognition model includes an RNN model, a fully connected layer and a softmax classifier, and the fifth spectral feature pair is input into the signal recognition model, and the operation of determining whether the signal to be detected contains an interference signal includes: inputting the fifth spectral feature pair into the RNN model and the fully connected layer, thereby outputting a feature vector corresponding to the fifth spectral feature pair; inputting the feature vector into the softmax classifier, and outputting the corresponding first probability value and second probability value, wherein the first probability value is used to indicate that it is an interference signal, and the second probability value is used to indicate that it is not an interference signal; and when the first probability value is greater than a pre-set probability threshold, it indicates that the signal to be detected corresponding to the fifth spectral feature pair is an interference signal.

[0072] Specifically, Figure 5 is a schematic diagram of a signal recognition model according to an embodiment of the present application. Figure 5 As shown, the signal recognition model includes an RNN model, a fully connected layer, and a softmax classifier. And when the recognition module calls and uses the signal recognition model to recognize the fifth spectrum feature pair, the recognition module first inputs the fifth spectrum feature pair into the RNN model and the fully connected layer, thereby outputting a feature vector corresponding to the fifth spectrum feature pair. Among them, the feature vector corresponding to the fifth spectrum feature pair includes a feature vector for indicating an interference signal and a feature vector for indicating that it is not an interference signal.

[0073] Further, the fully connected layer inputs the feature vector into the softmax classifier, so that the softmax classifier outputs the corresponding first probability value and second probability value based on the feature vector. For example, the softmax classifier outputs the first probability value and the second probability value Among them, the first probability value Used to indicate that it is an interference signal, the second probability value Used to indicate that it is not an interference signal.

[0074] And at the first probability value Greater than a preset probability threshold In the case of , it indicates that the non-real signal in the to-be-detected signal corresponding to the fifth frequency spectrum feature pair is an interference signal.

[0075] Therefore, when the receiver of the terminal device 20 determines that the signal to be detected contains an interference signal, the terminal device 20 can be alerted in time, thereby ensuring that the terminal device 20 will not use the signal to be detected containing the interference signal for navigation and positioning, and ultimately determine erroneous location information.

[0076] Optionally, the operation of training a pre-built signal recognition model using multiple third spectral feature pairs includes: generating a corresponding multiple sixth spectral feature pairs based on each third spectral feature pair, wherein the sixth spectral feature pairs include the third spectral feature pairs and the corresponding interference signal labels; generating a corresponding multiple spectral feature sequences based on the multiple sixth spectral feature pairs; and inputting the multiple spectral feature sequences into the signal recognition model and training the signal recognition model.

[0077] Specifically, before the recognition module calls the signal recognition model and recognizes the signal to be detected, the signal recognition model needs to be trained in advance. Thus, first, when the comparison module compares the first spectrum feature pairs with the second spectrum feature pairs and generates the third spectrum feature pairs, the corresponding sixth spectrum feature pairs are generated based on the third spectrum feature pairs. For example, the third spectrum feature pairs The corresponding interference signal label is , so the corresponding sixth spectral feature pair is ; The third spectral feature pair The corresponding interference signal label is , so the corresponding sixth spectral feature pair is ; The third spectral feature pair The corresponding interference signal label is , so the corresponding sixth spectral feature pair is ; And so on; The third spectrum feature pair The corresponding interference signal label is , so the corresponding sixth spectral feature pair is .

[0078] Further, the model training module generates a plurality of spectrum feature sequences based on a plurality of sixth spectrum feature pairs. Corresponding to the spectral feature sequence ; and the sixth spectrum feature Corresponding to the spectral feature sequence ; And so on; with the sixth spectrum feature Corresponding to the spectral feature sequence . Where t=m / 3.

[0079] Then the model training module inputs multiple spectral feature sequences into the signal recognition model and trains the signal recognition model. Input into a pre-built signal recognition model and train the signal recognition model.

[0080] Thus, by using multiple frequency spectrum feature sequences to train the signal recognition model, a technical effect is achieved that can provide the necessary basis for the subsequent accurate determination of whether there is an interference signal in the signal to be predicted.

[0081] Therefore, according to the first aspect of this embodiment, the technical effect of being able to accurately identify interference signals and improving the detection rate of interference signals is achieved.

[0082] In addition, reference Figure 1 As shown, according to the second aspect of this embodiment, a storage medium is provided, wherein the storage medium includes a stored program, wherein when the program is run, a processor executes any one of the above methods.

[0083] Therefore, according to this embodiment, the technical effect of being able to accurately identify interference signals and improving the detection rate of interference signals is achieved.

[0084] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0085] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0086] Example 2 Figure 6 FIG. 6 shows a device 600 for identifying satellite interference signals according to this embodiment, and the device 600 corresponds to the method according to embodiment 1. Figure 6As shown, the device 600 includes: a first signal acquisition module 610, which is used to acquire a first sample signal and perform spectrum analysis on the first sample signal to determine a first spectrum feature pair corresponding to the first sample signal, wherein the first sample signal is used to indicate a real signal transmitted by a satellite; a second signal acquisition module 620, which is used to acquire multiple second sample signals and perform spectrum analysis on the multiple second sample signals to determine multiple second spectrum feature pairs corresponding to each second sample signal, wherein the second sample signal is used to indicate a signal received by a receiver; a first feature comparison module 630, which is used to compare each second spectrum feature pair with the first spectrum feature pair and determine multiple third spectrum feature pairs, wherein the third spectrum feature pair corresponds to an interference signal; a model training module 640, which is used to train a pre-constructed signal recognition model using multiple third spectrum feature pairs; and a signal detection module 650, which is used to receive a signal to be detected and identify the signal to be detected using a signal recognition model to determine whether the signal to be detected contains an interference signal.

[0087] Optionally, the signal detection module 650 includes: a spectrum analysis module, used to receive the signal to be detected and perform spectrum analysis on the signal to be detected, so as to determine a fourth spectrum feature pair corresponding to the signal to be detected; a third feature comparison module, used to compare the fourth spectrum feature pair with the first spectrum feature pair, and determine the fifth spectrum feature pair; and a signal detection submodule, used to input the fifth spectrum feature pair into the signal recognition model, and determine whether the signal to be detected contains an interference signal.

[0088] Optionally, the signal recognition model includes an RNN model, a fully connected layer and a softmax classifier, and the signal detection submodule includes: a feature vector output module, used to input the fifth spectrum feature pair into the RNN model and the fully connected layer, thereby outputting a feature vector corresponding to the fifth spectrum feature pair; a probability value output module, used to input the feature vector into the softmax classifier and output the corresponding first probability value and second probability value, wherein the first probability value is used to indicate that it is an interference signal, and the second probability value is used to indicate that it is not an interference signal; and an interference signal determination module, used to indicate that the signal to be detected corresponding to the fifth spectrum feature pair is an interference signal when the first probability value is greater than a preset probability threshold.

[0089] Optionally, the model training module 640 includes: a spectrum feature pair generation module, used to generate a corresponding plurality of sixth spectrum feature pairs based on each third spectrum feature pair, wherein the sixth spectrum feature pairs include the third spectrum feature pairs and the corresponding interference signal labels; a spectrum feature sequence generation module, used to generate a corresponding plurality of spectrum feature sequences based on the plurality of sixth spectrum feature pairs; and a model training submodule, used to input the plurality of spectrum feature sequences into the signal recognition model and train the signal recognition model.

[0090] Therefore, according to this embodiment, the technical effect of being able to accurately identify interference signals and improving the detection rate of interference signals is achieved.

[0091] Example 3 Figure 7 FIG. 7 shows a satellite interference signal identification device 700 according to the first aspect of this embodiment, and the device 700 corresponds to the method according to Embodiment 1. Figure 7 As shown, the device 700 includes: a processor 710; and a memory 720, which is connected to the processor 710 and is used to provide the processor 710 with instructions for processing the following processing steps: collecting a first sample signal, and performing a spectrum analysis on the first sample signal, so as to determine a first spectrum feature pair corresponding to the first sample signal, wherein the first sample signal is used to indicate a real signal transmitted by a satellite; collecting multiple second sample signals, and performing a spectrum analysis on the multiple second sample signals, so as to determine multiple second spectrum feature pairs corresponding to each second sample signal, wherein the second sample signal is used to indicate a signal received by a receiver; comparing each second spectrum feature pair with the first spectrum feature pair, and determining multiple third spectrum feature pairs, wherein the third spectrum feature pairs correspond to interference signals; using multiple third spectrum feature pairs to train a pre-constructed signal recognition model; and receiving a signal to be detected, and using the signal recognition model to identify the signal to be detected, so as to determine whether the signal to be detected contains an interference signal.

[0092] Optionally, the operation of receiving a signal to be detected and identifying the signal to be detected using a signal recognition model to determine whether the signal to be detected contains an interference signal includes: receiving the signal to be detected and performing spectral analysis on the signal to be detected to determine a fourth spectral feature pair corresponding to the signal to be detected; comparing the fourth spectral feature pair with the first spectral feature pair to determine a fifth spectral feature pair; and inputting the fifth spectral feature pair into the signal recognition model to determine whether the signal to be detected contains an interference signal.

[0093] Optionally, the signal recognition model includes an RNN model, a fully connected layer and a softmax classifier, and the fifth spectral feature pair is input into the signal recognition model, and the operation of determining whether the signal to be detected contains an interference signal includes: inputting the fifth spectral feature pair into the RNN model and the fully connected layer, thereby outputting a feature vector corresponding to the fifth spectral feature pair; inputting the feature vector into the softmax classifier, and outputting the corresponding first probability value and second probability value, wherein the first probability value is used to indicate that it is an interference signal, and the second probability value is used to indicate that it is not an interference signal; and when the first probability value is greater than a pre-set probability threshold, it indicates that the signal to be detected corresponding to the fifth spectral feature pair is an interference signal.

[0094] Optionally, the operation of training a pre-built signal recognition model using multiple third spectral feature pairs includes: generating a corresponding multiple sixth spectral feature pairs based on each third spectral feature pair, wherein the sixth spectral feature pairs include the third spectral feature pairs and the corresponding interference signal labels; generating a corresponding multiple spectral feature sequences based on the multiple sixth spectral feature pairs; and inputting the multiple spectral feature sequences into the signal recognition model and training the signal recognition model.

[0095] Therefore, according to this embodiment, the technical effect of being able to accurately identify interference signals and improving the detection rate of interference signals is achieved.

[0096] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0097] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0099] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0102] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for identifying satellite interference signals, characterized in that: include: Collecting a first sample signal and performing spectrum analysis on the first sample signal to determine a first spectrum feature pair corresponding to the first sample signal, wherein the first sample signal is used to indicate a real signal transmitted by a satellite; Collecting a plurality of second sample signals and performing spectrum analysis on the plurality of second sample signals, thereby determining a plurality of second spectrum feature pairs corresponding to each second sample signal, wherein the second sample signal is used to indicate a signal received by a receiver; Comparing each second spectrum feature pair with the first spectrum feature pair, and determining a plurality of third spectrum feature pairs, wherein the third spectrum feature pairs correspond to interference signals; Using the plurality of third frequency spectrum features to train a pre-built signal recognition model; as well as A signal to be detected is received, and the signal to be detected is identified using the signal identification model, so as to determine whether the signal to be detected contains an interference signal.

2. The method according to claim 1, characterized in that The operation of receiving a signal to be detected and identifying the signal to be detected by using the signal recognition model to determine whether the signal to be detected contains an interference signal includes: receiving the signal to be detected, and performing spectrum analysis on the signal to be detected, so as to determine a fourth spectrum feature pair corresponding to the signal to be detected; comparing the fourth spectral feature pair with the first spectral feature pair, and determining a fifth spectral feature pair; and The fifth frequency spectrum feature pair is input into the signal recognition model, and it is determined whether the signal to be detected includes an interference signal.

3. The method according to claim 2, characterized in that The signal recognition model includes an RNN model, a fully connected layer, and a softmax classifier, and the fifth spectrum feature pair is input into the signal recognition model, and the operation of determining whether the signal to be detected contains an interference signal includes: Inputting the fifth spectral feature pair into the RNN model and the fully connected layer, thereby outputting a feature vector corresponding to the fifth spectral feature pair; Inputting the feature vector into the softmax classifier, and outputting a corresponding first probability value and a second probability value, wherein the first probability value is used to indicate that it is the interference signal, and the second probability value is used to indicate that it is not the interference signal; and When the first probability value is greater than a preset probability threshold, it indicates that the signal to be detected corresponding to the fifth frequency spectrum feature pair is the interference signal.

4. The method according to claim 3, characterized in that The operation of training a pre-built signal recognition model using the plurality of third spectrum features comprises: Based on each third spectrum feature pair, generating a corresponding plurality of sixth spectrum feature pairs, wherein the sixth spectrum feature pairs include the third spectrum feature pairs and corresponding interference signal labels; Based on the plurality of sixth frequency spectrum feature pairs, generating a corresponding plurality of frequency spectrum feature sequences; and The multiple frequency spectrum feature sequences are input into the signal recognition model, and the signal recognition model is trained.

5. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the processor executes the method according to any one of claims 1 to 4.

6. A device for identifying satellite interference signals, characterized in that: include: a first signal acquisition module, configured to acquire a first sample signal and perform spectrum analysis on the first sample signal, thereby determining a first spectrum feature pair corresponding to the first sample signal, wherein the first sample signal is used to indicate a real signal transmitted by a satellite; A second signal acquisition module, used for acquiring a plurality of second sample signals, and performing spectrum analysis on the plurality of second sample signals, so as to determine a plurality of second spectrum feature pairs corresponding to each second sample signal, wherein the second sample signal is used for indicating a signal received by the receiver; A first feature comparison module, used for comparing each second spectrum feature pair with the first spectrum feature pair, and determining a plurality of third spectrum feature pairs, wherein the third spectrum feature pairs correspond to interference signals; A model training module, used for training a pre-built signal recognition model using the plurality of third spectrum feature pairs; as well as The signal detection module is used to receive the signal to be detected and identify the signal to be detected using the signal recognition model, so as to determine whether the signal to be detected contains an interference signal.

7. The device according to claim 6, characterized in that The signal detection module comprises: A spectrum analysis module, used for receiving the signal to be detected, and performing spectrum analysis on the signal to be detected, so as to determine a fourth spectrum feature pair corresponding to the signal to be detected; a third feature comparison module, configured to compare the fourth spectrum feature pair with the first spectrum feature pair, and determine a fifth spectrum feature pair; and The signal detection submodule is used to input the fifth frequency spectrum feature pair into the signal recognition model and determine whether the signal to be detected contains an interference signal.

8. The device according to claim 7, characterized in that The signal recognition model includes an RNN model, a fully connected layer and a softmax classifier, and the signal detection submodule includes: A feature vector output module, used for inputting the fifth spectrum feature pair into the RNN model and the fully connected layer, thereby outputting a feature vector corresponding to the fifth spectrum feature pair; a probability value output module, configured to input the feature vector into the softmax classifier, and output a corresponding first probability value and a second probability value, wherein the first probability value is used to indicate that it is the interference signal, and the second probability value is used to indicate that it is not the interference signal; and The interference signal determination module is used to indicate that the signal to be detected corresponding to the fifth frequency spectrum feature pair is the interference signal when the first probability value is greater than a preset probability threshold.

9. The device according to claim 8, characterized in that The model training module includes: A spectrum feature pair generating module, configured to generate a corresponding plurality of sixth spectrum feature pairs based on each third spectrum feature pair, wherein the sixth spectrum feature pairs include the third spectrum feature pairs and corresponding interference signal labels; a spectrum feature sequence generating module, configured to generate a corresponding plurality of spectrum feature sequences based on the plurality of sixth spectrum feature pairs; and The model training submodule is used to input the multiple frequency spectrum feature sequences into the signal recognition model and train the signal recognition model.

10. A device for identifying satellite interference signals, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Collecting a first sample signal and performing spectrum analysis on the first sample signal to determine a first spectrum feature pair corresponding to the first sample signal, wherein the first sample signal is used to indicate a real signal transmitted by a satellite; Collecting a plurality of second sample signals and performing spectrum analysis on the plurality of second sample signals, thereby determining a plurality of second spectrum feature pairs corresponding to each second sample signal, wherein the second sample signal is used to indicate a signal received by a receiver; Comparing each second spectrum feature pair with the first spectrum feature pair, and determining a plurality of third spectrum feature pairs, wherein the third spectrum feature pairs correspond to interference signals; Using the plurality of third frequency spectrum features to train a pre-built signal recognition model; as well as A signal to be detected is received, and the signal to be detected is identified using the signal identification model, so as to determine whether the signal to be detected contains an interference signal.

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