Improved signal deception detection method and system based on PCS algorithm and Ratio algorithm

Abstract

The invention discloses a PCS algorithm and Ratio algorithm-based improved signal deception detection method and system. The method comprises the following steps: acquiring an in-phase branch output result and an orthogonal branch output result obtained by capturing and tracking satellite signal data; constructing an algorithm detection quantity by using the in-phase branch output result and the orthogonal branch output result; and obtaining a detection threshold, and carrying out signal spoofing detection on the algorithm detection quantity by using the detection threshold. According to the scheme provided by the invention, deception detection of different scenes can be realized, and better detectability and universality are achieved.

Description

Technical Field

[0001] The present invention relates to the field of information science and technology, and in particular to an improved signal deception detection method and system based on a PCS algorithm and a Ratio algorithm. Background Art

[0002] The global navigation satellite system (GNSS) uses satellite broadcasts of pseudo-range, ephemeris, and signal transmission time to provide users with all-weather positioning, speed measurement, and timing services. It has the characteristics of high precision, full coverage, and low cost. However, due to the weak energy of the signal when it is transmitted to the ground receiver, the signal structure is open and not confidential, GNSS is susceptible to various interferences. Among various interferences, the impact of spoofing interference is particularly serious. Spoofing interference refers to malicious interference or misleading of GNSS to mislead the receiver to obtain wrong information. In June 2017, the ship positioning results of more than 20 ships sailing in the Black Sea were affected by spoofing attacks and incorrect positioning. On the morning of March 13, 2024, the defense editor of the British Daily Telegraph and a member of the Shapps delegation encountered an initial short-term electronic warfare attack on the way from Poland to Kaliningrad, and about 511 aircraft in the area were also interfered on the same day. This spoofing signal has extremely bad effects and seriously threatens the safety of GNSS equipment users. In order to prevent this from happening, researching efficient and convenient deception detection technology is of great significance to ensuring the security of spatiotemporal information.

[0003] Deception interference can be divided into forwarding deception and generative deception according to the way the deception is generated. Forwarding deception is to forward the real signal to the user after special processing, while generative deception forms a new deception signal based on the real signal, which is more concealed than forwarding deception. Generative deception attacks can be divided into primary, intermediate and advanced deception attacks according to the complexity. Among them, intermediate deception attacks are also called portable attacks, which are widely used because they are moderately difficult to implement and have good deception effects.

[0004] In order to detect navigation deception interference, deception detection technology can be divided into three categories: space processing technology, measurement domain technology and baseband signal processing technology. Space processing technology detects deception interference by analyzing the spatial information of satellite signals. Its technical design is complex and costly. Measurement domain technology detects deception interference or multiple faults in GNSS / inertial navigation system integration by detecting the consistency of redundant information and the rationality of measurements. When the changes in the detected quantity are not obvious enough, the application of these technologies is limited. Baseband signal processing technology detects deception interference by detecting parameter changes during baseband signal processing. It only needs to design or modify the detection algorithm. The design is simple and does not require peripheral assistance.

[0005] As a relatively mature detection technology in baseband signal technology, signal quality monitoring (SQM) has the advantages of being simple, efficient and low-cost. It does not require modifying the original structure of the receiver and can directly determine whether the output peak of the correlator is distorted to detect deception interference. Phelts first proposed two SQM technologies: Delta algorithm and Ratio algorithm. Both construct the detection quantity through the output of the in-phase branch. The Delta indicator is intended to detect the correlation peak, and the Ratio indicator clearly detects whether there is a "dead zone" (flat correlation peak) at the peak of the correlation function. However, due to the lack of orthogonal branch output in the construction of the detection quantity, the detection performance deteriorates for composite signals that fluctuate in the orthogonal branch. The ELP (early-late phase) algorithm uses the phase difference between the leading code and the lagging code to detect deception interference and multipath interference. While it is effective, there is also a phenomenon that the detection effect deteriorates sharply when the phase difference between the deception signal and the real signal is an integer multiple of π. Dehghanian et al. proposed a deception detection method based on power monitoring, but this method is limited to the detection effect being obvious when the power of the deception signal is higher than the power of the real signal. Sun et al. proposed to use the sliding window average method to process the existing SQM method, which can significantly improve the probability of deception detection. The PCS algorithm proposed by Wang Lu et al. is a new detection algorithm based on the Ratio algorithm, but it is seriously affected by noise, has large fluctuations in the traction stage, and the detection effect is not ideal. Summary of the invention

[0006] In order to solve the technical problem that the current deception scenarios are diversified and complicated, traditional deception detection algorithms basically have good deception detection effects for a single scenario, but in other different deception scenarios, the detection effect is significantly reduced after being affected by various interferences. The embodiments of the present invention provide a signal deception detection method and system based on the PCS algorithm and the Ratio algorithm.

[0007] The technical solution of the embodiment of the present invention is achieved as follows:

[0008] The embodiment of the present invention provides an improved signal spoofing detection method based on the PCS algorithm and the Ratio algorithm, the method comprising:

[0009] Acquire an in-phase branch output result and a quadrature branch output result obtained by capturing and tracking satellite signal data;

[0010] Constructing an algorithm detection quantity using the in-phase branch output result and the orthogonal branch output result;

[0011] Acquire a detection threshold, and use the detection threshold to perform signal deception detection on the algorithm detection quantity;

[0012] The algorithm detects:

[0013]

[0014] Among them, PR(t) represents the algorithm detection quantity, I E (t) represents the output result of the in-phase branch of the early code, Q E (t) represents the orthogonal branch output result of the early code, I P (t) represents the output result of the in-phase branch of the instant code, Q P (t) represents the orthogonal branch output result of the instant code, I L (t) represents the output result of the in-phase branch of the late code, Q L (t) represents the orthogonal branch output result of the late code, and β is the correlation peak slope.

[0015] In one embodiment, using the detection threshold to perform signal deception detection on the algorithm detection amount includes:

[0016] The detection threshold is used to perform signal deception detection on the algorithm detection quantity through the following calculation formula:

[0017]

[0018] Among them, H 0 For no deception interference, H 1 For deceptive interference, Th l is the lower limit of the detection threshold, Th u is the upper limit of the detection threshold, and PR(t) represents the algorithm detection quantity.

[0019] In one embodiment, obtaining a detection threshold includes:

[0020] Determine the computational expression for the probability of a false alarm event;

[0021] Obtain the probability of a preset false alarm event;

[0022] Based on the calculation expression of the probability of the false alarm event, the detection threshold is calculated using the preset probability of the false alarm event.

[0023] In one embodiment, the probability of a false alarm event is calculated as follows:

[0024]

[0025] Among them, P f is the probability of a false alarm event, Th l is the lower limit of the detection threshold, Th u is the upper limit of the detection threshold, p(z|H 0 ) is the probability density function under the condition.

[0026] In one embodiment, based on the calculation expression of the probability of the false alarm event, the detection threshold is calculated using the preset probability of the false alarm event, including:

[0027] Based on the calculation expression of the probability of the false alarm event, obtaining the calculation expression of the detection threshold;

[0028] The detection threshold is calculated by using the probability of the preset false alarm event through the calculation expression of the detection threshold;

[0029] The calculation expression of the detection threshold is:

[0030]

[0031]

[0032] Among them, Th l is the lower limit of the detection threshold, Th u is the upper limit of the detection threshold, P f is the probability of a false alarm event, erfc -1 (·) is the complementary error function, σ 2 is the variance of the detected quantity, and μ is the mean of the detected quantity.

[0033] In one embodiment, after calculating the detection threshold, the method further includes:

[0034] Based on the detection threshold, determining a calculation expression for the detection probability;

[0035] The algorithm detection quantity is subjected to sliding window processing to obtain the detection probability of each sliding window.

[0036] In one embodiment, the calculation expression of the detection probability is:

[0037]

[0038] Among them, P d is the detection probability, PR(t) represents the algorithm detection quantity, num(·) represents the value that satisfies the conditions in brackets, N is the total number of samples with deception interference, and Th l is the lower limit of the detection threshold, Th u The upper limit of the detection threshold.

[0039] In one embodiment, the detection probability of each sliding window is:

[0040]

[0041] Among them, P d-ma (k) is the detection probability of each sliding window, Th lis the lower limit of the detection threshold, Th u is the upper limit of the detection threshold, num(·) represents the value that satisfies the conditions in the brackets, k=1,2,…,N; N represents the number of windows; ω represents the window length; L represents the window spacing; M PR [a,b] represents the PR detection quantity sample points in the interval from a to b.

[0042] An embodiment of the present invention also provides an improved signal deception detection system based on the PCS algorithm and the Ratio algorithm, comprising: a processor and a memory for storing a computer program that can be run on the processor; wherein, when the processor is used to run the computer program, the steps of any one of the above methods are executed.

[0043] An embodiment of the present invention further provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0044] The embodiment of the present invention provides an improved signal deception detection method and system based on the PCS algorithm and the Ratio algorithm, which obtains the in-phase branch output result and the orthogonal branch output result obtained by capturing and tracking satellite signal data; constructs an algorithm detection amount using the in-phase branch output result and the orthogonal branch output result; obtains a detection threshold, and uses the detection threshold to perform signal deception detection on the algorithm detection amount. The solution provided by the present invention can realize deception detection for different scenarios, and has better detectability and universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flowchart of an improved signal deception detection method based on the PCS algorithm and the Ratio algorithm according to an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of the intermediate deception process in an embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of the system flow of an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of the detection probability flow of partial sliding calculation according to an embodiment of the present invention;

[0049] Figure 5 Schematic diagram of comparison of detection performance curves under different false alarm probabilities in an embodiment of the present invention; wherein, Figure 5 (a) is a schematic diagram of the comparison of detection performance curves of the ds3 dataset; Figure 5 (b) is a schematic diagram of the comparison of detection performance curves of the ds4 dataset;

[0050] Figure 5(c) is a schematic diagram of the comparison of detection performance curves of the ds6 dataset; Figure 5 (d) is a schematic diagram of the comparison of detection performance curves of the ds7 dataset;

[0051] Figure 6 Schematic diagram of the relationship between the detection probability and the false alarm probability of multiple data sets in the embodiment of the present invention; wherein, Figure 6 (a) is a schematic diagram of the experimental comparison of the ds3 dataset; Figure 6 (b) is a schematic diagram of the experimental comparison of the ds4 dataset; Figure 6 (c) is a schematic diagram of the experimental comparison of the ds6 dataset; Figure 6 (d) is a schematic diagram of the experimental comparison of the ds7 dataset;

[0052] Figure 7 1 is a diagram showing the internal structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0054] The embodiment of the present invention provides an improved signal deception detection method based on the PCS algorithm and the Ratio algorithm. Figure 1 As shown, the method includes:

[0055] Step 101: Acquire an in-phase branch output result and a quadrature branch output result obtained by capturing and tracking satellite signal data;

[0056] Step 102: constructing an algorithm detection quantity using the in-phase branch output result and the quadrature branch output result;

[0057] Step 103: obtaining a detection threshold, and using the detection threshold to perform signal deception detection on the algorithm detection amount;

[0058] The algorithm detects:

[0059]

[0060] Among them, PR(t) represents the algorithm detection quantity, I E (t) represents the output result of the in-phase branch of the early code, Q E (t) represents the orthogonal branch output result of the early code, I P (t) represents the output result of the in-phase branch of the instant code, Q P (t) represents the orthogonal branch output result of the instant code, I L (t) represents the output result of the in-phase branch of the late code, Q L (t) represents the orthogonal branch output result of the late code, and β is the correlation peak slope.

[0061] This embodiment is based on the collection, capture and tracking of satellite signal data by the front end of the navigation receiver, and transmits the data in the tracking loop to the centralized processing unit. The satellite signal data is deception detected through an intelligent algorithm, and binary judgment is used to determine whether deception exists and calculate the judgment threshold. If the judgment threshold is exceeded, it is determined that deception interference exists.

[0062] Specifically, see Figure 2 The application object described in this embodiment is intermediate deception interference. When deception interference exists in the received satellite signal, the deception interference can be detected as early as possible and more accurately. The process of the intermediate deception attack is as follows: Figure 2 shown.

[0063] See also Figure 3 The overall algorithm flow chart of this embodiment is as follows: Figure 3 shown.

[0064] From the above overall algorithm flow chart, the specific contents of the deception interference detection process include the following parts:

[0065] The Ratio algorithm uses the in-phase branch output I E ,I P ,I L Data is used to construct the detection quantity in the form of ratio, and the detection quantity expression is:

[0066]

[0067] Where β is the correlation peak slope, which is usually 1.

[0068] The PCS algorithm uses the related output amplitudes of the leading code, the immediate code, and the lagging code to construct the detection quantity, combining power monitoring with SQM. The detection quantity is:

[0069] PCS(t)=E+L-2P Calculation formula (2)

[0070] Among them, E, P, and L are the output results of the advanced code, prompt code, and delayed code correlators, respectively. The specific expressions are: and

[0071] Based on the Ratio algorithm and the PCS algorithm, this embodiment improves the PCS algorithm detection amount and the Ratio algorithm detection amount, and forms a new detection amount through linear operation. The weight factor is introduced to make the new algorithm retain the Gaussianity of the original algorithm. The PR algorithm detection amount constructed in this embodiment is:

[0072]

[0073] Among them, PR(t) uses the amplitude and the real part output of the in-phase branch to construct the detection quantity. According to the correlation output amplitude relationship of the leading code, the immediate code, and the lagging code, PR(t) = -P can be obtained in the non-deception scenario. After the deception enters, the correlation peak is distorted, and then the output of the code tracking loop is abnormal, and the detection quantity PR(t) also changes accordingly.

[0074] The dual threshold deception detection process is introduced into the binary decision level, that is, there are two types of deception interference and no deception interference. The binary decision formula can be expressed as:

[0075]

[0076] Among them, H 0 For no deception, H 1 There are four possible outcomes (one is considered to be either no deception or fraud when there is no deception, and one is considered to be either no deception or fraud when there is deception). 1 |H 0 (decided to be deceptive when there is no deception in fact) and H 1 |H 1 (determined as fraud when there is actually fraud) respectively represent false alarm events and detection events, which are most likely to affect the judgment. The probability of a false alarm event is P f , which can be expressed as:

[0077]

[0078] Among them, Th l is the detection limit, Th u is the upper limit of detection. The probability of a detection event occurring is P d , which can be expressed as:

[0079]

[0080] Among them, p(zH 0 ) is the probability density function under the condition.

[0081] According to the above analysis, PR(t) is also approximately Gaussian distributed, and its probability density function is approximately Gaussian distributed. Therefore, from the calculation formula (5), we can know that the detection threshold Th is l ,Th h Need to know P f , the formula for the detection threshold is:

[0082]

[0083] Among them, μ is the mean value of the detection quantity, σ 2 is the variance of the detected quantity, erfc -1(·) is the complementary error function.

[0084] Since the parameters of the deceptive jamming signal are time-varying and unknown, this proposal uses a statistical method to calculate the detection probability P of the proposed algorithm. d , whose expression is:

[0085]

[0086] Where PR(t) is the output value of the PR algorithm detection quantity, num(·) represents the value that meets the conditions in the brackets, and N is the total number of samples with deceptive interference.

[0087] Under the condition of known false alarm probability, the detection threshold is calculated according to formula (7) and formula (8), and then the detection probability is obtained according to formula (9). In order to improve the accuracy of deception process analysis, the sliding window is used to calculate the detection probability, such as Figure 4 shown.

[0088] The window slides once and outputs a detection probability, and a total of k detection probabilities are output. The formula is:

[0089]

[0090] Where k = 1, 2, ..., N; N is the number of windows; ω is the window length; L is the window spacing; M PR [a,b] represents the PR detection quantity sample points in the interval from a to b.

[0091] This embodiment can realize deception detection for different scenarios by improving the algorithm, and can improve the universality and robustness of deception detection and the security of spatiotemporal information by optimizing the deception detection effect.

[0092] like Figure 5 As shown, this embodiment analyzes the deception detection performance according to the characteristics of each scenario. Figure 5 It can be seen that, compared with other original algorithms, the method of this embodiment has the best detection performance, better detectability and universality.

[0093] Also, see Figure 6 , for different deception scenarios, 3 satellites are selected from each deception scenario to analyze the deception detection performance. Figure 6 It can be seen that the detection performance of the method in this embodiment is better than that of other algorithms and has good robustness.

[0094] in, Figure 5 and Figure 6 The dataset in is as follows:

[0095]

[0096]

[0097] This embodiment has a better deception detection effect for deception interference in different types of scenarios, ensures the security of spatiotemporal information, and has better detectability, universality and robustness.

[0098] In order to implement the method of an embodiment of the present invention, an embodiment of the present invention also provides an improved signal deception detection system based on the PCS algorithm and the Ratio algorithm, comprising: a processor and a memory for storing a computer program that can be run on the processor; wherein, when the processor is used to run the computer program, the steps of any one of the above methods are executed.

[0099] The above-mentioned system provided in this embodiment belongs to the same concept as the above-mentioned method embodiment. The specific implementation process thereof is detailed in the method embodiment and will not be repeated here.

[0100] In order to implement the method of the embodiment of the present invention, the embodiment of the present invention also provides a computer program product, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps of the above method.

[0101] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiment of the present invention, the embodiment of the present invention also provides an electronic device (computer device). Specifically, in one embodiment, the computer device can be a terminal, and its internal structure diagram can be as follows: Figure 7 As shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, the method of any one of the above embodiments is implemented. The display screen A04 of the computer device can be a liquid crystal display or an electronic ink display, and the input device A05 of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0102] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0103] The device provided by the embodiment of the present invention includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the method of any one of the above embodiments is implemented.

[0104] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0105] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0108] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0109] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0110] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0111] It can be understood that the memory of the embodiment of the present invention can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memories.

[0112] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0113] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. An improved signal deception detection method based on PCS algorithm and Ratio algorithm, It is characterized in that The method comprises: Acquire an in-phase branch output result and a quadrature branch output result obtained by capturing and tracking satellite signal data; Constructing an algorithm detection quantity using the in-phase branch output result and the orthogonal branch output result; Acquire a detection threshold, and use the detection threshold to perform signal deception detection on the algorithm detection quantity; The algorithm detects: Among them, PR(t) represents the algorithm detection quantity, I E (t) represents the output result of the in-phase branch of the early code, Q E (t) represents the orthogonal branch output result of the early code, I P (t) represents the output result of the in-phase branch of the instant code, Q P (t) represents the orthogonal branch output result of the instant code, I L (t) represents the output result of the in-phase branch of the late code, Q L (t) represents the orthogonal branch output result of the late code, and β is the correlation peak slope.

2. According to claim 1, the improved signal deception detection method based on PCS algorithm and Ratio algorithm, It is characterized in that Using the detection threshold to perform signal deception detection on the algorithm detection amount includes: The detection threshold is used to perform signal deception detection on the algorithm detection quantity through the following calculation formula: Among them, H 0 For no deception interference, H 1 For deceptive interference, Th l is the lower limit of the detection threshold, Th u is the upper limit of the detection threshold, and PR(t) represents the algorithm detection quantity.

3. The signal deception detection method based on the PCS algorithm and the Ratio algorithm according to claim 1, It is characterized in that Get the detection threshold, including: Determine the computational expression for the probability of a false alarm event; Obtain the probability of a preset false alarm event; Based on the calculation expression of the probability of the false alarm event, the detection threshold is calculated using the preset probability of the false alarm event.

4. The signal deception detection method based on the PCS algorithm and the Ratio algorithm according to claim 3, It is characterized in that The calculation expression of the probability of a false alarm event is: Among them, P f is the probability of a false alarm event, Th l is the lower limit of the detection threshold, Th u is the upper limit of the detection threshold, p(z|H 0 ) is the probability density function under the condition.

5. The signal deception detection method based on the PCS algorithm and the Ratio algorithm according to claim 3, It is characterized in that Based on the calculation expression of the probability of the false alarm event, the detection threshold is calculated using the preset probability of the false alarm event, including: Based on the calculation expression of the probability of the false alarm event, obtaining the calculation expression of the detection threshold; The detection threshold is calculated by using the probability of the preset false alarm event through the calculation expression of the detection threshold; The calculation expression of the detection threshold is: Among them, Th l is the lower limit of the detection threshold, Th u is the upper limit of the detection threshold, P f is the probability of a false alarm event, erfc -1 (·) is the complementary error function, σ 2 is the variance of the detected quantity, and μ is the mean of the detected quantity.

6. The signal deception detection method based on the PCS algorithm and the Ratio algorithm according to claim 5, It is characterized in that After calculating the detection threshold, the method further includes: Based on the detection threshold, determining a calculation expression for the detection probability; The algorithm detection quantity is subjected to sliding window processing to obtain the detection probability of each sliding window.

7. The signal deception detection method based on the PCS algorithm and the Ratio algorithm according to claim 6, It is characterized in that The calculation expression of detection probability is: Among them, P d is the detection probability, PR(t) represents the algorithm detection quantity, num(·) represents the value that satisfies the conditions in brackets, N is the total number of samples with deception interference, and Th l is the lower limit of the detection threshold, Th u The upper limit of the detection threshold.

8. The signal deception detection method based on the PCS algorithm and the Ratio algorithm according to claim 6, It is characterized in that The detection probability of each sliding window is: Among them, P d-ma (k) is the detection probability of each sliding window, Th l is the lower limit of the detection threshold, Th u is the upper limit of the detection threshold, num(·) represents the value that satisfies the conditions in the brackets, k=1,2,…,N; N represents the number of windows; ω represents the window length; L represents the window spacing; M PR [a,b] represents the PR detection quantity sample points in the interval from a to b.

9. A signal deception detection system based on the PCS algorithm and the Ratio algorithm. It is characterized in that include: A processor and a memory for storing a computer program that can be run on the processor; wherein, when the processor is used to run the computer program, the steps of the method according to any one of claims 1 to 8 are executed.

10. A storage medium having a computer program stored therein. It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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