GNSS pseudo code blind estimation method and device, equipment and storage medium

Abstract

The invention relates to a GNSS pseudo code blind estimation method and device, equipment and a storage medium. The method comprises the following steps: obtaining an I / Q branch pseudo code estimation value of each sampling point through pseudo code symbol judgment; for any branch, counting pseudo code estimation values of all sampling points in each chip of the current branch, and obtaining a pseudo code initial estimation value of each chip of the current branch through discrete integral operation; constructing two types of local code sequences corresponding to any chip through positive and negative polarity hypothesis, and reconstructing to generate two types of local navigation signals; respectively multiplying and accumulating the received signal of each sampling point and two types of local navigation signals of any chip of the current branch point by point to obtain two types of signal correlation values of any chip, and carrying out pseudo code symbol correction judgment on any chip according to the difference of the two types of signal correlation values; and obtaining the corrected pseudo code estimated value of any chip of the current branch. The method can reduce the bit error rate and improve the accuracy of pseudo code estimation.

Description

Technical Field

[0001] This application relates to the technical field of satellite navigation pseudo-code estimation, and particularly to a GNSS pseudo-code blind estimation method, device, equipment, and storage medium. Background Art

[0002] In the reception of non-cooperative GNSS (Global Navigation Satellite System) signals, the accurate estimation of the pseudo-code sequence is the key to achieving signal synchronization, demodulation, and navigation information extraction.

[0003] In the prior art, a typical method is to separate the I / Q branch signals and directly judge the symbols of the pseudo-code sequence of the corresponding branch according to the positive and negative polarities of the I / Q branch baseband signals by using their orthogonality. This method can effectively recover the pseudo-code sequence under ideal conditions, but in a low signal-to-noise ratio environment, noise will cause incorrect polarity judgment of the I / Q branches, thereby affecting the accuracy of pseudo-code estimation. At the same time, the bit error rate (BER) of pseudo-code estimation has a monotonic decreasing relationship with the signal-to-noise ratio, and the theoretical limit of the performance of pseudo-code estimation is clear. When the signal-to-noise ratio is lower than a certain threshold, the bit error rate rises sharply, making it difficult to meet the high-precision requirements of satellite navigation pseudo-code estimation. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a GNSS pseudo-code blind estimation method, device, equipment, and storage medium that can reduce the bit error rate and improve the accuracy of pseudo-code estimation.

[0005] A GNSS pseudo-code blind estimation method, the method includes: Receiving an orthogonally modulated GNSS signal and performing sampling; Separating the I / Q branch baseband signals in the received signals at each sampling point through quadrature demodulation; Performing pseudo-code symbol judgment on the I / Q branch baseband signals to obtain the I / Q branch pseudo-code estimation values at each sampling point; For any one branch, statistically calculate the pseudo-code estimation values of all sampling points in each chip of the current branch, and obtain the initial pseudo-code estimation value of each chip of the current branch through discrete integral operation; By making positive and negative polarity assumptions on the initial pseudo-code estimation value of any chip in the current branch, constructing two types of local code sequences corresponding to any chip, and reconstructing and generating two types of local navigation signals according to the two types of local code sequences; Multiplying the received signals at each sampling point by the two types of local navigation signals of any chip in the current branch point by point and accumulating, obtaining the two types of signal correlation values of any chip, and performing pseudo-code symbol correction judgment on any chip through the difference between the two types of signal correlation values to obtain the corrected pseudo-code estimation value of any chip in the current branch.

[0006] In one embodiment, receiving an orthogonally modulated GNSS signal and performing sampling includes: Receiving an orthogonally modulated GNSS signal and performing sampling to obtain the received signal at each sampling point, expressed as: ; where is the received signal at the th sampling point; is the power of the P(Y) code sequence modulated on the I branch, is the power of the C / A code sequence modulated on the Q branch; and respectively represent the P(Y) code sequence and the C / A code sequence; represents the sampling point duration, is the sampling rate; and are the data codes of the P(Y) code sequence and the C / A code sequence respectively; is the carrier frequency, is the Doppler frequency shift, is the carrier center frequency; is complex Gaussian noise with a mean of 0 and a variance of .

[0007] In one embodiment, separating the I / Q branch baseband signals in the received signal at each sampling point through quadrature demodulation includes: Separating the I / Q branch baseband signals in the received signal at the th sampling point through quadrature demodulation, respectively expressed as: ; ; where and respectively represent the I branch baseband signal and the Q branch baseband signal at the th sampling point; , and and respectively represent the I branch complex Gaussian noise and the Q branch complex Gaussian noise at the th sampling point.

[0008] In one embodiment, performing pseudo-code symbol decision on the I / Q branch baseband signals to obtain the I / Q branch pseudo-code estimation values at each sampling point includes: Performing pseudo-code symbol decision on the I branch baseband signal at the th sampling point and the Q branch baseband signal respectively to obtain the The estimated pseudo-code value of the P(Y) code sequence of the I branch at each sampling point And the estimated pseudo-code value of the C / A code sequence of the Q branch Are respectively expressed as: ; ; Wherein, Is the sign function. For any real number , ; Is the real number space.

[0009] In one embodiment, the estimated pseudo-code values of all sampling points in each chip of the current branch are statistically analyzed, and the initial estimated pseudo-code value of each chip of the current branch is obtained through discrete integral operation, including: When the current branch is the I branch, the estimated pseudo-code values of the P(Y) code sequence of all sampling points in each chip of the I branch are statistically analyzed and averaged through discrete integral operation to obtain the average estimated pseudo-code value of all sampling points in each chip, which is expressed as: ; Wherein, Represents the average estimated pseudo-code value of the P(Y) code sequence of all sampling points in the m th chip, Represents the number of sampling points in each chip, Is the sampling rate, Is the code rate; Is the floor function, Is the ceiling function; Based on Perform pseudo-code symbol decision to obtain the initial estimated pseudo-code value of the P(Y) code sequence of the th chip, which is expressed as: .

[0010] In one embodiment, by making positive and negative polarity assumptions on the initial estimated pseudo-code value of any chip of the current branch, two types of local code sequences corresponding to any chip are constructed, and two types of local navigation signals are reconstructed based on the two types of local code sequences, including: By making positive and negative polarity assumptions on the initial estimated pseudo-code value of the P(Y) code sequence of the th chip of the I branch, and keeping the initial estimated pseudo-code values of the P(Y) code sequences of the remaining chips unchanged, two types of local code sequences with positive and negative polarities corresponding to the th chip of the I branch are constructed, which are respectively expressed as: ; Wherein, And respectively represent the positive and negative local code sequences of the th chip of the I branch; Reconstruct and generate two types of local navigation signals of the th chip of the I branch according to the two types of local code sequences, which are respectively expressed as: ; ; wherein, and respectively represent the positive local navigation signal and the negative local navigation signal, represents the time sequence of pseudo-code sampling.

[0011] In one embodiment, the received signal of each sampling point is multiplied point by point and accumulated with the two types of local navigation signals of any chip of the current branch to obtain the two types of signal correlation values of any chip, and the pseudo-code symbol correction decision is made on any chip through the difference between the two types of signal correlation values, and the corrected pseudo-code estimation value of any chip of the current branch is obtained, including: Multiply the received signal of the th sampling point respectively with the two types of local navigation signals of the th chip of the I branch, and accumulate them to obtain the two types of signal correlation values of the th chip of the I branch, which are respectively expressed as: ; ; wherein, and respectively represent the positive signal correlation value and the negative signal correlation value of the th chip of the I branch; is the total number of sampling points; Define the difference between the two types of signal correlation values as: ; Based on the positive and negative conditions of , make a pseudo-code symbol correction decision on the th chip of the I branch, and obtain the corrected pseudo-code estimation value of the P(Y) code sequence of the th chip of the I branch, which is expressed as: .

[0012] A GNSS pseudo-code blind estimation device, the device includes: A signal receiving and sampling module, configured to receive GNSS signals subjected to quadrature modulation and perform sampling; A demodulation separation module, which is used to separate the I / Q branch baseband signals in the received signals of each sampling point through quadrature demodulation; A preliminary pseudo-code estimation module, which is used to perform pseudo-code symbol decision on the I / Q branch baseband signals to obtain the I / Q branch pseudo-code estimation values of each sampling point; A discrete integral operation module, which is used for any one branch to count the pseudo-code estimation values of all sampling points in each chip of the current branch, and obtain the initial pseudo-code estimation value of each chip of the current branch through discrete integral operation; A hypothesis reconstruction module, which is used to construct two types of local code sequences corresponding to any one chip by making positive and negative polarity hypotheses on the initial pseudo-code estimation value of any one chip of the current branch, and reconstruct and generate two types of local navigation signals according to the two types of local code sequences; A pseudo-code estimation correction module, which is used to multiply and accumulate the received signals of each sampling point with the two types of local navigation signals of any one chip of the current branch point by point to obtain the two types of signal correlation values of any one chip, and perform pseudo-code symbol correction decision on any one chip through the difference between the two types of signal correlation values to obtain the corrected pseudo-code estimation value of any one chip of the current branch.

[0013] A computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: Receive orthogonally modulated GNSS signals and perform sampling; Separate the I / Q branch baseband signals in the received signals of each sampling point through quadrature demodulation; Perform pseudo-code symbol decision on the I / Q branch baseband signals to obtain the I / Q branch pseudo-code estimation values of each sampling point; For any one branch, count the pseudo-code estimation values of all sampling points in each chip of the current branch, and obtain the initial pseudo-code estimation value of each chip of the current branch through discrete integral operation; Construct two types of local code sequences corresponding to any one chip by making positive and negative polarity hypotheses on the initial pseudo-code estimation value of any one chip of the current branch, and reconstruct and generate two types of local navigation signals according to the two types of local code sequences; Multiply and accumulate the received signals of each sampling point with the two types of local navigation signals of any one chip of the current branch point by point to obtain the two types of signal correlation values of any one chip, and perform pseudo-code symbol correction decision on any one chip through the difference between the two types of signal correlation values to obtain the corrected pseudo-code estimation value of any one chip of the current branch.

[0014] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Receive orthogonally modulated GNSS signals and perform sampling; The I / Q branch baseband signals in the received signals of each sampling point are separated through quadrature demodulation; Perform pseudo-code symbol decision on the I / Q branch baseband signals to obtain the pseudo-code estimation values of the I / Q branches at each sampling point; For any one branch, count the pseudo-code estimation values of all sampling points in each chip of the current branch, and obtain the initial pseudo-code estimation value of each chip of the current branch through discrete integral operation; By assuming the positive and negative polarities of the initial pseudo-code estimation value of any chip in the current branch, construct two types of local code sequences corresponding to any chip, and reconstruct and generate two types of local navigation signals according to the two types of local code sequences; Multiply the received signals of each sampling point by the two types of local navigation signals of any chip in the current branch point by point and accumulate them to obtain the correlation values of the two types of signals of any chip, and perform pseudo-code symbol correction decision on any chip through the difference between the correlation values of the two types of signals to obtain the corrected pseudo-code estimation value of any chip in the current branch.

[0015] The above GNSS pseudo-code blind estimation method, device, equipment and storage medium can effectively suppress the influence of random noise on the pseudo-code estimation performance in a low signal-to-noise ratio environment by statistically calculating the pseudo-code estimation values of multiple sampling points in each chip and performing discrete integration, improving the accuracy and reliability of the initial pseudo-code estimation of a single chip in a low signal-to-noise ratio environment. At the same time, the multiple sampling points in the chip provide more complete statistical features for subsequent pseudo-code correction, which is beneficial to reducing the bit error rate of pseudo-code estimation; further, by assuming the positive and negative polarities of the initial pseudo-code estimation value of any chip, reconstructing and generating local navigation signals with positive and negative polarities and performing correlation calculations with the received signals of each sampling point, it is possible to correct the polarity error of the initial pseudo-code estimation of the chip that may be caused by noise, multipath or quantization error according to the difference between the correlation values, further improving the accuracy of pseudo-code estimation. Description of the Drawings

[0016] Figure 1 It is a schematic flowchart of a GNSS pseudo-code blind estimation method in an embodiment; Figure 2 It is a schematic diagram of the pseudo-code estimation effect of the I / Q branches at each sampling point when the signal-to-noise ratio is -10 dB in an embodiment; where, Figure 2 (a) is a schematic diagram of the pseudo-code estimation effect of the I branch, Figure 2 (b) is a schematic diagram of the pseudo-code estimation effect of the Q branch; Figure 3 It is a schematic diagram of the pseudo-code estimation performance of the existing method at different signal-to-noise ratios in an embodiment; Figure 4 It is a schematic diagram of the pseudo-code estimation performance of the present application after correction at different sampling rates and different signal-to-noise ratios in an embodiment; Figure 5 Schematic diagram of the performance gain of pseudo-code estimation under different sampling rates and different signal-to-noise ratios after calibration of the present application in an embodiment; Figure 6 Internal structure diagram of a computer device in an embodiment. Specific implementation manners

[0017] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] In one embodiment, as Figure 1 shown, a GNSS pseudo-code blind estimation method is provided, including the following steps: Step S1, receiving an orthogonally modulated GNSS signal and performing sampling.

[0019] The sampling rate of the received signal can be set according to the signal-to-noise ratio environment of the specific application of pseudo-code estimation. The higher the sampling rate, the more beneficial it is to provide more sampling points within a chip, with more information, providing more complete statistical features for pseudo-code calibration, and thus gradually reducing the bit error rate of pseudo-code estimation.

[0020] Step S2, separating the I / Q branch baseband signals in the received signals of each sampling point through orthogonal demodulation.

[0021] Orthogonal demodulation means decomposing the received signal into two orthogonal components (I (in-phase) path and Q (quadrature) path), multiplying them with the in-phase (In-phase) and quadrature (Quadrature) components of the local carrier respectively, and then extracting the baseband signal through low-pass filtering.

[0022] Step S3, performing pseudo-code symbol decision on the I / Q branch baseband signals to obtain the pseudo-code estimation values of the I / Q branches of each sampling point.

[0023] The pseudo-code estimation values of the I / Q branches of each sampling point are directly judged according to the positive and negative polarities of the I / Q branch baseband signals. Considering that in a low signal-to-noise ratio environment, noise will cause incorrect polarity decision of the I / Q branches, which will in turn affect the accuracy of pseudo-code estimation. Therefore, further pseudo-code estimation calibration is required later.

[0024] Step S4, for any one branch, statistically calculating the pseudo-code estimation values of all sampling points in each chip of the current branch, and obtaining the initial pseudo-code estimation value of each chip of the current branch through discrete integral operation.

[0025] The discrete integral operation means taking the average of the pseudo-code estimation values of all sampling points within a chip. By integrating and averaging all sampling points within a chip, it is beneficial to suppress the influence caused by random noise of sampling points and improve the reliability of the pseudo-code estimation of a single chip.

[0026] Step S5: By assuming the positive and negative polarities of the initial pseudo-code estimation value of any chip in the current branch, two types of local code sequences corresponding to any chip are constructed, and two types of local navigation signals are reconstructed and generated based on the two types of local code sequences.

[0027] Step S6: Multiply the received signal of each sampling point by the two types of local navigation signals of any chip in the current branch point by point and accumulate them to obtain the correlation values of the two types of signals for any chip. Then, perform a pseudo-code symbol correction decision on any chip based on the difference between the correlation values of the two types of signals to obtain the corrected pseudo-code estimation value of any chip in the current branch.

[0028] In the above GNSS pseudo-code blind estimation method, by statistically calculating the pseudo-code estimation values of multiple sampling points within each chip and performing discrete integration, the influence of random noise on the pseudo-code estimation performance in a low signal-to-noise ratio environment can be effectively suppressed, and the accuracy and reliability of the initial pseudo-code estimation of a single chip in a low signal-to-noise ratio environment are improved. At the same time, multiple sampling points within the chip provide more complete statistical features for subsequent pseudo-code correction, which is beneficial to reducing the bit error rate of pseudo-code estimation. Further, by assuming the positive and negative polarities of the initial pseudo-code estimation value of any chip, reconstructing and generating local navigation signals with positive and negative polarities and performing correlation calculations with the received signals of each sampling point, it is possible to correct the possible polarity errors of the initial pseudo-code estimation of the chip caused by noise, multipath or quantization errors according to the difference of the correlation values, further improving the accuracy of pseudo-code estimation.

[0029] In one embodiment, step S1 specifically includes: taking the QPSK (Quadrature Phase Shift Keying) modulated signal transmitted by a GPS satellite as an example. This signal contains two orthogonal components: the I branch modulates the P(Y) code (Precise Range Code), and the Q branch modulates the C / A code (Coarse Acquisition Code). Receive the signal and perform sampling to obtain the received signals of each sampling point, which are expressed as: ; where, is the received signal of the th sampling point; is the power of the P(Y) code sequence modulated by the I branch, is the power of the C / A code sequence modulated by the Q branch; and respectively represent the P(Y) code sequence and the C / A code sequence; represents the sampling point duration, is the sampling rate; and are the data codes of the P(Y) code sequence and the C / A code sequence respectively; is the carrier frequency, is the Doppler frequency shift, is the carrier center frequency; is complex Gaussian noise with a mean of 0 and a variance of .

[0030] In one embodiment, step S2 specifically includes: separating the received signal at the th sampling point through quadrature demodulation to obtain the I / Q branch baseband signals, which are respectively expressed as: ; ; where and respectively represent the I-branch baseband signal and the Q-branch baseband signal at the th sampling point; , and and respectively represent the I-branch complex Gaussian noise and the Q-branch complex Gaussian noise at the th sampling point.

[0031] In one embodiment, step S3 specifically includes: respectively performing pseudo-code symbol decisions on the I-branch baseband signal at the th sampling point and the Q-branch baseband signal to obtain the pseudo-code estimation values of the I-branch P(Y) code sequence and the Q-branch C / A code sequence at the th sampling point, which are respectively expressed as: ; ; where is the sign function. For any real number , ; is the real number space. This decision is essentially a symbol decision based on the minimum phase distance criterion. Although it can achieve pseudo-code estimation for each sampling point to a certain extent, under low signal-to-noise ratio conditions (such as -10 dB), as Figure 2 shows, there are many errors in the pseudo-code estimation of the I / Q branch signals at each sampling point, which is caused by noise contaminating the phase of the sampling points. Therefore, this pseudo-code estimation value needs to be further corrected. Figure 2 The abscissa in

[0032] In one embodiment, step S4 includes: when the current branch is the I branch, first count the pseudo-code estimation values of the P(Y) code sequences of all sampling points in each chip of the I branch and take the average through discrete integral operation to obtain the pseudo-code estimation mean value of all sampling points in each chip, which is expressed as: ; where, represents the pseudo-code estimation mean value of the code sequences of all sampling points in the th chip, represents the number of sampling points in each chip, is the sampling rate, is the code rate; is the floor function, is the ceiling function. Specifically, for the QPSK modulation signal transmitted by GPS satellites, . By taking the average, the influence of random noise on single sampling points can be suppressed, and the decision reliability of a single chip can be improved.

[0033] Then, based on , perform pseudo-code symbol decision to obtain the initial pseudo-code estimation value of the P(Y) code sequence of the m th chip, which is expressed as: .

[0034] In one embodiment, step S5 includes: when the current branch is the I branch, first make positive and negative polarity assumptions on the initial pseudo-code estimation value of the P(Y) code sequence of the th chip of the I branch, and keep the initial pseudo-code estimation values of the P(Y) code sequences of the remaining chips unchanged, and construct two types of local code sequences with positive and negative polarities corresponding to the th chip of the I branch, which are respectively expressed as: ; where, and respectively represent the positive local code sequence and the negative local code sequence of the th chip of the I branch.

[0035] Then, reconstruct and generate two types of local navigation signals of the th chip of the I branch according to the two types of local code sequences, which are respectively expressed as: ; ; where, and respectively represent the positive local navigation signal and the negative local navigation signal, Represents the time series of pseudo-code sampling.

[0036] In one of the embodiments, step S6 includes: when the current branch is the I branch, first the received signal of the th sampling point is multiplied point by point and accumulated with two types of local navigation signals of the th chip of the I branch, and the correlation values of the two types of signals of the th chip of the I branch are obtained, and are respectively expressed as: ; where, and respectively represent the positive signal correlation value and the negative signal correlation value of the th chip of the I branch; is the total number of sampling points.

[0037] Then, define the difference between the correlation values of the two types of signals as: ; Finally, based on the positive and negative conditions, perform pseudo-code symbol correction decision on the th chip of the I branch, and obtain the pseudo-code estimated value of the corrected P(Y) code sequence of the th chip of the I branch, which is expressed as: .

[0038] Among them, by performing correlation calculations on the reconstructed positive and negative local navigation signals and the received signals of each sampling point, it is possible to correct the possible polarity errors of the initial pseudo-code estimation of the chip due to noise, multipath or quantization errors, and further improve the accuracy of pseudo-code estimation.

[0039] To verify the pseudo-code estimation performance of the method proposed in this application, further experimental verification was carried out. Since the existing method directly performs pseudo-code symbol decision based on the minimum phase distance, its pseudo-code estimation performance is only related to the signal-to-noise ratio. Therefore, the pseudo-code estimation performance at different signal-to-noise ratios was simulated. As Figure 3 shown, it uses the bit error rate to characterize the code estimation performance. The lower the bit error rate, the better the code estimation performance. Therefore, Figure 3 shows that the pseudo-code estimation performance of the existing method is low in a low signal-to-noise ratio environment. Figure 4 is the schematic diagram of the pseudo-code estimation performance of this application after correction at different sampling rates and different signal-to-noise ratios. As Figure 4As shown, at a fixed signal-to-noise ratio, as the sampling rate increases, the bit error rate of the pseudo-code estimation gradually decreases. This is because at a high sampling rate, more sampling points within a chip are provided, with more information, providing more complete statistical features for pseudo-code correction. Figure 5 The bit error rate reduction amount is then used to show the performance gain of the pseudo-code estimation under different sampling rates and different signal-to-noise ratios after the correction of this application. As can be seen from Figure 5 it that at a fixed signal-to-noise ratio, the performance gain of the pseudo-code estimation increases with the increase of the sampling rate. This benefits from the correction information amount brought by the increase in the number of sampling points within the code. The maximum reduction amount of the bit error rate can reach 21.15%. Therefore, as can be seen from Figure 4 and Figure 5 it, even in a low signal-to-noise ratio environment, this application can improve the pseudo-code estimation performance by increasing the sampling rate.

[0040] In one embodiment, a GNSS pseudo-code blind estimation device is provided, including: A signal receiving and sampling module, configured to receive a quadrature-modulated GNSS signal and perform sampling; A demodulation and separation module, configured to separate the I / Q branch baseband signals in the received signals of each sampling point through quadrature demodulation; A preliminary pseudo-code estimation module, configured to perform pseudo-code symbol decision on the I / Q branch baseband signals to obtain the I / Q branch pseudo-code estimation values of each sampling point; A discrete integral operation module, configured to, for any one branch, count the pseudo-code estimation values of all sampling points in each chip of the current branch, and obtain the initial pseudo-code estimation value of each chip of the current branch through discrete integral operation; A hypothesis reconstruction module, configured to construct two types of local code sequences corresponding to any one chip by making positive and negative polarity hypotheses on the initial pseudo-code estimation value of any one chip of the current branch, and reconstruct two types of local navigation signals according to the two types of local code sequences; A pseudo-code estimation correction module, configured to multiply each received signal of each sampling point by the two types of local navigation signals of any one chip of the current branch point by point and accumulate, obtain the two types of signal correlation values of any one chip, and perform pseudo-code symbol correction decision on any one chip through the difference between the two types of signal correlation values to obtain the corrected pseudo-code estimation value of any one chip of the current branch.

[0041] For the specific limitations of a GNSS pseudo-code blind estimation device, reference can be made to the limitations of a GNSS pseudo-code blind estimation method in the above text, which will not be elaborated here. Each module in the above GNSS pseudo-code blind estimation device can be implemented in whole or in part through software, hardware, and their combinations. The above modules can be embedded in the processor in the computer device in hardware form or be independent of it, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0042] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in Figure 6 . The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface 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, it implements a GNSS pseudo-code blind estimation method. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0043] Those skilled in the art can understand that Figure 6 the structure shown in

[0044] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. Receive an orthogonally modulated GNSS signal and perform sampling; Separate the I / Q branch baseband signals in the received signals of each sampling point through quadrature demodulation; Perform pseudo-code symbol decision on the I / Q branch baseband signals to obtain the I / Q branch pseudo-code estimation values of each sampling point; For any one branch, count the pseudo-code estimation values of all sampling points in each chip of the current branch, and obtain the initial pseudo-code estimation value of each chip of the current branch through discrete integral operation; By assuming the positive and negative polarities of the initial pseudo-code estimation value of any chip in the current branch, construct two types of local code sequences corresponding to any chip, and reconstruct and generate two types of local navigation signals according to the two types of local code sequences; Multiply the received signals at each sampling point with two types of local navigation signals of any chip in the current branch point by point and accumulate them to obtain the correlation values of the two types of signals for any chip. Then, perform a pseudo-code symbol correction decision on any chip based on the difference between the correlation values of the two types of signals to obtain the corrected pseudo-code estimation value of any chip in the current branch.

[0045] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: Receive the quadrature-modulated GNSS signal and perform sampling; Separate the I / Q branch baseband signals in the received signals at each sampling point through quadrature demodulation; Perform a pseudo-code symbol decision on the I / Q branch baseband signals to obtain the pseudo-code estimation values of the I / Q branches at each sampling point; For any one branch, count the pseudo-code estimation values of all sampling points in each chip of the current branch, and obtain the initial pseudo-code estimation value of each chip in the current branch through discrete integral operation; Construct two types of local code sequences corresponding to any chip by assuming the positive and negative polarities of the initial pseudo-code estimation value of any chip in the current branch, and reconstruct and generate two types of local navigation signals according to the two types of local code sequences; Multiply the received signals at each sampling point with two types of local navigation signals of any chip in the current branch point by point and accumulate them to obtain the correlation values of the two types of signals for any chip. Then, perform a pseudo-code symbol correction decision on any chip based on the difference between the correlation values of the two types of signals to obtain the corrected pseudo-code estimation value of any chip in the current branch.

[0046] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0047] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0048] The above-described embodiments merely represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application.

Claims

1. A GNSS pseudo-code blind estimation method, characterized in that: The method comprises: Receive orthogonally modulated GNSS signals and perform sampling; Separate the I / Q branch baseband signals from the received signals at each sampling point through orthogonal demodulation; Performing pseudo code symbol decision on the I / Q branch baseband signal to obtain an I / Q branch pseudo code estimation value at each sampling point; For any branch, the pseudo code estimated values of all sampling points in each chip of the current branch are counted, and the initial pseudo code estimated value of each chip of the current branch is obtained by discrete integration operation; By making positive and negative polarity assumptions on the initial pseudo-code estimate of any chip in the current branch, two types of local code sequences corresponding to any chip are constructed, and two types of local navigation signals are reconstructed based on the two types of local code sequences. The received signal of each sampling point is multiplied point by point with the two types of local navigation signals of any code chip of the current branch and accumulated to obtain the two types of signal correlation values of any code chip. The pseudo-code symbol correction decision is performed on any code chip based on the difference between the two types of signal correlation values to obtain the corrected pseudo-code estimate value of any code chip of the current branch.

2. The method according to claim 1, characterized in that Receive and sample quadrature modulated GNSS signals, including: Receive the orthogonally modulated GNSS signal and perform sampling to obtain the received signal at each sampling point, which is expressed as: ； in, For the The received signal of each sampling point; is the power of the P(Y) code sequence modulated by the I branch, is the power of the C / A code sequence modulated by the Q branch; and represent the P(Y) code sequence and the C / A code sequence respectively; Indicates the duration of the sampling point, is the sampling rate; and The data codes are P(Y) code sequence and C / A code sequence respectively; is the carrier frequency, is the Doppler shift, is the carrier center frequency; is complex Gaussian noise with mean 0 and variance .

3. The method according to claim 2, characterized in that The I / Q branch baseband signals in the received signal at each sampling point are separated by orthogonal demodulation, including: Through quadrature demodulation, the The received signal at each sampling point The I / Q branch baseband signals in are expressed as: ； ； in, and Respectively represent The I-branch baseband signal and the Q-branch baseband signal of the sampling points; ,and and Respectively represent The I branch complex Gaussian noise and the Q branch complex Gaussian noise of the sampling points.

4. The method according to claim 3, characterized in that Performing pseudo code symbol determination on the I / Q branch baseband signal to obtain an I / Q branch pseudo code estimation value at each sampling point includes: Respectively The baseband signal of the I branch at each sampling point and Q branch baseband signal Make pseudo code symbol decisions respectively and obtain the Pseudocode estimation value of the P(Y) code sequence of the I branch at sampling points and the estimated pseudo code value of the C / A code sequence of the Q branch , respectively expressed as: ； ； in, is a sign function, for any real number , ; is the real number space.

5. The method according to claim 4, characterized in that Count the pseudo code estimated values of all sampling points in each chip of the current branch, and obtain the initial pseudo code estimated value of each chip of the current branch through discrete integration operation, including: When the current branch is the I branch, the pseudo code estimation values of the P(Y) code sequence of all sampling points in each chip of the I branch are counted and averaged through discrete integral operation to obtain the pseudo code estimation mean of all sampling points in each chip, which is expressed as: ； in, Indicates the m The estimated mean value of the pseudo code of the P(Y) code sequence of all sampling points in the code chip, Indicates the number of sampling points in each chip, is the sampling rate, is the bit rate; is the floor function, is the ceiling function; based on Make pseudo code symbol judgment and get the m The initial estimated value of the pseudo code of the P(Y) code sequence of chips is expressed as: 。 6. The method according to claim 5, characterized in that By making positive and negative polarity assumptions on the initial pseudo-code estimate of any chip in the current branch, two types of local code sequences corresponding to any chip are constructed. Two types of local navigation signals are reconstructed based on the two types of local code sequences, including: By The initial estimated values of the pseudo-code of the P(Y) code sequence of the first chip are assumed to be positive or negative, while the initial estimated values of the pseudo-code of the P(Y) code sequence of the remaining chips remain unchanged, and the I branch is constructed. The two types of local code sequences corresponding to the chips are positive and negative polarities, respectively, and are expressed as: ； in, and Respectively represent the I branch A positive local code sequence and a negative local code sequence of chips; According to the two types of local code sequences, the I branch is reconstructed and generated. The two types of local navigation signals of the chip are expressed as: ； ； in, and Represent the positive local navigation signal and the negative local navigation signal respectively. Represents a time series of pseudo-code samples.

7. The method according to claim 6, characterized in that The received signal at each sampling point is multiplied point by point with the two types of local navigation signals of any chip of the current branch and then accumulated to obtain the correlation value of the two types of signals of any chip. The pseudo-code symbol correction decision is performed on any chip based on the difference between the two types of signal correlation values to obtain the corrected pseudo-code estimate of any chip of the current branch, including: The first The received signal at each sampling point I branch The two types of local navigation signals of the chip are multiplied point by point and accumulated to obtain the I branch The two types of signal correlation values of chips are expressed as: ； ； in, and Respectively represent the I branch The positive signal correlation value and the negative signal correlation value of each chip; is the total number of sampling points; The difference between the correlation values of the two types of signals is defined as: ； based on The positive and negative conditions of the I branch The code chips are used to perform pseudo code symbol correction and judgment, and the first The pseudo code estimate of the P(Y) code sequence after chip correction is expressed as: 。 8. A GNSS pseudo-code blind estimation device, characterized in that: The device comprises: A signal receiving and sampling module is used to receive and sample the orthogonally modulated GNSS signal; A demodulation and separation module is used to separate the I / Q branch baseband signals from the received signal of each sampling point through orthogonal demodulation; A preliminary pseudo code estimation module is used to perform pseudo code symbol judgment on the I / Q branch baseband signal and obtain an I / Q branch pseudo code estimation value at each sampling point; The discrete integral operation module is used to count the pseudo code estimation values of all sampling points in each chip of the current branch for any branch, and obtain the initial pseudo code estimation value of each chip of the current branch through discrete integral operation; A hypothesis reconstruction module is used to construct two types of local code sequences corresponding to any code chip by making positive and negative polarity assumptions on the initial estimated pseudo code value of any code chip of the current branch, and reconstruct two types of local navigation signals based on the two types of local code sequences; The pseudo code estimation and correction module is used to multiply the received signal of each sampling point with the two types of local navigation signals of any code chip of the current branch point by point and accumulate them to obtain the two types of signal correlation values of any code chip, and perform pseudo code symbol correction judgment on any code chip based on the difference between the two types of signal correlation values to obtain the corrected pseudo code estimation value of any code chip of the current branch.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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