A method, a computing device and a storage medium for multipath pseudorange estimation

Abstract

The application discloses a method for estimating multipath pseudo-range, comprising: calculating the correlation values of the sampling points closest to the best sampling point on the multipath time-domain waveform diagram and the sampling points before and after the sampling points based on the received signal and the spread spectrum pseudo-code; calculating the correlation values of the multipath between the adjacent sampling points; correcting the correlation values of the sampling points closest to the best sampling point and the sampling points before and after the sampling points based on the correlation values of the multipath between the adjacent sampling points, and obtaining the corrected correlation values; calculating the decimal multiple time delay error based on the corrected correlation values, so as to estimate the pseudo-range based on the decimal multiple time delay error. The scheme can improve the precision of distance measurement in a complex electromagnetic environment.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) ranging technology, specifically to a multipath pseudorange estimation method, computing device, and storage medium. Background Technology

[0002] Pseudo-code ranging technology has been widely used in UAV telemetry and control systems. Based on the relationship between uplink and downlink ranging signals, pseudo-code ranging methods are divided into coherent ranging and incoherent ranging. Whether it is coherent ranging or incoherent ranging, it is based on the frame structure of the signal. The synchronization code is estimated to sample the signal at the beginning of the synchronization header to obtain the pseudo-range. Finally, the actual distance is calculated based on the pseudo-range.

[0003] Coherent ranging involves the measuring node sampling the transmitted signal at the start of the synchronization header of the received signal, while the synchronization header transmission time of the other node needs to be strictly aligned with the synchronization header of the received signal according to the time slot. However, in actual FPGA operation, due to the sampling clock frequency... Due to limitations, the accuracy of pseudorange measurement can only be... The magnitude of the time difference is far from meeting the requirements for high-precision ranging of UAVs. Therefore, it is necessary to estimate the pseudorange, which is a fraction of the sampling period, i.e., to estimate the fractional part of the time between two sampling points.

[0004] However, for multipath channel systems, when using spread spectrum pseudocode tracking for time estimation, it is impossible to process the modulus square of the correlation values ​​of the optimal sampling point and the two sampling points on either side. Furthermore, when the multipath is within a chip but is greater than the sampling time interval, it has a significant impact on the judgment of the optimal sampling point of the correlation peak, thus affecting the accuracy of multipath ranging. Summary of the Invention

[0005] To improve the pseudorange measurement accuracy of UAVs in complex electromagnetic environments, this solution proposes a multipath pseudorange estimation method, computing device, and storage medium. The correlation values ​​of multiple sampling points near the optimal sampling point are calculated by the modulus of the correlation coefficient between the received signal and the spread spectrum pseudocode. Based on the correlation values ​​between two adjacent sampling points under multipath conditions, the correlation values ​​of the sampling points are corrected to obtain the corrected multipath fractional delay error. Pseudorange estimation is then performed based on this fractional delay error to improve the accuracy of pseudorange measurement.

[0006] According to a first aspect of the present invention, a multipath pseudorange estimation method is provided, comprising: calculating the correlation values ​​of the sampling point closest to the optimal sampling point and the two sampling points before and after it on the multipath time-domain waveform diagram based on the received signal and the spreading pseudocode; and calculating the correlation values ​​of the multipath between two adjacent sampling points. Based on the correlation value between two adjacent sampling points of multipath, the correlation values ​​of the sampling point closest to the optimal sampling point and the two sampling points before and after it are corrected to obtain the corrected correlation value; the fractional delay error is calculated based on the corrected correlation value so as to perform pseudorange estimation based on the fractional delay error.

[0007] Optionally, in the above multipath pseudorange estimation method, the correlation value of the sampling point closest to the optimal sampling point and the two sampling points before and after it is calculated using the following formula: Where R is the correlation value, and the received signal is... The spreading pseudocode is N is the number of sampling points.

[0008] Optionally, in the above multipath pseudorange estimation method, the values ​​of the nearest sampling point and the two sampling points before and after the optimal sampling point are obtained based on the actual optimal sampling point value, chip period, sampling period and delay error; the values ​​of the nearest sampling point and the two sampling points before and after the optimal sampling point are substituted into the correlation value calculation formula to calculate the correlation values ​​of the first sampling point, the second sampling point, the third sampling point, the fourth sampling point and the fifth sampling point.

[0009] Optionally, in the above multipath pseudorange estimation method, the value of the first sampling point is... The value of the second sampling point is The value of the third sampling point The value of the fourth sampling point is The value of the fifth sampling point .

[0010] Optionally, in the above multipath pseudorange estimation method, the correlation value of the first sampling point is: ; The correlation value of the second sampling point is: ; The correlation value of the third sampling point is: ; The correlation value of the fourth sampling point is: ; The correlation value of the fifth sampling point is: ; Where N is the number of sampling points, A is the value of the actual optimal sampling point, and T is the chip period. The sampling period is For time delay error, This represents the power of the noise.

[0011] Optionally, in the above multipath pseudorange estimation method, the time delay error is calculated based on the correlation values ​​of the second, third, and fourth sampling points: .

[0012] Optionally, in the above multipath pseudorange estimation method, the correlation value between two adjacent sampling points of the multipath is calculated based on the following formula: in, , , , The correlation values ​​for the first, second, fourth, and fifth sampling points, and the multipath delay magnitude, are respectively. and .

[0013] Optionally, in the above multipath pseudorange estimation method, the correlation value between two adjacent sampling points is obtained by subtracting the correlation value between the sampling point closest to the optimal sampling point and the sampling points before and after it. The corrected correlation value is: ; Substituting the corrected correlation values ​​into the normalized time delay error formula, we get: .

[0014] According to a second aspect of the present invention, a computing device is provided, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions including instructions for performing the multipath pseudorange estimation method described above.

[0015] According to a third aspect of the present invention, a readable storage medium storing program instructions is provided, which, when read and executed by a computing device, causes the computing device to perform the multipath pseudorange estimation method described above.

[0016] According to the present invention, the correlation values ​​of multiple sampling points near the optimal sampling point are calculated by the modulus of the correlation coefficient between the received signal and the spread spectrum pseudocode. The correlation values ​​of the sampling points are then corrected based on the correlation values ​​between two adjacent sampling points under multipath conditions to obtain the corrected multipath fractional delay error. Pseudorange estimation is then performed based on the fractional delay error, which can improve the accuracy of UAV multipath pseudorange measurement in complex electromagnetic environments.

[0017] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic diagram of the sampling points on the time-domain waveform is shown; Figure 2 A flowchart illustrating a multipath pseudorange estimation method 200 according to an embodiment of the present invention is shown. Figure 3 A schematic diagram of the delay correlation peak point under multipath propagation according to an embodiment of the present invention is shown; Figure 4 A schematic diagram of the multipath lower code correlation peak sampling points is shown; Figure 5 This diagram illustrates the change in multipath correlation peak values ​​after delayed sampling. Figure 6 A structural diagram of a computing device 100 according to an embodiment of the present invention is shown. Detailed Implementation

[0019] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0020] Whether it is coherent ranging or incoherent ranging, both methods rely on the frame structure of the signal, sampling the signal at the beginning of the synchronization header to obtain pseudorange, and finally calculating the actual distance based on the pseudorange.

[0021] In the RTT-based round-trip time ranging method, the slave node sends a synchronization message starting from time slot q, which arrives at the master node after time tp. After receiving this message, the master node sends an acknowledgment synchronization message at the beginning of the next time slot q+1, which arrives at the slave node after time tp, at time t2. This represents the round-trip time difference. In the formula, c is the speed of light. L is the duration of a time slot, where L is the pseudorange. The time slots of the master and slave nodes are strictly aligned. However, in actual FGPA operation, due to the sampling clock frequency... Due to limitations, the accuracy of pseudorange measurement can only reach 1 / The magnitude is on the order of magnitude. To improve the accuracy of pseudorange measurements, it is necessary to estimate the pseudorange at a fraction of the sampling period.

[0022] Multipath delay error is one of the key indicators reflecting the quality of wireless channel communication. Matched correlation is a classic method for estimating multipath delay error, but its performance drops sharply as the relative delay of multipath paths decreases.

[0023] When using spread spectrum pseudocode for pseudorange estimation, the correlation values ​​at the optimal sampling point and three points on either side are used. Figure 1 A schematic diagram of the sampling points on the time-domain waveform is shown. For example... Figure 1 As shown, T is the chip period. The sampling period is .

[0024] Typically, the chip period T is the sampling period. It is 4 times or 8 times, therefore IPOINT=[4,8,,16...]. This is the time difference between the actual optimal sampling point and the optimal sampling point obtained during capture. If the actual optimal sampling point is earlier than the optimal sampling point obtained during capture, then... Negative, or vice versa positive (e.g.) Figure 1 (as shown in the image) The quantity that needs to be estimated.

[0025] If the received signal is The pseudocode is Then the expression for the relevant process is: Where n is the sampling point code, and if the actual optimal sampling point value is A, then Figure 1 middle The value of the point When 1 and -1 are uniformly distributed in the chip, half of the N sampled values ​​are this value, and half are A. The relevant values ​​of the point are: In the above formula This represents the power of the noise.

[0026] Similarly, we can obtain and The relevant values ​​for the points are as follows: The above three equations can be solved to obtain... The specific steps are as follows: Therefore, we can conclude that: For example, in a certain drone communication system, there is Then the normalized time difference is: The above analysis calculates the modulus square of the correlation value, which allows for direct calculation using the power value, avoiding the need for square root operations. However, power processing cannot be implemented for multipath channels.

[0027] This is because multipath effects can cause changes in signal delay and amplitude. To improve the accuracy of clock error estimation, this scheme provides a method for multipath pseudorange estimation by calculating correlation values ​​using the modulus.

[0028] Figure 2 A flowchart illustrating a multipath pseudorange estimation method 200 according to an embodiment of the present invention is shown. Figure 2 As shown, the method 200 begins with step S210, which calculates the correlation values ​​of the sampling point closest to the optimal sampling point and the two sampling points before and after it on the time-domain waveform diagram based on the received signal and the spread spectrum pseudocode.

[0029] It should be noted that in order to obtain the transmitted information data in a burst communication system, the signal must first be sampled at the symbol rate at the output of the matched filter in the demodulator. Therefore, accurately determining the sampling point is extremely important.

[0030] In practical applications, due to the low rate and high sampling rate of short-duration burst signals, timing estimation is generally performed by sampling the maximum average power. Based on the symmetrical power of the left and right halves of the matched filter, and utilizing the idea that accumulating multiple data streams can smooth noise, the sampling point corresponding to the minimum power difference between the left and right halves is the optimal sampling point.

[0031] If the received signal is The spreading pseudocode is The expression for the relevant process is: The values ​​of the nearest sampling point to the optimal sampling point and the two sampling points before and after it can be obtained based on the actual value of the optimal sampling point, the chip period, the sampling period, and the delay error.

[0032] Then, the values ​​of the nearest sampling point to the optimal sampling point and the two sampling points before and after it are substituted into the correlation value calculation formula to calculate the correlation values ​​of the first sampling point, the second sampling point, the third sampling point, the fourth sampling point, and the fifth sampling point.

[0033] Figure 3 A schematic diagram of the delay correlation peak points under multipath propagation according to an embodiment of the present invention is shown. Figure 3 As shown, the third sampling point P3 is the sampling point closest to the optimal sampling point, the first sampling point P1 and the second sampling point P2 are the two sampling points before the third sampling point P3, and the fourth sampling point P4 and the fifth sampling point P5 are the two sampling points after the third sampling point P3.

[0034] If the actual optimal sampling point value is A, then Figure 3 The value of the third sampling point P3 is: ((T⁄(2-)τ)A) / (T⁄2), Substituting the value of P3 into the correlation value calculation formula, we obtain the correlation value of the third sampling point P3: ; Accordingly, the value of the first sampling point can be calculated. The value of the second sampling point is The value of the fourth sampling point is The value of the first sampling point is .

[0035] Substituting the values ​​of the above sampling points into the correlation value calculation formula, the correlation value of the first sampling point P1 is obtained as follows: ; The correlation value of the second sampling point P2 is: ; The correlation value of the fourth sampling point P4 is: ; The P5 correlation value of the fifth sampling point is: ; Where N is the number of sampling points, A is the value of the actual optimal sampling point, and T is the chip period. The sampling period is For time delay error, This represents the power of the noise.

[0036] By combining the correlation values ​​from the second, third, and fourth sampling points, the fractional time delay error can be calculated: .

[0037] If the multipath paths in the system are more than one chip apart, their impact on determining the optimal sampling point for the correlation peak is minimal and can be ignored. If they are within one sampling point, they can also be ignored. This scheme focuses on multipath paths occurring within one chip but with an interval greater than one sampling point. This situation is actually quite common, hence its emphasis.

[0038] Figure 4 A schematic diagram of multipath lower code correlation peak sampling points is shown. For example... Figure 4 As shown, when IPOINT=8, the secondary path is delayed by 3 sampling points compared to the primary path. After multipath aggregation, the peak point shifts later, resulting in a longer distance for path discrimination. When there is only the primary path, the actual peak point is before the relevant peak point. Therefore, delayed sampling can be applied to multipaths.

[0039] Figure 5 A schematic diagram illustrating the change in multipath correlation peak values ​​after delayed sampling is shown. (For example...) Figure 5 As shown, if for Figure 4 In multipath sampling, a delay of 0.4 sampling points is sufficient to overlap with normal sampling points.

[0040] Therefore, considering the multipath delay magnitude and Then, the point closest to the optimal sampling point is used. and the two points before that , and the two points after that , The relevant value at that location can accurately estimate the distance value by a fraction of a factor.

[0041] Then, step S220 is executed to calculate the correlation value between two adjacent sampling points of the multipath.

[0042] Specifically, the correlation value between two adjacent sampling points of the multipath is calculated based on the following formula: in, , , , These are the correlation values ​​for the first, second, fourth, and fifth sampling points, respectively.

[0043] Next, step S230 is executed, which corrects the correlation values ​​of the sampling point closest to the optimal sampling point and the two sampling points before and after it based on the correlation values ​​between two adjacent sampling points of the multipath, and obtains the corrected correlation values.

[0044] That is, the point closest to the optimal sampling point. and the two points before and after it , Subtracting the multipath correlation value from the correlation value yields the corrected correlation value: ; Finally, step S240 is executed to calculate the fractional delay error based on the corrected correlation value, so as to perform pseudorange estimation based on the fractional delay error.

[0045] Specifically, the correlation value can be obtained by subtracting the correlation value between two adjacent sampling points from the correlation value of the sampling point closest to the optimal sampling point and the sampling points before and after it, resulting in the corrected correlation value: ; Then, substituting the corrected correlation values ​​into the normalized time delay error formula, we get: .

[0046] Figure 6 A structural diagram of a computing device 100 according to an embodiment of the present invention is shown. Figure 6 As shown, in the basic configuration 102, the computing device 100 typically includes system memory 106 and one or more processors 104. Memory bus 108 can be used for communication between processor 104 and system memory 106.

[0047] Depending on the desired configuration, processor 104 can be any type of processor, including but not limited to: microprocessors (µP), microcontrollers (µC), digital information processors (DSPs), or any combination thereof. Processor 104 may include one or more levels of cache such as L1 cache 110 and L2 cache 112, processor core 114, and registers 116. Example processor core 114 may include an arithmetic logic unit (ALU), a floating-point unit (FPU), a digital signal processing core (DSP core), or any combination thereof. Example memory controller 118 may be used with processor 104, or in some implementations, memory controller 118 may be an internal part of processor 104.

[0048] Depending on the desired configuration, system memory 106 can be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. Physical memory in a computing device typically refers to volatile RAM, and data on a disk needs to be loaded into physical memory before it can be read by processor 104. System memory 106 may include operating system 120, one or more applications 122, and program data 124.

[0049] In some implementations, application 122 may be configured to execute instructions on an operating system using program data 124 by one or more processors 104. The operating system 120 may be, for example, Linux, Windows, etc., and includes program instructions for handling basic system services and performing hardware-dependent tasks. Application 122 includes program instructions for implementing various user-desired functions; application 122 may be, for example, a browser, instant messaging software, software development tools (e.g., integrated development environment IDE, compiler, etc.), but is not limited thereto. When application 122 is installed in computing device 100, driver modules may be added to operating system 120.

[0050] When computing device 100 starts up, processor 104 reads and executes program instructions from memory 106 of operating system 120. Application 122 runs on operating system 120, utilizing interfaces provided by operating system 120 and underlying hardware to implement various user-expected functions. When user starts application 122, application 122 is loaded into memory 106, and processor 104 reads and executes program instructions from memory 106 of application 122.

[0051] The computing device 100 also includes a storage device 132, which includes a removable storage device 136 and a non-removable storage device 138, both of which are connected to a storage interface bus 134.

[0052] The computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to the basic configuration 102 via a bus / interface controller 130. Example output devices 142 include a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices such as displays or speakers via one or more A / V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboards, mice, pens, voice input devices, touch input devices) or other peripherals (e.g., printers, scanners, etc.) via one or more I / O ports 158. Example communication devices 146 may include a network controller 160, which may be arranged to facilitate communication with one or more other computing devices 162 via a network communication link through one or more communication ports 164.

[0053] A network communication link can be an example of a communication medium. A communication medium can typically be embodied in computer-readable instructions, data structures, program modules in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium. A “modulated data signal” can be a signal whose data set, or whose modifications, can be encoded with information in the signal. As a non-limiting example, a communication medium can include wired media such as wired networks or leased lines, and various wireless media such as sound, radio frequency (RF), microwave, infrared (IR), or other wireless media. The term “computer-readable medium” as used herein can include both storage media and communication media. In the computing device 100 according to the invention, application 122 includes instructions for performing the multipath pseudorange estimation method 200 of the invention.

[0054] According to the multipath pseudorange estimation method provided by the present invention, the correlation values ​​of multiple sampling points near the optimal sampling point are calculated by the modulus of the correlation coefficient between the received signal and the spread spectrum pseudocode. The correlation values ​​of the sampling points are corrected based on the correlation values ​​between two adjacent sampling points under multipath conditions, and the corrected multipath fractional delay error is obtained. Thus, pseudorange estimation is performed based on the fractional delay error, which can improve the accuracy of UAV multipath pseudorange measurement in complex electromagnetic environments.

[0055] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0056] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various aspects of the invention, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed invention requires more features than expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0057] Those skilled in the art will understand that modules, units, or components of the devices disclosed in the examples herein can be arranged in the devices described in this embodiment, or alternatively, can be located in one or more devices different from the devices in this example. The modules in the foregoing examples can be combined into a single module or, in addition, can be divided into multiple sub-modules.

[0058] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0059] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0060] Furthermore, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other means of performing functions. Therefore, a processor having the necessary instructions for implementing a method or method element forms means for implementing that method or method element. Furthermore, the elements described herein in the apparatus embodiments are examples of means for implementing functions performed by elements for the purposes of carrying out the invention.

[0061] As used herein, unless otherwise specified, the use of ordinal numbers such as “first,” “second,” “third,” etc., to describe ordinary objects merely indicates different instances of similar objects and is not intended to imply that the objects being described must have a given order in time, space, ordering, or any other manner.

[0062] Although the invention has been described with respect to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and edibility purposes, and not for the purpose of interpreting or limiting the subject matter of the invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the invention is illustrative rather than restrictive, and the scope of the invention is defined by the appended claims.

Claims

1. A multipath pseudorange estimation method, characterized in that, include: Based on the received signal and the spread spectrum pseudocode, the correlation values ​​of the sampling point closest to the optimal sampling point and the two sampling points before and after it on the multipath time-domain waveform are calculated. Calculate the correlation value between two adjacent sampling points of the multipath; Based on the correlation value between two adjacent sampling points of the multipath, the correlation value of the sampling point closest to the optimal sampling point and the two sampling points before and after it is corrected to obtain the corrected correlation value. Calculate the fractional delay error based on the corrected correlation value, so as to perform pseudorange estimation based on the fractional delay error; The step of calculating the correlation value between two adjacent sampling points of the multipath includes: The correlation value between two adjacent sampling points of the multipath is calculated based on the following formula: in, , , , The correlation values ​​for the first, second, fourth, and fifth sampling points, and the multipath delay magnitude, are respectively. and ; The step of calculating the fractional delay error based on the corrected correlation value includes: The corrected correlation value is obtained by subtracting the correlation value between two adjacent sampling points from the correlation value of the sampling point closest to the optimal sampling point and the sampling points before and after it. The corrected correlation value is: ； Substituting the corrected correlation values ​​into the normalized time delay error formula, we get: 。 2. The multipath pseudorange estimation method according to claim 1, characterized in that, The correlation values ​​of the sampling point closest to the optimal sampling point and the two sampling points before and after it are calculated using the following formula: Where R is the correlation value, and the received signal is... The spreading pseudocode is N is the number of sampling points.

3. The multipath pseudorange estimation method according to claim 2, characterized in that, The step of calculating the correlation values ​​of the sampling point closest to the optimal sampling point and the two sampling points before and after it on the time-domain waveform diagram based on the received signal and the spreading pseudocode includes: Based on the actual optimal sampling point value, chip period, sampling period, and delay error, the values ​​of the nearest sampling point to the optimal sampling point and the two sampling points before and after it are obtained. Substitute the values ​​of the nearest sampling point to the optimal sampling point and the two sampling points before and after it into the correlation value calculation formula to calculate the correlation values ​​of the first sampling point, the second sampling point, the third sampling point, the fourth sampling point, and the fifth sampling point.

4. The multipath pseudorange estimation method according to claim 3, characterized in that, The value of the first sampling point is The value of the second sampling point is The value of the third sampling point The value of the fourth sampling point is The value of the fifth sampling point .

5. The multipath pseudorange estimation method according to claim 4, characterized in that, The correlation value of the first sampling point is: ； The correlation value of the second sampling point is: ； The correlation value of the third sampling point is: ； The correlation value of the fourth sampling point is: ； The correlation value of the fifth sampling point is: ； Where N is the number of sampling points, A is the value of the actual optimal sampling point, and T is the chip period. The sampling period is For time delay error, This represents the power of the noise.

6. The multipath pseudorange estimation method according to claim 5, characterized in that, The method further includes: The fractional time delay error is calculated based on the correlation values ​​of the second, third, and fourth sampling points: 。 7. A computing device, comprising: At least one processor; and A memory storing program instructions configured to be executed by the at least one processor, the program instructions including instructions for performing the multipath pseudorange estimation method as described in any one of claims 1-6.

8. A readable storage medium storing program instructions that, when read and executed by a computing device, cause the computing device to perform the multipath pseudorange estimation method as described in any one of claims 1-6.

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