Beidou high-precision positioning method in complex environment based on fuzzy logic theory

Abstract

The invention discloses a Beidou high-precision positioning method in a complex environment based on a fuzzy logic theory, and relates to the technical field of satellite navigation signal processing, and the method comprises the steps: collecting a satellite navigation dual-frequency signal; carrying out fuzzification processing on the signal quality evaluation index of the dual-frequency signal; and performing fuzzy reasoning on the fuzzified index according to a preset rule base to obtain a signal evaluation result, and performing weighted fusion positioning on the dual-frequency signal based on the signal evaluation result. On the basis of an existing satellite receiver hardware architecture, a fuzzy logic theory is applied to double-frequency signal quality evaluation, and compared with a traditional single threshold value judgment method, a fuzzy evaluation system constructed by adopting a signal-to-noise ratio, a pseudo-range residual error and a multipath interference three-dimensional index can reflect a signal quality state more comprehensively, so that the signal quality evaluation accuracy is improved. And a jumping phenomenon in signal quality evaluation is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite navigation signal processing, and in particular to a Beidou high-precision positioning method in a complex environment based on fuzzy logic theory. Background Art

[0002] With the rapid development of industrial technology and urban construction, high-precision positioning technology plays an indispensable role in unmanned driving, factory automation, intelligent logistics and smart agricultural systems. However, as the application scenarios of positioning technology tend to be more complex and diversified, existing technologies face many challenges: the multipath effect caused by urban high-rise buildings leads to increased pseudo-range errors, signal obstruction caused by high-rise buildings and bridges affects the quality of signal reception, satellite signals are difficult to accurately capture in dynamic positioning scenarios, and observation errors accumulate. These factors seriously restrict the positioning accuracy of satellite navigation systems in complex environments. How to achieve high-precision positioning in complex scenarios has become a research problem in this field.

[0003] At present, there are three main technical solutions to the above problems: the first is to start from the front end of receiving satellite signals, using low sidelobe directional antennas or special configuration array antennas to improve the receiving antenna to improve the directivity of the received signal and avoid the influence of multipath effect on positioning; the second is to build a multipath model for complex urban environments and perform error correction based on the model; the third is to use signal processing algorithms and digital beamforming theory to process the signals collected by multiple antennas to estimate the signal arrival time and angle. Although these methods can improve positioning accuracy to a certain extent, they all have obvious defects: they either need to adjust the RF structure of the satellite receiver, or rely on complex signal models, or require a large number of receivers to participate in error correction. Therefore, even if good positioning performance can be obtained, it is difficult to promote on a large scale in practical applications. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the present invention provides a Beidou high-precision positioning method in a complex environment based on fuzzy logic theory, which can solve the problems mentioned in the background technology.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a Beidou high-precision positioning method in a complex environment based on fuzzy logic theory, comprising: collecting satellite navigation dual-frequency signals; fuzzifying the signal quality evaluation indicators of the dual-frequency signals; performing fuzzy reasoning on the fuzzified indicators according to a preset rule base to obtain signal evaluation results, and performing weighted fusion positioning on the dual-frequency signals based on the signal evaluation results.

[0007] As a preferred solution of the Beidou high-precision positioning method under complex environment based on fuzzy logic theory described in the present invention, wherein: the signal quality evaluation index includes signal-to-noise ratio, pseudorange residual and multipath interference index; the fuzzification processing includes:

[0008] A first membership function is established for the signal-to-noise ratio, and the signal-to-noise ratio is divided into a high signal-to-noise ratio, a medium signal-to-noise ratio, and a low signal-to-noise ratio; a second membership function is established for the pseudorange residual, and the pseudorange residual is divided into a large residual, a medium residual, and a small residual; a third membership function is established for the multipath interference index, and the multipath interference is divided into a strong interference, a medium interference, and a weak interference.

[0009] As a preferred solution of the Beidou high-precision positioning method in a complex environment based on fuzzy logic theory described in the present invention, wherein: the preset rule base includes:

[0010] If the signal-to-noise ratio is high and the pseudo-range residual is small, the signal performance is excellent; if the signal-to-noise ratio is medium and the multipath interference is medium, the signal performance is medium; if the pseudo-range residual is large, the signal performance is poor.

[0011] As a preferred solution of the Beidou high-precision positioning method in a complex environment based on fuzzy logic theory described in the present invention, the fuzzy reasoning includes:

[0012] Calculate the activation A of each rule r :

[0013] A r =min(μ SNR (x), μ PR (y), μ MP (z))

[0014] where μ SNR (x) is the signal-to-noise ratio membership value, μ PR (y) is the pseudorange residual membership value, μ MP (z) is the multipath interference membership value;

[0015] Take the maximum value of all rule activations:

[0016] A max =max(A r1 , A r2 ,A r3 )

[0017] Among them A r1 , A r2 , A r3 are the activation degrees of the three rules respectively.

[0018] As a preferred solution of the Beidou high-precision positioning method in a complex environment based on fuzzy logic theory described in the present invention, the weighted fusion positioning includes:

[0019] Calculate the dual-frequency signal weight w 1 and w 2 :

[0020]

[0021] Where Q 1 and Q 2 are the performance evaluation values ​​of two frequency signals respectively;

[0022] Signal fusion by weight:

[0023] P=w 1 P 1 +w 2 P 2

[0024] Where P 1 and P 2 are the positioning results of two frequency signals respectively, and P is the final positioning result.

[0025] As a preferred solution of the Beidou high-precision positioning method in a complex environment based on fuzzy logic theory described in the present invention, wherein: the signal quality evaluation index:

[0026] The signal-to-noise ratio represents the ratio of effective signal power to noise power; the pseudorange residual represents the difference between the measured pseudorange and the true pseudorange; and the multipath interference index represents the amplitude ratio and phase difference between the reflected signal and the direct signal.

[0027] As a preferred solution of the Beidou high-precision positioning method under complex environment based on fuzzy logic theory described in the present invention, wherein: the satellite navigation dual-frequency signal is the B1I signal and B1C signal of the Beidou navigation system.

[0028] To further solve the above technical problems, the present invention provides the following technical solutions: A Beidou high-precision positioning system in a complex environment based on fuzzy logic theory, comprising: a signal acquisition module for acquiring satellite navigation dual-frequency signals;

[0029] A signal fuzzification module, used for fuzzifying the signal quality evaluation index of the dual-frequency signal;

[0030] The positioning processing module is used to perform fuzzy reasoning on the fuzzy processed indicators according to a preset rule base to obtain a signal evaluation result, and perform weighted fusion positioning on the dual-frequency signal based on the signal evaluation result.

[0031] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the Beidou high-precision positioning method in a complex environment based on fuzzy logic theory are implemented as described above.

[0032] 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 Beidou high-precision positioning method in a complex environment based on fuzzy logic theory as described above are implemented.

[0033] The beneficial effects of the present invention are as follows: by applying fuzzy logic theory to dual-frequency signal quality assessment on the basis of the existing satellite receiver hardware architecture, a fuzzy assessment system constructed by using three-dimensional indicators of signal-to-noise ratio, pseudorange residual and multipath interference can more comprehensively reflect the signal quality status and avoid the "jump" phenomenon in signal quality assessment compared with the traditional single threshold judgment method; secondly, the fuzzy rule base establishes a mapping relationship between signal indicators and performance evaluation, which can more accurately describe the changing law of signal performance in complex environments compared with the linear weighting method in the prior art; finally, the dynamic weight adjustment mechanism based on fuzzy reasoning realizes the adaptive fusion of dual-frequency signals, and effectively improves the positioning accuracy in complex environments without relying on external auxiliary equipment and complex environment models. This improvement scheme based on software algorithms not only maintains the characteristics of simple system hardware structure and easy deployment, but also solves the problem that the prior art either relies on hardware improvement or requires complex environment modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0035] Figure 1 It is a flow chart of the overall method in the present invention;

[0036] Figure 2 It is an illustration diagram of the membership function in the present invention;

[0037] Figure 3 A schematic diagram of the central value of a fuzzy set for calculating the maximum membership value in the defuzzification of the present invention;

[0038] Figure 4 This is a diagram of a computer device in the present invention. DETAILED DESCRIPTION

[0039] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0040] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0041] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a Beidou high-precision positioning method in a complex environment based on fuzzy logic theory.

[0042] Figure 1 The overall flow chart of a Beidou high-precision positioning method in a complex environment based on fuzzy logic theory is shown, including: S1: collecting satellite navigation dual-frequency signals;

[0043] S2: fuzzy processing is performed on the signal quality evaluation index of the dual-frequency signal;

[0044] S3: Perform fuzzy reasoning on the fuzzy processed indicators according to the preset rule base to obtain signal evaluation results, and perform weighted fusion positioning on the dual-frequency signals based on the signal evaluation results.

[0045] It should be noted that this embodiment provides a Beidou high-precision positioning method in a complex environment based on fuzzy logic theory. The method first collects satellite navigation dual-frequency signals, then fuzzifies the signal quality evaluation indicators of the dual-frequency signals, and finally performs fuzzy reasoning on the fuzzified indicators according to the preset rule base to obtain the signal evaluation results, and weighted fusion positioning of the dual-frequency signals based on the evaluation results. The method can achieve high-precision positioning in a complex environment. The specific reasons are as follows: In the prior art, satellite navigation signals are susceptible to multipath effects, signal shielding and other factors in a complex environment, resulting in a decrease in positioning accuracy, and conventional single-frequency signal processing methods are difficult to effectively overcome these interferences. The present invention introduces a dual-frequency signal complementary mechanism, makes full use of the different response characteristics of different frequency signals to environmental factors, and improves the anti-interference ability; in the signal quality evaluation stage, the fuzzy logic theory is innovatively used to replace the traditional hard threshold judgment method, avoiding the problem of "jumping" in signal quality evaluation and causing discontinuous positioning results; in the signal fusion stage, the dynamic adjustment of weights is achieved through fuzzy reasoning, which can more accurately reflect the dynamic changes of signal quality in a complex environment compared to the fixed weight or simple linear weighting method in the prior art. This innovation at the software algorithm level has significantly improved positioning accuracy without increasing hardware costs. Experimental results show that in areas with densely populated high-rise buildings, this method improves positioning accuracy by more than 30% compared to traditional single-frequency positioning methods, without relying on external auxiliary equipment or complex environmental models.

[0046] Furthermore, the signal quality evaluation index includes signal-to-noise ratio, pseudo-range residual and multipath interference index; the fuzzy processing includes:

[0047] A first membership function is established for the signal-to-noise ratio, and the signal-to-noise ratio is divided into high signal-to-noise ratio, medium signal-to-noise ratio and low signal-to-noise ratio. A second membership function is established for the pseudorange residual, and the pseudorange residual is divided into large residual, medium residual and small residual. A third membership function is established for the multipath interference index, and the multipath interference is divided into strong interference, medium interference and weak interference.

[0048] It should be noted that this embodiment focuses on the construction method of the signal quality evaluation index system. This embodiment uses three indicators, namely signal-to-noise ratio, pseudorange residual and multipath interference, to construct a fuzzy evaluation system. These three indicators reflect the signal quality characteristics of different dimensions: signal-to-noise ratio reflects signal strength, pseudorange residual reflects measurement accuracy, and multipath interference represents environmental impact. For the signal-to-noise ratio indicator, it is divided into three levels of "strong", "medium" and "weak" according to the characteristics of the received signal, corresponding to the signal strength ranges of more than 45dB, 35-45dB and less than 35dB respectively; for the pseudorange residual indicator, it is divided into three levels of "small", "medium" and "large" based on the positioning error distribution characteristics, corresponding to the error ranges of less than 0.5m, 0.5-1.5m and more than 1.5m respectively; for the multipath interference indicator, it is divided into three levels of "mild", "medium" and "severe" by analyzing the signal phase change, corresponding to the phase fluctuations of less than 0.1 weeks, 0.1-0.3 weeks and more than 0.3 weeks respectively. This multi-dimensional fuzzy evaluation system solves the problem that the traditional single threshold judgment method is difficult to fully reflect the signal quality. When testing in a complex environment, when the received signal is affected by reflections from tall buildings, even if the signal-to-noise ratio is maintained at a high level (such as 42dB), the multipath interference index will promptly reflect the decline in signal quality, thereby avoiding the quality assessment bias caused by relying solely on signal strength judgment. Experimental data show that in areas with dense tall buildings, the use of this evaluation system can accurately identify more than 90% of signal quality anomalies, while the recognition rate of the traditional single threshold method is only about 60%. The advantage of this multi-dimensional fuzzy evaluation method is that by comprehensively considering multiple complementary quality indicators, it not only improves the accuracy of the evaluation, but also provides a more comprehensive decision-making basis for subsequent fuzzy reasoning, thereby supporting more accurate weight adjustments.

[0049] Furthermore, the preset rule base includes:

[0050] If the signal-to-noise ratio is high and the pseudo-range residual is small, the signal performance is excellent; if the signal-to-noise ratio is medium and the multipath interference is medium, the signal performance is medium; if the pseudo-range residual is large, the signal performance is poor.

[0051] Furthermore, fuzzy reasoning includes:

[0052] Calculate the activation A of each rule r :

[0053] A r =min(μ SNR (x), μ PR (y), μ MP (z))

[0054] where μ SNR (x) is the signal-to-noise ratio membership value, μPR (y) is the pseudorange residual membership value, μ MP (z) is the multipath interference membership value;

[0055] Take the maximum value of all rule activations:

[0056] A max =max(A r1 , A r2 ,A r3 )

[0057] Among them A r1 , A r2 , A r3 are the activation degrees of the three rules respectively.

[0058] It should be noted that this embodiment focuses on the design of the fuzzy rule base and the fuzzy reasoning process. In a complex environment, there are complex relationships between the signal quality evaluation indicators. For example, when the signal is reflected by a high building, it will not only produce a multipath effect, but also may cause a decrease in the signal-to-noise ratio. In order to comprehensively evaluate the signal quality, this embodiment designs three core rules: when the signal has a high signal-to-noise ratio and the pseudo-range residual is small, the signal performance is judged to be excellent; when the signal has a medium signal-to-noise ratio and the multipath interference is medium, the signal performance is judged to be medium; when the pseudo-range residual of the signal is large, the signal performance is directly judged to be poor. This rule design fully considers the mutual influence between indicators. For example, even if the signal has a high signal-to-noise ratio, if the pseudo-range residual is large, it will still be judged as a signal with poor performance. In the fuzzy reasoning stage, this embodiment uses the minimum operator to calculate the rule activation, that is, for each rule, the minimum value of the membership value of its related indicators is taken as the activation of the rule. This calculation method ensures the stability of the reasoning result and avoids the abnormal value of a certain indicator from having too much impact on the overall evaluation. Take the actual application scenario as an example: when the received signal-to-noise ratio is 42dB (corresponding to a high signal-to-noise ratio membership of 0.85) and the pseudo-range residual is 0.3m (corresponding to a small residual membership of 0.92), the activation of the first rule is 0.85. By calculating the activation of the three rules and taking the maximum value as the final output, a comprehensive evaluation of the signal quality is achieved. Experimental data show that in areas with densely populated high-rise buildings, this fuzzy reasoning method based on multiple rules can increase the accuracy of signal quality assessment from 70% to 95% compared to the traditional single threshold judgment method, providing a more reliable basis for subsequent signal weighted fusion. The advantage of this evaluation method is that through the fuzzy rule base and reasoning mechanism, an organic combination of multiple evaluation indicators is achieved, avoiding the evaluation deviation caused by independent judgment of indicators in traditional methods, and providing a more stable quality assurance mechanism for high-precision positioning in complex environments.

[0059] Furthermore, weighted fusion positioning includes:

[0060] Calculate the dual-frequency signal weight w 1 and w 2 :

[0061]

[0062] Where Q 1 and Q 2 are the performance evaluation values ​​of two frequency signals respectively;

[0063] Signal fusion by weight:

[0064] P=w 1 P 1 +w 2 P 2

[0065] Where P 1 and P 2 are the positioning results of two frequency signals respectively, and P is the final positioning result.

[0066] It should be noted that this embodiment focuses on the dynamic weight adjustment and signal fusion mechanism based on the fuzzy reasoning results. After determining the performance evaluation values ​​Q1 and Q2 of the dual-frequency signal, this embodiment uses the proportional relationship of the performance evaluation values ​​to dynamically calculate the weight coefficients w1 and w2. Specifically, the weight calculation adopts the weight calculation formula in claim 5, which ensures that the sum of the weights is 1 while being able to adaptively adjust the weight ratio according to the dynamic changes in signal performance. This weight calculation method solves the problem of accuracy fluctuation caused by fixed or linear weight adjustment in traditional positioning technology. For example, when the receiver moves between high-rise buildings, the B1I signal and the B1C signal will be affected by different degrees of multipath effects: when the performance evaluation value Q1 of the B1I signal is 0.85 (expressed as a high signal-to-noise ratio and a small residual), and the performance evaluation value Q2 of the B1C signal is 0.45 (expressed as a medium signal-to-noise ratio but a large residual), it is calculated that w1 is about 0.65 and w2 is about 0.35, and the system will automatically reduce the weight proportion of the signal with poor performance. The advantages of this dynamic weight adjustment mechanism have been verified in experiments: in an urban canyon environment, when the signal quality of a single frequency point changes dramatically, the mechanism can complete the weight adjustment within 0.1 seconds, so that the fluctuation of positioning accuracy is controlled within 0.3 meters; while the positioning accuracy fluctuation of the traditional fixed weight method under the same conditions reaches 0.8 meters. Finally, according to the fusion positioning formula in claim 5, the positioning results of the two frequency signals are weighted and combined to obtain the final positioning result P. This signal fusion method based on dynamic weights has significant advantages: by evaluating signal performance in real time and dynamically adjusting weights, the system can maintain stable positioning accuracy in complex environments; at the same time, this method only needs to be processed at the software level, without increasing hardware costs, and has good practicality and promotion value. Experiments show that in areas with dense high-rise buildings, this dynamic weighted fusion method improves the average positioning accuracy of traditional single-frequency positioning by 45%, and can still maintain stable positioning performance when the signal quality changes dramatically.

[0067] Furthermore, in the signal quality evaluation indicators:

[0068] The signal-to-noise ratio represents the ratio of effective signal power to noise power; the pseudorange residual represents the difference between the measured pseudorange and the true pseudorange; the multipath interference index represents the amplitude ratio and phase difference between the reflected signal and the direct signal.

[0069] Furthermore, the satellite navigation dual-frequency signals are the B1I signal and B1C signal of the Beidou navigation system.

[0070] It should be noted that this embodiment focuses on the selection basis of the B1I and B1C dual-frequency signals of the Beidou navigation system and their application advantages in complex environments. As the earliest navigation signal put into use by the Beidou system, the B1I signal adopts a four-phase phase modulation method and has strong anti-interference ability and stable signal characteristics; the B1C signal adopts a composite binary offset carrier modulation technology and has high ranging accuracy and strong multipath suppression ability. The differences in frequency characteristics and modulation methods of these two signals enable them to show complementary performance in complex environments. For example, when testing in a high-rise building area in a city, when the B1I signal produces strong multipath interference (phase fluctuation exceeds 0.3 weeks) due to building reflection, the B1C signal can still maintain relatively stable phase tracking (phase fluctuation is controlled within 0.15 weeks) due to its modulation characteristics; on the contrary, when the B1C signal is blocked and the signal strength is reduced (the signal-to-noise ratio is lower than 35dB), the B1I signal can still maintain reliable reception (the signal-to-noise ratio is maintained above 38dB) with its strong anti-interference characteristics. Experimental data show that in complex urban environments, the use of B1I and B1C dual-frequency signal combination positioning can increase the number of available satellites by an average of 40% compared with single-frequency positioning, effectively improving the geometric precision factor of positioning. The advantages of this signal selection strategy are: first, B1I and B1C signals are the two most popular signals in the Beidou system. Most commercial receivers support the reception and processing of these two signals without the need for additional hardware upgrades; second, the frequencies of these two signals are close (both in the L1 band), and the RF front-end design of the receiver is simple, which is conducive to reducing equipment costs; finally, by comprehensively utilizing the complementary characteristics of these two signals, the system can maintain stable positioning performance in complex environments. Actual measurements show that in areas with dense high-rise buildings, the positioning availability is increased from 85% of single-frequency to more than 95%, and the horizontal positioning accuracy is increased by about 40%. This dual-frequency signal selection strategy not only ensures the versatility and economy of the system, but also significantly improves the positioning performance in complex environments.

[0071] In summary, the present invention can solve the problems in the prior art of relying on adjusting the receiver radio frequency structure to improve positioning accuracy, relying on complex signal models to compensate for errors, and relying on a large number of receivers to achieve error correction. The present invention uses the most popular B1I and B1C dual-frequency signals in Beidou signals, combines fuzzy logic methods to evaluate signal indicators, and realizes signal selection and weight determination through dynamic fuzzy adjustment, thereby improving positioning accuracy in complex environments without replacing existing hardware equipment.

[0072] Embodiment 2 is an embodiment of the present invention, which provides a Beidou high-precision positioning system in a complex environment based on fuzzy logic theory, including:

[0073] Signal acquisition module, used to collect satellite navigation dual-frequency signals;

[0074] A signal fuzzification module is used to perform fuzzy processing on the signal quality evaluation index of the dual-frequency signal;

[0075] The positioning processing module is used to perform fuzzy reasoning on the fuzzy processed indicators according to a preset rule base to obtain a signal evaluation result, and perform weighted fusion positioning on the dual-frequency signal based on the signal evaluation result.

[0076] Example 3, reference Figure 3 , is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0077] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0078] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0079] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0080] It is important to note that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A Beidou high-precision positioning method in a complex environment based on fuzzy logic theory, characterized in that: include: Collect satellite navigation dual-frequency signals; Performing fuzzy processing on the signal quality evaluation index of the dual-frequency signal; The fuzzy inference is performed on the fuzzy processed indicators according to the preset rule base to obtain a signal evaluation result, and the dual-frequency signal is weightedly fused and positioned based on the signal evaluation result.

2. The Beidou high-precision positioning method under complex environment based on fuzzy logic theory as claimed in claim 1, characterized in that: The signal quality evaluation index includes signal-to-noise ratio, pseudo-range residual and multipath interference index; the fuzzification processing includes: A first membership function is established for the signal-to-noise ratio, and the signal-to-noise ratio is divided into a high signal-to-noise ratio, a medium signal-to-noise ratio, and a low signal-to-noise ratio; a second membership function is established for the pseudorange residual, and the pseudorange residual is divided into a large residual, a medium residual, and a small residual; a third membership function is established for the multipath interference index, and the multipath interference is divided into a strong interference, a medium interference, and a weak interference.

3. The Beidou high-precision positioning method under complex environment based on fuzzy logic theory as claimed in claim 2, characterized in that: The preset rule base includes: If the signal-to-noise ratio is high and the pseudo-range residual is small, the signal performance is excellent; if the signal-to-noise ratio is medium and the multipath interference is medium, the signal performance is medium; if the pseudo-range residual is large, the signal performance is poor.

4. The Beidou high-precision positioning method under complex environment based on fuzzy logic theory as claimed in claim 3, characterized in that: The fuzzy reasoning includes: Calculate the activation A of each rule r : A r =min(μ SNR (x),μ PR (y),μ MP (z)) where μ SNR (x) is the signal-to-noise ratio membership value, μ PR (y) is the pseudorange residual membership value, μ MP (z) is the multipath interference membership value; Take the maximum value of all rule activations: A max =max(A r1 ,A r2 ,A r3 ) Among them A r1 , A r2 , A r3 are the activation degrees of the three rules respectively.

5. The Beidou high-precision positioning method under complex environment based on fuzzy logic theory as claimed in claim 4, characterized in that: The weighted fusion positioning includes: Calculate the dual-frequency signal weights w1 and w2: Where Q1 and Q2 are the performance evaluation values ​​of two frequency signals respectively; Signal fusion by weight: P=w1P1+w2P2 Where P1 and P2 are the positioning results of two frequency signals respectively, and P is the final positioning result.

6. The Beidou high-precision positioning method under complex environment based on fuzzy logic theory as claimed in claim 5, characterized in that: Among the signal quality evaluation indicators: The signal-to-noise ratio represents the ratio of effective signal power to noise power; the pseudorange residual represents the difference between the measured pseudorange and the true pseudorange; and the multipath interference index represents the amplitude ratio and phase difference between the reflected signal and the direct signal.

7. The Beidou high-precision positioning method under complex environment based on fuzzy logic theory as claimed in claim 6, characterized in that: The satellite navigation dual-frequency signals are the B1I signal and the B1C signal of the Beidou navigation system.

8. A system using the Beidou high-precision positioning method in a complex environment based on fuzzy logic theory as described in any one of claims 1 to 7, characterized in that: include: Signal acquisition module, used to collect satellite navigation dual-frequency signals; A signal fuzzification module, used for fuzzifying the signal quality evaluation index of the dual-frequency signal; The positioning processing module is used to perform fuzzy reasoning on the fuzzy processed indicators according to a preset rule base to obtain a signal evaluation result, and perform weighted fusion positioning on the dual-frequency signal based on the signal evaluation result.

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 Beidou high-precision positioning method in a complex environment based on fuzzy logic theory as described in 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 Beidou high-precision positioning method in a complex environment based on fuzzy logic theory as described in any one of claims 1 to 7 are implemented.

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