Positioning method based on maximum likelihood estimation and pseudorange residual compensation

Abstract

The present invention provides a positioning method based on maximum likelihood estimation and pseudorange residual compensation, comprising the following steps: step S1, acquiring channel state information (CSI); step S2, on the basis of a maximum likelihood measurement method and a minimum description length criterion, determining the number of multiple paths and corresponding parameters of the multiple paths on the basis of the CSI; step S3, determining initial positioning coordinates on the basis of the number of multiple paths and the parameters of the multiple paths, and then calculating a pseudorange residual; and step S4, calculating terminal positioning coordinates on the basis of the initial positioning coordinates and the pseudorange residual. The positioning method based on maximum likelihood estimation and pseudorange residual compensation provided by the present invention has lower calculation complexity, stronger applicability, more accurate terminal positioning calculation and high positioning accuracy.

Description

Positioning method based on maximum likelihood estimation and pseudo-range residual compensation TECHNICAL FIELD

[0001] The present application belongs to the field of positioning and navigation, and particularly relates to a positioning method based on maximum likelihood estimation and pseudo-range residual compensation. BACKGROUND

[0002] Unlike the global navigation positioning system in an open outdoor environment, in a non-line-of-sight environment where obstacles exist in the indoor environment, the arrival signal propagates along the non-line-of-sight. Due to the existence of buildings, terrain and other obstacles, the radio signal may experience reflection, refraction or scattering in the propagation process, resulting in attenuation of signal strength and deviation of propagation path. This phenomenon is particularly evident in urban canyons, indoor environments and other complex terrains, which seriously affects the communication quality and positioning accuracy. Non-line-of-sight propagation will cause more dramatic changes in signal strength over time and space, which makes the signal strength unstable, causing signal power loss and communication quality degradation. In the field of positioning and navigation, non-line-of-sight phenomenon will greatly reduce the accuracy and reliability of calculating the position, so it is necessary to estimate the channel parameters with a suitable method to maintain the overall positioning performance.

[0003] In 5G, due to the improvement of its transmission performance, 5G-based positioning is achievable. However, affected by the non-line-of-sight propagation effect, especially in indoor environments, non-line-of-sight phenomenon becomes an important bottleneck limiting the positioning capability of 5G. In most scenarios, the ranging error caused by non-line-of-sight phenomenon is much larger than the error of the ranging capability itself. Based on the above, the present application provides a technical solution to solve the above technical problems. SUMMARY

[0004] For the scenario in the prior art where the non-line-of-sight phenomenon in the 5G scenario causes the pseudo-range residual to affect the positioning accuracy and accuracy, the present application provides a positioning method based on maximum likelihood estimation and pseudo-range residual compensation, comprising the following steps:

[0005] Step S1, acquiring channel state information (CSI);

[0006] Step S2, determining the number of multipaths and corresponding multipath parameters according to the CSI based on the maximum likelihood measurement method and the minimum description length criterion;

[0007] Step S3, determining the initial positioning coordinates according to the number of multipaths and the multipath parameters, and then calculating the pseudo-range residual;

[0008] Step S4, calculating the terminal positioning coordinates according to the initial positioning coordinates and the pseudo-range residual.

[0009] In one specific embodiment of the present application, step S2 comprises:

[0010] Step S2.1, after the desired cycle resolution, calculate each complete data set and parameter estimates;

[0011] Step S2.2, based on the complete data set to construct the maximum likelihood function, and the strongest signal intensity in the complete data set to the arrival path is the lth path, l is a positive integer and l∈[1, L];

[0012] Step S2.3, based on the minimum description length criterion to detect the number of multipath L and multipath parameters.

[0013] In one embodiment of the present application, step S2.1 includes: received superimposed signal Y(f) subtracted from all paths before the lth path, the complete parameter of the lth path is obtained Wherein, Indicates the parameter estimate of the lth path, Is the parameter estimate of the l'th path, Indicates the complete data set estimate of the lth path.

[0014] In one embodiment of the present application, step S2.2 includes: constructing a maximum likelihood function according to the complete data set and maximizing the function,

[0015] Wherein, Θ l The parameter set of the lth path, The complete data set of the lth path, The likelihood function about pseudo-range parameters, Y is the channel state information, L is the number of multipath, σ is the standard deviation of additive white Gaussian noise.

[0016] In one embodiment of the present application, step S2.3 includes: minimum description length criterion detection model includes:

[0017] Wherein, X(f; Θ l ) is the complete data set of the lth path, L is the number of multipath, K is the maximum number of multipath limits, α L L is the penalty function, α is the penalty function coefficient, σ L Is the noise standard deviation.

[0018] In one embodiment of the present application, the number of multipath L is calculated when the MDL reaches the minimum value, which specifically includes:

[0019] If the minimum description length criterion detection model reaches the minimum value when L-1, output the current number of multipath L and stop iteration;

[0020] Otherwise, let L = L + 1, repeat step S2.1 and step S2.2.

[0021] In one embodiment of the present application, step S3 comprises: after obtaining the initial position by using the weighted least squares method, the estimated coordinates are obtained by bringing into the maximum likelihood stage of Gauss-Newton:

[0022] Wherein, B is the number of remote radio units RRU, σ b is the standard deviation of the measured pseudo-range of the bth RRU, x is the estimated terminal coordinates, x b is the coordinates of the bth RRU, is the pseudo-range residual of the i-th iteration, is the maximum likelihood function, p b is the pseudo-range of the first path received by the bth base station BS relative to the terminal, b is a positive integer and is the pseudo-range residual of the bth base station BS.

[0023] In one embodiment of the present application, step S4 comprises:

[0024] Step S4.1, calculating the pseudo-range residual: Wherein, is the estimated position of the terminal UE in the i-th iteration;

[0025] Step S4.2, judging whether the UE estimated position converges, if the UE estimated position converges, the UE estimated position is the terminal positioning coordinates, and the terminal positioning is output, if the UE estimated position does not converge, returning to step S3, iterating until the UE estimated position converges.

[0026] In one embodiment of the present application, before the loop decomposition expectation, it further comprises:

[0027] The multi-path parameters are initialized by using the successive interference cancellation method.

[0028] In one embodiment of the present application, step S1 comprises: the positioning server requires the baseband processing unit BBU of the 5G distributed base station to open a data interface, acquires the CSI of the channel sounding reference signal SRS corresponding to each remote radio unit RRU via the data interface, and transmits the CSI of the SRS to the positioning server before the RRU radio frequency combining.

[0029] The present application can bring at least one of the following beneficial effects: the present application provides a positioning method based on maximum likelihood estimation and pseudo-range residual compensation, based on maximum likelihood estimation method and minimum description length criterion, the number of multipath and parameters are calculated through channel state information CSI, then the initial positioning coordinates and pseudo-range residual are calculated, and the pseudo-range residual result is compensated to determine the terminal positioning coordinates, solving the defect that the positioning or ranging result appears error due to pseudo-range residual caused by non-line-of-sight phenomenon in the prior art indoor scene, the positioning method based on maximum likelihood estimation and pseudo-range residual compensation provided by the present application does not need pre-training model, the calculation complexity is lower, can be widely applied to non-line-of-sight scene of 5G, has stronger applicability, and the terminal positioning calculation is more accurate and has high positioning accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0030] The above characteristics, technical features, advantages and implementation modes will be further described in the following in a clear and understandable manner in combination with the preferred embodiments and the accompanying drawings.

[0031] Fig. 1 is a step schematic diagram of a positioning method based on maximum likelihood estimation and pseudo-range residual compensation provided by an embodiment of the present application;

[0032] Fig. 2 is a positioning system structure schematic diagram in a positioning method based on maximum likelihood estimation and pseudo-range residual compensation provided by an embodiment of the present application;

[0033] Fig. 3 is a cumulative distribution function comparison schematic diagram of whether to compensate the pseudo-range residual by the positioning method based on maximum likelihood estimation and pseudo-range residual compensation provided by the present application. DETAILED DESCRIPTION

[0034] The various aspects of the present application are further described in detail below.

[0035] Unless otherwise defined or specified, all professional and scientific terms used in the present application have the same meaning as familiar to those skilled in the art. In addition, any method and material similar or equivalent to those described can be applied in the present application.

[0036] The terms are described below.

[0037] Unless otherwise explicitly specified and limited, the "or" in the present application includes the "and". The "and" corresponds to the Boolean logical operator "AND", the "or" corresponds to the Boolean logical operator "OR", and the "AND" is a subset of the "OR".

[0038] It will be understood that, although the terms "first", "second", etc. can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. Thus, a first element could be termed a second element without departing from the teachings of the present inventive concept.

[0039] In the present invention, the terms "comprising", "containing" or "including" mean that various elements can be used in the mixture or composition of the present invention together. Therefore, the term "consisting essentially of is included in the term "comprising", "containing" or "including".

[0040] Unless otherwise defined and limited, the terms "connected", "coupled", "connected" of the present invention should be interpreted broadly, for example, can be fixedly connected, or connected through an intermediate medium, can be internal connection of two elements or interaction relationship of two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0041] For example, if an element (or component) is referred to as being "on", "coupled with" or "connected with" another element, it can be directly formed on, coupled with or connected with the other element, or one or more intermediate elements can be provided between them. In contrast, if the expressions "directly on", "directly coupled with" and "directly connected with" are used herein, it means that there is no intermediate element. Other words used to describe the relationship between elements should be similarly interpreted, such as "between" and "directly between", "attached" and "directly attached", "adjacent" and "directly adjacent", etc.

[0042] In addition, it should be noted that the words "front", "back", "left", "right", "up" and "down" used in the following description refer to the directions in the drawings. The words "inner" and "outer" are used to refer to the direction towards or away from the geometric center of a particular component. It can be understood that these terms are used herein to describe the relationship of one element, layer or region with respect to another element, layer or region as shown in the drawings. In addition to the orientation described in the drawings, these terms should also include other orientations of the device.

[0043] Other aspects of the present invention will be apparent to those skilled in the art from the disclosure herein.

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, specific implementations of the present application will be described below with reference to the drawings. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained from these drawings without creative labor, and other embodiments can also be obtained.

[0045] It should also be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner, and only the components related to the present application are shown in the diagrams, not the number, shape and size of the components when actually implemented. The actual implementation of each component may be a random change in shape, number and proportion, and the component layout pattern may also be more complex. For example, the thickness of the elements in the drawings can be exaggerated for clarity.

[0046] Embodiments

[0047] For the scene in which the pseudorange residual caused by the non-line-of-sight phenomenon in the prior art 5G scenario affects the positioning accuracy and accuracy, referring to FIG. 1, the present application provides a positioning method based on maximum likelihood estimation and pseudorange residual compensation, comprising the following steps:

[0048] Step S1, acquiring channel state information CSI;

[0049] Step S2, determining the number of multipaths and corresponding multipath parameters according to the CSI based on the maximum likelihood measurement method and the minimum description length criterion;

[0050] Step S3, determining the initial positioning coordinates according to the number of multipaths and the multipath parameters, and then calculating the pseudorange residual;

[0051] Step S4, calculating the actual terminal positioning coordinates according to the initial positioning coordinates and the pseudorange residual.

[0052] In a preferred embodiment of the present application, referring to the structural schematic diagram of the positioning system shown in FIG. 2, step S1 includes: the positioning server requiring the baseband processing unit BBU of the 5G distributed base station to open a data interface, acquiring the CSI of the channel sounding reference signal SRS corresponding to each remote radio unit RRU via the data interface, and transmitting the CSI of the SRS to the positioning server before the RRU radio frequency combines.

[0053] Wherein, the 5G distributed base station includes a plurality of remote radio units RRU, a transmission center HUB and a baseband processing unit BBU, the RRU is connected with the BBU through a plurality of transmission centers HUB, and the BBU is connected with the 5G core network, the positioning server and the application program.

[0054] Preferably, the 5G distributed base station comprises a BBU.

[0055] In a preferred embodiment of the present application, the step S2 comprises:

[0056] Step S2.1, initializing the multipath parameters by using a successive interference cancellation method, after the loop decomposition of the desired signal, calculating the perfect data set and parameter estimation value of each path: subtracting all paths before the lth path from the received superimposed signal Y(f) to obtain the perfect parameter of the lth path wherein, represents the parameter estimation value of the lth path, is the parameter estimation value of the l'th path, represents the perfect data set estimation value of the lth path.

[0057] Step S2.2, constructing a maximum likelihood function based on the perfect data set, and taking the path with the strongest signal strength in the perfect data set as the lth path, l is a positive integer and l∈[1,L]: constructing a maximum likelihood function based on the perfect data set and maximizing the function,

[0058] wherein, Θ l is the parameter set of the lth path, is the perfect data set of the lth path, is the likelihood function about the pseudo-range parameter, Y is the channel state information, L is the number of multipaths, and σ is the standard deviation of the additive white Gaussian noise.

[0059] Step S2.3, detecting the number L of multipaths and the multipath parameters based on the minimum description length criterion: the minimum description length criterion detection model comprises:

[0060] wherein, X(f; Θ l ) is the perfect data set of the lth path, L is the number of multipaths, is the number of multipaths corresponding to the minimum value of the minimum description length criterion detection model, K is the maximum number of multipaths limited, and α L L is the penalty function, α is the penalty function coefficient, and σ L is the noise standard deviation.

[0061] Further, the calculation of the number L of multipaths when the MDL reaches the minimum value specifically comprises:

[0062] If the minimum description length criterion detection model reaches the minimum value when L-1, output the current number L of multipaths and stop iteration;

[0063] Otherwise, let L=L+1, repeat step S2.1 and step S2.2.

[0064] In a preferred embodiment of the present application, step S3 comprises: after the initial position is obtained by using the weighted least square method, the estimated coordinates are obtained by using the maximum likelihood stage of Gauss-Newton:

[0065] wherein B is the number of remote radio units RRU, σ b is the standard deviation of the measured pseudo-range of the bth RRU, x is the estimated terminal coordinates, x b is the coordinates of the bth RRU, is the pseudo-range residual error of the ith iteration, is the maximum likelihood function, ρ b is the pseudo-range of the first path received by the bth base station BS relative to the terminal, b is a positive integer and is the pseudo-range residual error of the bth base station BS.

[0066] In a specific embodiment of the present application, step S4 comprises:

[0067] Step S4.1, calculating the pseudo-range residual error: wherein, is the estimated position of the terminal UE of the ith iteration;

[0068] Step S4.2, judging whether the estimated position of the UE converges, if the estimated position of the UE converges, the estimated position of the UE is the terminal positioning result, and the terminal positioning is output, if the estimated position of the UE does not converge, returning to step S3, and iterating until the estimated position of the UE converges.

[0069] It should be understood that the pseudo-range residual error refers to the error between the measured pseudo-range and the true distance due to non-line-of-sight. FIG. 3 shows a comparison chart of error cumulative distribution functions generated by whether the positioning result is compensated for the pseudo-range residual error, wherein the red curve represents the error cumulative distribution function graph with the pseudo-range residual error compensation step, and the green curve represents the error cumulative distribution function graph without the pseudo-range residual error compensation step. As shown in FIG. 3, by using the positioning method based on the maximum likelihood estimation and the pseudo-range residual error compensation provided by the present application, the measurement error in positioning can be minimized.

[0070] In summary, the present application has the following effects:

[0071] The application provides a positioning method based on maximum likelihood estimation and pseudo-range residual compensation.

[0072] Based on the present application, those skilled in the art will appreciate that one aspect described herein can be implemented independently of any other aspects and that some aspects can be implemented independently of each other. For example, an apparatus can be implemented and / or a method can be practiced using any number of the aspects described herein. In addition, an apparatus can be implemented and / or a method can be practiced using other structure and / or functionality in addition to or other than one or more of the aspects described herein.

[0073] Those skilled in the art know that, in addition to implementing the system provided by the present application and each device, module and unit thereof in a pure computer readable program code manner, the system provided by the present application and each device, module and unit thereof can also be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers, etc. by logically programming the method steps to achieve the same functions. Therefore, the system provided by the present application and each device, module and unit thereof can be considered as a hardware component, and the devices, modules and units included therein for achieving various functions can also be considered as structures within the hardware component. The devices, modules and units for achieving various functions can also be considered as both software modules for implementing methods and structures within hardware components.

[0074] It should be noted that the above embodiments can be freely combined according to needs. The above descriptions are only preferred embodiments of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

[0075] All documents mentioned in the present application are cited as references in the present application, just as each document is cited as a reference. In addition, it should be understood that those skilled in the art can make various modifications or improvements to the present application after reading the above description of the present application, and these equivalent forms also fall within the scope of the claims attached to the present application.

Claims

1. A positioning method based on maximum likelihood estimation and pseudorange residual compensation, characterized in that, The method comprises the following steps: Step S1, acquiring channel state information (CSI); Step S2, determining the number of multipaths and corresponding multipath parameters according to the CSI based on a maximum likelihood measurement method and a minimum description length criterion; Step S3, determining an initial positioning coordinate according to the number of multipaths and the multipath parameters, and then calculating a pseudo-range residual; Step S4, calculating a terminal positioning coordinate according to the initial positioning coordinate and the pseudo-range residual. 2.The positioning method based on maximum likelihood estimation and pseudo-range residual compensation of claim 1, wherein, Step S2 comprises: Step S2.1, after cyclically decomposing the expected, calculating each complete data set and parameter estimation value; Step S2.2, constructing a maximum likelihood function based on the complete data set, and taking the path with the strongest signal strength in the complete data set as the lth path, where l is a positive integer and l∈[1, L]; Step S2.3, detecting the number of multipaths L and the multipath parameters based on a minimum description length criterion. 3.The positioning method based on maximum likelihood estimation and pseudo-range residual compensation of claim 2, characterized in that, Step S2.1 comprises: subtracting all paths before the lth path from the received superposition signal Y(f) to obtain the perfect parameter of the lth path wherein, represents the parameter estimate of the lth path, is the parameter estimate of the l'th path, represents the perfect data set estimate of the lth path. 4.The positioning method based on maximum likelihood estimation and pseudo-range residual compensation of claim 3, characterized in that, Step S2.2 comprises constructing a likelihood function from the complete data set and maximizing the function: wherein Θ l is a parameter set of the lth path, for the complete data set of the first path, For the likelihood function of the pseudo-range parameters, Y is channel state information, L is the number of multipaths, and σ is the standard deviation of additive white Gaussian noise. 5.The positioning method based on maximum likelihood estimation and pseudo-range residual compensation of claim 4, characterized in that, Step S2.3 comprises: the minimum description length criterion detects models comprising: where X(f; Θ) is the complete data set of the lth path, is the number of multipaths corresponding to the minimum value of the minimum description length criterion, K is the maximum number of multipaths limited, and l is the penalty function, and is the penalty function coefficient, and L is the noise standard deviation. L is the noise standard deviation. 6.The positioning method based on maximum likelihood estimation and pseudo-range residual compensation of claim 5, characterized in that, The calculation of the number of multipaths L when the MDL reaches the minimum value specifically comprises: If the minimum description length criterion detection model reaches the minimum value when L-1, the current number of multipaths L is output, and the iteration is stopped; Otherwise, L is set to L+1, and steps S2.1 and S2.2 are repeated. 7.The positioning method based on maximum likelihood estimation and pseudo-range residual compensation of claim 6, characterized in that, Step S3 includes: after the initial position is obtained by using the weighted least square method, the estimated coordinate is obtained by bringing into the maximum likelihood stage of Gauss Newton: Wherein, B is the number of remote radio unit RRU, σ b is the standard deviation of the measured pseudo-range of the bth RRU, x is the estimated terminal coordinate, x b is the coordinate of the bth RRU, is the pseudo-range residual of the ith iteration, is the maximum likelihood function, p b is the pseudo-range of the first path received by the bth RRU relative to the terminal, b is a positive integer and b [1, B] is the pseudo-range residual of the bth base station BS. 8.The positioning method based on maximum likelihood estimation and pseudo-range residual compensation of claim 7, characterized in that, Step S4 comprises: Step S4.1, calculate pseudo-range residuals: wherein, is the terminal UE estimation position in the ith iteration; Step S4.2, judging whether the UE estimation position converges, if the UE estimation position converges, the UE estimation position is the terminal positioning result, and the terminal positioning is output, if the UE estimation position does not converge, returning to step S3 and iterating until the UE estimation position converges.

9. The method of claim 2-6, wherein, Before the cyclically decomposing the expected, it further comprises: initializing the multipath parameters by using a successive interference cancellation method. 10.The positioning method based on maximum likelihood estimation and pseudo-range residual compensation of claim 1, characterized in that, Step S1 comprises: a positioning server requiring a baseband processing unit (BBU) of a 5G distributed base station to open a data interface, acquiring the CSI of a channel sounding reference signal (SRS) corresponding to each remote radio unit (RRU) via the data interface, and transparently transmitting the CSI of the SRS to the positioning server before the RRU radio frequency combining.

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