Unmanned system positioning method and device based on area array ultra wide band, terminal and medium

Abstract

The invention discloses an unmanned system positioning method and device based on an area array ultra wide band, a terminal and a medium, and relates to the field of unmanned aerial vehicle cluster systems and wireless positioning, and the method comprises the steps: obtaining each initial relative distance and each initial phase difference between a to-be-positioned robot and an anchor robot based on an ultra wide band three-dimensional antenna array; de-noising each initial relative distance and each initial phase difference, and determining each de-noising relative distance and each de-noising phase difference; and determining the relative position and the relative direction of the to-be-positioned robot according to each denoising relative distance and each denoising phase difference. According to the invention, the arrival time of the ultra-wideband signal between the robot to be positioned and the anchor point robot is measured synchronously, so that the relative distance and the plurality of phase differences of the ultra-wideband signal in the three-dimensional space are obtained, and the relative position, posture and the like are calculated according to the relative distance and the plurality of phase differences. Therefore, the problems that in the prior art, frequent two-way communication interaction is needed, and energy consumption and communication time delay of the system are increased can be effectively solved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) swarm systems and wireless positioning, and more particularly to a positioning method, device, terminal, and medium for unmanned systems based on area array ultra-wideband. Background Technology

[0002] In recent years, unmanned aerial vehicle (UAV) swarm systems have shown potential in fields such as routine inspection, collaborative operations, regional exploration, disaster relief, military, and medical care. The efficient collaboration and task execution of robot swarms are inseparable from precise positioning.

[0003] Currently, the relative positioning method for unmanned aerial vehicle (UAV) swarm systems refers to the technology by which robots within the system obtain their relative positions, distances, and attitude relationships through mutual perception, information exchange, and collaborative computation. However, this type of method requires frequent two-way communication interactions, increasing the system's energy consumption and communication latency.

[0004] Therefore, existing technologies still need improvement and development. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a positioning method, device, terminal and medium for unmanned systems based on ultra-wideband array, which addresses the above-mentioned defects of the prior art. The aim is to solve the problem that the prior art requires frequent two-way communication interaction, which increases the energy consumption and communication latency of the system.

[0006] The technical solution adopted by this invention to solve the problem is as follows: In a first aspect, embodiments of the present invention provide a positioning method for an unmanned system based on an ultra-wideband area array, wherein the method includes: The initial relative distances and initial phase differences between the robot to be located and the anchor robot are obtained based on an ultra-wideband three-dimensional antenna array. Denoise each of the initial relative distances and initial phase differences to determine each denoised relative distance and each denoised phase difference; The relative position and relative orientation of the robot to be located are determined based on the denoised relative distance and the denoised phase difference.

[0007] In one implementation method, the initial relative distances and initial phase differences between the robot to be located and the anchor robot are obtained based on an ultra-wideband three-dimensional antenna array, including: The arrival times and initial phase differences of each signal between the robot to be positioned and the anchor robot are determined based on the signals from the central antenna of the robot to be positioned to each phase measuring antenna of the anchor robot. The initial relative distances are determined based on the arrival times of the signals using a two-way ranging model.

[0008] In one implementation method, denoising is performed on each of the initial relative distances and each of the initial phase differences to determine each denoised relative distance and each denoised phase difference, including: Filter each of the initial relative distances and each of the initial phase differences to determine each initial denoised relative distance and each initial denoised phase difference; A fully connected neural network is used to predict the distance compensation value corresponding to each of the initial denoised relative distances and the phase compensation value corresponding to each of the initial denoised phase differences; Each denoising relative distance is determined based on the initial denoising relative distance and the corresponding distance compensation value, and each denoising phase difference is determined based on the initial denoising phase difference and the corresponding phase compensation value.

[0009] In one implementation method, determining the relative position and relative orientation of the robot to be located based on the denoised relative distances and the denoised phase differences includes: The relative distance of each target is determined based on the denoised relative distance and the denoised phase difference. Obtain the coordinates of each phase measurement antenna on the anchor point robot; The relative position and relative orientation of the robot to be located are determined based on the relative distances to each target and the coordinates of each phase measuring antenna.

[0010] In one implementation method, determining the relative position and relative orientation of the robot to be located based on the relative distances to each target and the coordinates of each phase-measuring antenna includes: A three-dimensional spatial geometric model is constructed based on the relative distances of each target and the coordinates of each phase measuring antenna. Solve the three-dimensional spatial geometric model to determine the relative position and the relative orientation; The three-dimensional spatial geometric model is as follows: , in, The coordinates of the robot to be located are: For the first anchor point robot Coordinates of the phase-measuring antenna For the first The robot to be located and the first The first anchor point robot The relative distance to the target measured by each phase-measuring antenna.

[0011] In one implementation method, solving the three-dimensional spatial geometric model to determine the relative position and the relative orientation includes: Let the three-dimensional spatial geometric model be represented as: , in, The coefficient matrix, Let be the vector to be solved. A vector of constant terms. = , = , = ; The relative positions are determined by solving the three-dimensional spatial geometric model. and the relative orientation , wherein the relative position for: = , The relative orientation for: , , , Represents the norm, Angle of elevation, This is the direction angle.

[0012] In one implementation method, solving the three-dimensional spatial geometric model to determine the relative position and the relative orientation includes: Let the three-dimensional spatial geometric model be represented as: , in, The coefficient matrix, Let be the vector to be solved. A vector of constant terms. = , = , = ; Introduction ,in, The coefficient matrix, Let be the vector to be solved. A vector of constant terms. , , , The relative positions are determined by solving the three-dimensional spatial geometric model. and the relative orientation , wherein the relative position for: , , , , in, For process variables, , They represent the weights, Represents a diagonal matrix. It represents the Hadamah accumulation. The relative orientation for: , , , Represents the norm, Angle of elevation, This is the direction angle.

[0013] Secondly, embodiments of the present invention also provide a positioning device for an unmanned system based on an area array ultra-wideband, wherein the positioning device for an unmanned system based on an area array ultra-wideband includes: The data acquisition module is used to acquire the initial relative distances and initial phase differences between the robot to be located and the anchor point robot based on the ultra-wideband three-dimensional antenna array. The data denoising module is used to denoise each of the initial relative distances and each of the initial phase differences, and to determine each denoised relative distance and each denoised phase difference. The position and orientation determination module is used to determine the relative position and orientation of the robot to be located based on the denoised relative distances and the denoised phase differences.

[0014] Thirdly, embodiments of the present invention also provide a terminal, the terminal including a memory and one or more processors; the memory stores one or more programs; the programs include instructions for executing the unmanned system positioning method based on area array ultra-wideband as described above; the processor is used to execute the programs.

[0015] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a plurality of instructions, wherein the instructions are adapted to be loaded and executed by a processor to implement any of the above-described unmanned system positioning methods based on area array ultrawideband.

[0016] The beneficial effects of this invention are as follows: This invention acquires the initial relative distances and initial phase differences between the robot to be located and the anchor robot based on an ultra-wideband three-dimensional antenna array; it denoises each of the initial relative distances and initial phase differences to determine each denoised relative distance and each denoised phase difference; and it determines the relative position and relative orientation of the robot to be located based on each denoised relative distance and each denoised phase difference. Because this invention simultaneously measures the arrival time of the ultra-wideband signal between the robot to be located and the anchor robot, obtains the relative distance and multiple phase differences of the ultra-wideband signal in three-dimensional space, and calculates the relative position and attitude based on the relative distance and multiple phase differences, it can effectively solve the problem of frequent bidirectional communication interaction required in existing technologies, which increases system energy consumption and communication latency. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the unmanned system positioning method based on area array ultra-wideband provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the distributed volume array relative positioning model and the centralized array relative positioning model provided in the embodiments of the present invention.

[0020] Figure 3 This is a schematic diagram of the noise reduction process provided in an embodiment of the present invention.

[0021] Figure 4 This is a schematic diagram illustrating the implementation process of the phase-corrected time-of-arrival positioning algorithm provided in this embodiment of the invention.

[0022] Figure 5 This is a schematic diagram of the antenna array model of the pyramid model provided in the embodiment of the present invention.

[0023] Figure 6 This is a schematic diagram illustrating the implementation process of the time-of-arrival localization algorithm based on the two-step maximum likelihood method and phase correction provided in an embodiment of the present invention.

[0024] Figure 7 This is a schematic diagram of the internal modules of the unmanned system positioning device based on ultra-wideband array provided in an embodiment of the present invention.

[0025] Figure 8 This is a schematic diagram of the terminal provided in the embodiment of the present invention. Detailed Implementation

[0026] This invention discloses a positioning method, device, terminal, and medium for unmanned systems based on ultra-wideband area arrays. To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention.

[0027] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0028] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0029] In recent years, unmanned aerial vehicle (UAV) swarm systems have shown potential in fields such as routine inspection, collaborative operations, regional exploration, disaster relief, military, and medical care. The efficient collaboration and task execution of robot swarms are inseparable from precise positioning.

[0030] Currently, the relative positioning method for unmanned aerial vehicle (UAV) swarm systems refers to the technology by which robots within the system obtain their relative positions, distances, and attitude relationships through mutual perception, information exchange, and collaborative computation. However, this type of method requires frequent two-way communication interactions, increasing the system's energy consumption and communication latency.

[0031] To address the aforementioned shortcomings of existing technologies, this invention provides a localization method for unmanned systems based on an ultra-wideband (UWB) antenna array. The method acquires initial relative distances and initial phase differences between the robot to be located and the anchor robot using an UWB three-dimensional antenna array. It then denoises each initial relative distance and initial phase difference to determine denoised relative distances and denoised phase differences. Finally, it determines the relative position and orientation of the robot to be located based on these denoised relative distances and phase differences. Because this invention simultaneously measures the arrival time of the UWB signal between the robot to be located and the anchor robot, obtaining the relative distance and multiple phase differences of the UWB signal in three-dimensional space, and calculating the relative position and attitude based on these relative distances and multiple phase differences, it effectively solves the problem of frequent bidirectional communication interactions required in existing technologies, which increases system energy consumption and communication latency.

[0032] Exemplary method: like Figure 1 As shown, the method includes: Step S100: Obtain the initial relative distances and initial phase differences between the robot to be located and the anchor point robot based on the ultra-wideband three-dimensional antenna array.

[0033] Anchor robots serve as reference points in ultra-wideband (UWB) unmanned systems (UWBS). The robot to be localized is the one in the UWBS that requires its position and attitude information to be determined. UAV swarm models are categorized into distributed volume array relative positioning models and centralized array relative positioning models. When using UWBS communication, different models have different communication timing requirements; the distributed volume array relative positioning model requires… The secondary communication timing, the centralized model is as follows: Secondary communication timing.

[0034] When the drone swarm model is a centralized array relative positioning model, the anchor point robot There is one central antenna and m phase-measuring antennas. The position of the anchor robot is considered as the origin of a three-dimensional coordinate system, and its absolute position is... Robot to be located For a single central antenna, such as Figure 2 Centralized array relative positioning model, robot For anchor point robots, and The robot to be located. When the UAV swarm model is a distributed volume array relative positioning model, the anchor robot has one central antenna and m phase-measuring antennas. The position of the anchor robot is considered as the origin of the three-dimensional coordinate system, and its absolute position is... .like Figure 2 In the distributed array relative positioning model, any robot can serve as an anchor point robot (e.g., selecting...). (Anchor robot), other robots are used as robots to be located (e.g., and (As a robot to be located). The system consists of one central antenna and m phase-measuring antennas. The initial relative distance and initial phase difference between the anchor robot and the robot to be located can be obtained through signal transmission between the phase-measuring antennas of the anchor robot and the central antenna of the robot to be located. This embodiment, through a single ultra-wideband system, fundamentally avoids the synchronization and calibration problems caused by multi-source data fusion, significantly improving the system's reliability and stability.

[0035] In one implementation, the initial relative distances and initial phase differences between the robot to be located and the anchor robot are obtained based on an ultra-wideband three-dimensional antenna array, including: Step S101: Determine the arrival time of each signal and the initial phase difference between the robot to be positioned and the anchor robot based on the signals from the center antenna of the robot to be positioned to each phase measurement antenna of the anchor robot. Step S102: Determine the initial relative distance of each signal based on the arrival time of each signal using a two-way ranging model.

[0036] In an ultra-wideband three-dimensional antenna array, if it is a centralized array relative positioning model, the signal arrival time between the k-th phase-measuring antenna of the anchor robot and the central antenna of the robot to be positioned is obtained. Based on the arrival times of each signal, the initial relative distance between the robot to be positioned and the anchor robot is calculated using a two-way ranging model (TWR model). The initial phase difference is obtained based on the phase difference between the k-th phase measuring antenna and the 1-th phase measuring antenna of the anchor robot relative to the center antenna of the robot to be positioned. If it is a distributed volume array relative positioning model, the first anchor point robot... k The signal arrival times of each antenna element and the central antenna of the robot to be located are used to calculate the initial relative distance between the robot to be located and the anchor robot based on the signal arrival times using a two-way ranging model (TWR model). and initial phase difference .

[0037] Step S200: Denoise each of the initial relative distances and initial phase differences to determine each denoised relative distance and each denoised phase difference.

[0038] To eliminate various errors during the solution process, the initial relative distance and each initial phase difference are denoised to improve data quality and thus improve the accuracy of subsequent position and attitude solutions.

[0039] In one implementation, denoising is performed on each of the initial relative distances and each of the initial phase differences to determine each denoised relative distance and each denoised phase difference, including: Step S201: Filter each of the initial relative distances and each of the initial phase differences to determine each initial denoised relative distance and each initial denoised phase difference; Step S202: Use a fully connected neural network to predict the distance compensation value corresponding to each of the initial denoised relative distances and the phase compensation value corresponding to each of the initial denoised phase differences; Step S203: Determine each of the initial denoising relative distances and the corresponding distance compensation values, and determine each of the initial denoising phase differences and the corresponding phase compensation values.

[0040] like Figure 3 As shown, to eliminate various errors during the solution process, Kalman filtering and low-pass filtering algorithms are used to remove Gaussian white noise and random fluctuation noise from the measurement data in the pre-filtering of the initial relative distance and initial phase difference. Then, a fully connected neural network is used for prediction to eliminate bias errors. The specific processing procedure is as follows: The initial relative distance measured by the ultra-wideband three-dimensional antenna array Considered Initial phase difference Considered . Its composition is: The set representing real relative distances Represents Gaussian white noise in the initial relative distance, This represents random fluctuation noise in the initial relative distance. This represents the bias noise in the initial relative distance. and Their compositions are similar. The set representing the true phase difference Represents Gaussian white noise in the initial phase difference, This represents random fluctuation noise in the initial phase difference. Let the bias noise in the initial phase difference be represented. and It is expressed as follows: , , The Kalman filter and low-pass filter in the filtering algorithm mainly remove Gaussian white noise and random fluctuation noise from the data. Therefore, the initial denoised relative distance is obtained after pre-filtering. and initial denoised phase difference , means as follows: , .

[0041] By using a fully connected neural network to learn the errors present in the measurement data and predict compensation during practical use, the following results can be obtained: ( ), ( ), in and These represent the initial denoised relative distance and the initial denoised phase difference, respectively, after pre-filtering. Therefore, by eliminating the bias error, more accurate measurement data can be obtained. and It can be seen as and Nonlinear functions can be effectively approximated using fully connected neural networks. Using the input... and As supervisory data, two prediction networks corresponding to relative distance and phase difference are trained respectively.

[0042] The bias noise is estimated using the trained prediction network corresponding to the relative distance and phase difference, respectively. , , in, and These represent the distance compensation value and phase compensation value predicted by the fully connected neural network, respectively. and These represent fully connected neural networks trained on prior data for relative distance and fully connected neural networks for phase difference, respectively. and These represent the layer parameters of a fully connected neural network for relative distance and a fully connected neural network for phase difference, respectively. and These are the connection weight parameters for fully connected neural networks based on relative distance and those based on phase difference, respectively. and These are the initial denoised relative distance and initial denoised phase difference used for prediction.

[0043] By combining the distance compensation and phase compensation values ​​predicted by the fully connected neural network with the initial denoised relative distance and initial denoised phase difference, respectively, the denoised relative distance and denoised phase difference are obtained with high accuracy, closely approximating the true values: , , and This indicates that the denoised relative distance and denoised phase difference, predicted by the fully connected neural network, are very close to the true values. Through a dual data processing mechanism of filtering and a fully connected neural network, it effectively combats measurement noise and non-line-of-sight errors in complex environments, significantly improving data quality.

[0044] Step S300: Determine the relative position and relative orientation of the robot to be located based on the denoised relative distance and the denoised phase difference.

[0045] By using multiple sets of denoised relative distances and denoised phase differences, combined with a three-dimensional spatial geometric model and positioning algorithm, the complete three-dimensional relative position and relative orientation are calculated, providing richer information for advanced cooperative control of swarm robots.

[0046] In one implementation, determining the relative position and relative orientation of the robot to be located based on the denoised relative distances and the denoised phase differences includes: Step S301: Determine the relative distance of each target based on the denoised relative distance and the denoised phase difference; Step S302: Obtain the coordinates of each phase measuring antenna on the anchor point robot; Step S303: Determine the relative position and relative orientation of the robot to be located based on the relative distances to each target and the coordinates of each phase measuring antenna.

[0047] The denoised relative distances and denoised phase differences are converted into distance values ​​to obtain the target relative distance. The conversion process is as follows: ; in, For the first The first anchor point robot The phase-measuring antenna to the first The relative distance to the target of the first robot to be located is the distance of the first robot to be located. The anchor point robot to the first The denoised relative distance of the robot to be located For the first The first anchor point robot The phase-measuring antenna to the first Denoising phase difference of a robot to be located For the signal wavelength, For size A vector with elements equal to 1.

[0048] Because an ultra-wideband 3D antenna array is deployed, and the anchor robot's position is the origin of the coordinate system, the positions of each phase-measuring antenna on the anchor robot are known. Assume the position of the first phase-measuring antenna on the anchor robot is... The coordinates of the phase-measuring antennas corresponding to each phase-measuring antenna are: The coordinates of the robot to be located are The relative position and relative orientation are calculated by using a positioning algorithm based on the relative distance of each target and the coordinates of each phase measuring antenna.

[0049] In one implementation, determining the relative position and relative orientation of the robot to be located based on the relative distances to each target and the coordinates of each phase-measuring antenna includes: Step S303: Construct a three-dimensional spatial geometric model based on the relative distances of each target and the coordinates of each phase measuring antenna; Step S303: Solve the three-dimensional spatial geometric model to determine the relative position and the relative orientation; Based on the relative distances of each target , No. Coordinates of each phase-measuring antenna Construct a three-dimensional spatial geometric model and solve for the relative position of the robot to be localized. Then we have: , According to the Time of Arrival (TOA) algorithm, we have:

[0050] The resulting three-dimensional spatial geometric model is as follows: , in, The coordinates of the robot to be located are: For the first anchor point robot Coordinates of the phase-measuring antenna For the first The robot to be located and the first The first anchor point robot The relative distance to the target measured by each phase-measuring antenna.

[0051] In one implementation, solving the three-dimensional spatial geometric model to determine the relative position and the relative orientation includes: The phase-corrected time-of-arrival (PT-TOA) localization algorithm solves the 3D geometric model to obtain the relative position and relative orientation of the robot to be localized. The implementation process is as follows: Figure 4As shown, the initial relative distance and initial phase difference obtained based on the ultra-wideband 3D array are subjected to Kalman filtering, low-pass filtering, and fully connected neural network prediction to eliminate noise, thus obtaining the denoised relative distance and denoised phase difference; the denoised relative distance and denoised phase difference are then converted into the target relative distance; based on... Figure 5 The antenna array model of the pyramid model is used, and the time-of-arrival (TOA) localization algorithm based on phase correction is employed to solve for the relative position and relative orientation of the target relative distance and the coordinate relationship of each phase-measuring antenna of the anchoring robot. The TOA algorithm based on phase correction requires at least three anchor points. The specific solution process is shown below: Let the three-dimensional spatial geometric model be represented as: , The coefficient matrix, Let be the vector to be solved. A vector of constant terms. in, = , = , = ; For the three-dimensional spatial geometric model ( The relative position is determined by performing the calculation. and the relative orientation The specific process is as follows: , Wherein, the relative position for: = , The relative orientation for: , , , Represents the norm, Angle of elevation, This is the direction angle.

[0052] In one implementation, solving the three-dimensional spatial geometric model to determine the relative position and the relative orientation includes: The three-dimensional geometric model is solved using a phase-corrected two-step maximum likelihood method and a phase-corrected time-of-arrival (PT-TOA-TSML) localization algorithm to obtain the relative position and relative orientation of the robot to be localized. The implementation process is as follows: Figure 6As shown, the initial relative distance and initial phase difference obtained from the ultra-wideband 3D array are subjected to Kalman filtering, low-pass filtering, and fully connected neural network prediction to obtain the denoised relative distance and denoised phase difference. The denoised relative distance and denoised phase difference are then converted into the target relative distance. A two-step maximum likelihood method and a phase-corrected time-of-arrival localization algorithm are used to solve for the relative position and relative orientation based on the target relative distance and the coordinates of the phase-measuring antennas of the anchoring robot. The two-step maximum likelihood method and phase-corrected time-of-arrival localization algorithm requires at least four anchor points. The specific solution process is shown below: Let the three-dimensional spatial geometric model be represented as: , The coefficient matrix, Let be the vector to be solved. A vector of constant terms. in, = , = , = ; The least squares solution of the three-dimensional spatial geometric model is expressed by the following equation based on the least squares method: , , , , in, Indicates weight, and For process variables, Indicates the relative distance of the target variance Represents a diagonal matrix; Introduction ,in, The coefficient matrix, Let be the vector to be solved. A vector of constant terms. , , , The relative positions are determined by solving the three-dimensional spatial geometric model. and the relative orientation , wherein the relative position for: , , , , in, For process variables, Indicates weight, Represents a diagonal matrix. It represents the Hadamah accumulation. Calculate the relative orientation The details are as follows: , , , Represents the norm, Angle of elevation, This is the azimuth angle.

[0053] Because ultra-wideband technology has strong anti-interference capabilities, combined with filtering algorithms and communication timing design between robots, it can maintain stable operation in a variety of complex scenarios, including indoor, outdoor, and visual failure environments.

[0054] Based on the above embodiments, the present invention also provides a positioning device for unmanned systems based on an area array ultra-wideband, such as... Figure 7 As shown, the device includes: Data acquisition module 01 is used to acquire the initial relative distances and initial phase differences between the robot to be located and the anchor point robot based on the ultra-wideband three-dimensional antenna array; Data denoising module 02 is used to denoise each of the initial relative distances and each of the initial phase differences, and to determine each denoised relative distance and each denoised phase difference; The position and orientation determination module 03 is used to determine the relative position and relative orientation of the robot to be located based on the denoised relative distances and the denoised phase differences.

[0055] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 8 As shown, the terminal includes a processor, memory, network interface, and display screen connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a positioning method for unmanned systems based on an area array ultra-wideband display. The display screen can be a liquid crystal display (LCD) or an e-ink display.

[0056] Those skilled in the art will understand that Figure 8 The schematic diagram shown is only a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0057] In one implementation, the terminal's memory stores one or more programs, and these programs are configured to be executed by one or more processors, and the programs contain instructions for performing a localization method for an unmanned system based on an area array ultrawideband.

[0058] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0059] In summary, this invention discloses a method, device, terminal, and medium for unmanned system positioning based on an ultra-wideband (UWB) antenna array. The method acquires initial relative distances and initial phase differences between the robot to be positioned and the anchor robot using an UWB three-dimensional antenna array; denoises the initial relative distances and initial phase differences to determine denoised relative distances and denoised phase differences; and determines the relative position and relative orientation of the robot to be positioned based on the denoised relative distances and denoised phase differences. Because this invention simultaneously measures the arrival time of the UWB signal between the robot to be positioned and the anchor robot, obtaining the relative distance and multiple phase differences of the UWB signal in three-dimensional space, and calculating the relative position and attitude based on the relative distance and multiple phase differences, it effectively solves the problem of frequent bidirectional communication interaction required in existing technologies, which increases system energy consumption and communication latency.

[0060] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A positioning method for unmanned systems based on ultra-wideband area arrays, characterized in that, The method includes: The initial relative distances and initial phase differences between the robot to be located and the anchor robot are obtained based on an ultra-wideband three-dimensional antenna array. Denoise each of the initial relative distances and initial phase differences to determine each denoised relative distance and each denoised phase difference; The relative position and relative orientation of the robot to be located are determined based on the denoised relative distance and the denoised phase difference.

2. The unmanned system positioning method based on ultra-wideband array according to claim 1, characterized in that, The initial relative distances and initial phase differences between the robot to be located and the anchor robot are obtained based on an ultra-wideband 3D antenna array, including: The arrival times and initial phase differences of each signal between the robot to be positioned and the anchor robot are determined based on the signals from the central antenna of the robot to be positioned to each phase measuring antenna of the anchor robot. The initial relative distances are determined based on the arrival times of the signals using a two-way ranging model.

3. The unmanned system positioning method based on ultra-wideband array according to claim 1, characterized in that, Denoising is performed on each of the initial relative distances and initial phase differences to determine each denoised relative distance and each denoised phase difference, including: Filter each of the initial relative distances and each of the initial phase differences to determine each initial denoised relative distance and each initial denoised phase difference; A fully connected neural network is used to predict the distance compensation value corresponding to each of the initial denoised relative distances and the phase compensation value corresponding to each of the initial denoised phase differences; Each denoising relative distance is determined based on the initial denoising relative distance and the corresponding distance compensation value, and each denoising phase difference is determined based on the initial denoising phase difference and the corresponding phase compensation value.

4. The unmanned system positioning method based on ultra-wideband array according to claim 1, characterized in that, Determining the relative position and relative orientation of the robot to be located based on the denoised relative distances and the denoised phase differences includes: The relative distance of each target is determined based on the denoised relative distance and the denoised phase difference. Obtain the coordinates of each phase measurement antenna on the anchor point robot; The relative position and relative orientation of the robot to be located are determined based on the relative distances to each target and the coordinates of each phase measuring antenna.

5. The unmanned system positioning method based on ultra-wideband array according to claim 4, characterized in that, Determining the relative position and relative orientation of the robot to be located based on the relative distances to each target and the coordinates of each phase-measuring antenna includes: A three-dimensional spatial geometric model is constructed based on the relative distances of each target and the coordinates of each phase measuring antenna. Solve the three-dimensional spatial geometric model to determine the relative position and the relative orientation; The three-dimensional spatial geometric model is as follows: ， in, The coordinates of the robot to be located are: For the first anchor point robot Coordinates of the phase-measuring antenna For the first The robot to be located and the first The first anchor point robot The relative distance to the target measured by each phase-measuring antenna.

6. The unmanned system positioning method based on ultra-wideband array according to claim 5, characterized in that, Solving the three-dimensional spatial geometric model to determine the relative position and the relative orientation includes: Let the three-dimensional spatial geometric model be represented as: , in, The coefficient matrix, Let be the vector to be solved. A vector of constant terms. = ， = ， = ； The relative positions are determined by solving the three-dimensional spatial geometric model. and the relative orientation , wherein the relative position for: = ， The relative orientation for: ， ， , Represents the norm, Angle of elevation This is the direction angle.

7. The unmanned system positioning method based on ultra-wideband array according to claim 5, characterized in that, Solving the three-dimensional spatial geometric model to determine the relative position and the relative orientation includes: Let the three-dimensional spatial geometric model be represented as: , in, The coefficient matrix, Let be the vector to be solved. A vector of constant terms. = ， = ， = ； Introduction ,in, The coefficient matrix, Let be the vector to be solved. A vector of constant terms. ， ， ， The relative positions are determined by solving the three-dimensional spatial geometric model. and the relative orientation , wherein the relative position for: ， ， ， ， in, For process variables, , They represent the weights, Represents a diagonal matrix. It represents the Hadamah accumulation. The relative orientation for: ， ， , Represents the norm, Angle of elevation This is the direction angle.

8. A positioning device for an unmanned system based on an ultra-wideband area array, characterized in that, The device includes: The data acquisition module is used to acquire the initial relative distances and initial phase differences between the robot to be located and the anchor point robot based on the ultra-wideband three-dimensional antenna array. The data denoising module is used to denoise each of the initial relative distances and each of the initial phase differences, and to determine each denoised relative distance and each denoised phase difference. The position and orientation determination module is used to determine the relative position and orientation of the robot to be located based on the denoised relative distances and the denoised phase differences.

9. A terminal, characterized in that, The terminal includes a memory and one or more processors; the memory stores one or more programs; the programs contain instructions for executing the unmanned system positioning method based on area array ultra-wideband as described in any one of claims 1-7; the processors are used to execute the programs.

10. A computer-readable storage medium storing a plurality of instructions thereon, characterized in that, The instructions are loaded and executed by the processor to implement the steps of the unmanned system positioning method based on area array ultra-wideband as described in any one of claims 1-7.

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