Equipment position determination method and device, electronic equipment and computer readable medium

Abstract

The embodiment of the invention discloses an equipment position determination method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: acquiring distance information between target equipment and communication equipment; performing communication state monitoring on the target equipment and the communication equipment to obtain a state updating signal; determining a coarse position set of the target equipment and the communication equipment according to the state updating signal and the distance information; and determining positions of the target device and the communication device according to the coarse position set. According to the embodiment, under the condition that other information source equipment is not used, high-precision estimation of the positions of the reference node and the mobile equipment in an indoor scene where the position information of the reference node is lost is realized quickly.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and more particularly, to a method and apparatus for determining device location, an electronic device, and a computer-readable medium. Background Art

[0002] The current RTT has gradually become the mainstream method for Wi-Fi ranging and positioning, and has become an important meter-level indoor positioning means in the PNT system. Related Wi-Fi-RTT positioning ideas often require the known location of reference routers.

[0003] However, in actual situations, there may be cases where the router location is unknown or the accuracy of the reference location is severely reduced due to external disturbances, resulting in the problem that additional inspections and estimations are often required. At the same time, existing Wi-Fi-RTT positioning technical solutions often ignore the constraints on the phase distortion errors caused by changes in communication states, which limits the accuracy of the positioning effect. The above problems have restricted the development and application of indoor Wi-Fi-RTT positioning technology. Summary of the Invention

[0004] This section of the present disclosure is used to briefly introduce concepts, which will be described in detail in the following detailed implementation section. This section of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to be used to limit the scope of the claimed technical solution.

[0005] Some embodiments of the present disclosure propose a method and apparatus for determining device location, an electronic device, and a computer-readable medium to solve the technical problems mentioned in the above background art section.

[0006] In a first aspect, some embodiments of the present disclosure provide a method for determining device location, the method including: obtaining distance information between a target device and a communication device; monitoring the communication states of the target device and the communication device to obtain a status update signal; determining a rough location set of the target device and the communication device according to the status update signal and the distance information; and determining the locations of the target device and the communication device according to the rough location set.

[0007] In a second aspect, some embodiments of the present disclosure provide a device for determining device location, the device including: an obtaining unit configured to obtain distance information between a target device and a communication device; a monitoring unit configured to monitor the communication states of the target device and the communication device to obtain a status update signal; a first determining unit configured to determine a rough location set of the target device and the communication device according to the status update signal and the distance information; and a second determining unit configured to determine the locations of the target device and the communication device according to the rough location set.

[0008] In a third aspect, an embodiment of the present application provides an electronic device, which includes: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method described in any implementation manner of the first aspect.

[0009] In a fourth aspect, an embodiment of the present application provides a computer-readable medium, on which a computer program is stored, and when the computer program is executed by a processor, the method described in any implementation manner of the first aspect is implemented.

[0010] One embodiment of the above various embodiments of the present disclosure has the following beneficial effects: Based on the rough estimated values of the reference nodes (communication devices), high-precision estimation of the positions of the unknown reference nodes and the target device is simultaneously achieved. The iterative LS method with a sliding window is used to solve the underdetermination of the problem equation, correct the phase distortion error while improving the smoothness of the positioning effect, and give a rough estimated solution set of the problem; the classification and weighting method of SVM is used to constrain errors such as multipath effects in the rough estimation results, and further optimize the positioning results. Thus, without relying on other information source devices, high-precision estimation of the positions of the reference nodes and the target device is quickly achieved in an indoor scenario where the position information of the reference nodes is missing. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic, and the elements and elements are not necessarily drawn to scale.

[0012] Figure 1 is a schematic diagram of an application scenario of a device position determination method according to some embodiments of the present disclosure;

[0013] Figure 2 is a flowchart of some embodiments of a device position determination method according to the present disclosure;

[0014] Figure 3 is a schematic structural diagram of some embodiments of a device position determination device according to the present disclosure;

[0015] Figure 4 is a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0017] In addition, it should be noted that for ease of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0018] It should be noted that concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0019] It should be noted that the modifiers "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

[0020] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0021] The present disclosure will be described in detail below with reference to the drawings and in combination with embodiments.

[0022] Figure 1 is a schematic diagram of an application scenario of a method for determining the location of a device according to some embodiments of the present disclosure.

[0023] As Figure 1 shown, the server 101 can obtain the distance information 102 between the target device and the communication device; monitor the communication status of the target device and the communication device to obtain a status update signal 103; determine a rough location set 104 of the target device and the communication device according to the status update signal 103 and the distance information 102; and determine the locations 105 of the target device and the communication device according to the rough location set 104.

[0024] It can be understood that the device location determination method can be executed by the terminal device, or can also be executed by the server 101. The execution subject of the above method can also include the device formed by integrating the above terminal device and the above server 101 through a network, or can also be executed by various software programs. Among them, the terminal device can be various electronic devices with information processing capabilities, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, desktop computers, and so on. The execution subject can also be embodied as the server 101, software, etc. When the execution subject is software, it can be installed in the above-listed electronic devices. It can be implemented as, for example, multiple software or software modules for providing distributed services, or can also be implemented as a single software or software module. No specific limitation is made here.

[0025] It should be understood that Figure 1 the number of servers in is only illustrative. According to the implementation requirements, any number of servers can be provided.

[0026] Continuing to refer to Figure 2 , a flow 200 of some embodiments of the device location determination method according to the present disclosure is shown. The device location determination method includes the following steps:

[0027] Step 201, obtain distance information between a target device and a communication device.

[0028] In some embodiments, the execution subject of the device location determination method (such as Figure 1 the server shown) can obtain the distance information between the target device and the communication device through a wired connection method or a wireless connection method. It should be noted that the above wireless connection method can include but not be limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future-developed wireless connection methods. Specifically, the above target device and the above communication device generally refer to wireless ad hoc network devices.

[0029] Specifically, the above distance information is usually the ranging data collection after the target device and the communication device establish a connection and remain stationary for a period of time.

[0030] Here, the above target device generally refers to mobile devices such as mobile phones, tablets, and mobile computers. The above communication device generally refers to a device that performs a network connection or a communication connection with the above target device. As an example, the above communication device can be a router.

[0031] Specifically, the above distance information generally includes the distance between the above target device and the above communication device, the number of the above communication device, the timestamp of the ranging moment between the above target device and the communication device, and the variance of the ranging result between the above target device and the communication device.

[0032] The above execution entity generally preprocesses the above distance information.

[0033] Step 202: Monitor the communication status of the above target device and the above communication device to obtain a status update signal.

[0034] In some embodiments, the above execution entity (such as Figure 1 the server shown) can monitor the communication status of the above target device and the above communication device to obtain a status update signal. Here, the above status update signal generally refers to the signal at the moment of communication status change accompanied by the reset of the phase distortion error.

[0035] Step 203: Determine the rough position sets of the above target device and the above communication device according to the above status update signal and the above distance information.

[0036] In some embodiments, the above execution entity can determine the rough position sets of the above target device and the above communication device according to the above status update signal and the above distance information.

[0037] Here, for a communication device with an unknown position, the above execution entity can establish a solution equation based on the ranging result to roughly estimate the positions of the unknown communication device and the target device. Specifically, the above rough position sets generally refer to the approximate positions estimated for the target device and the above communication device at a certain moment.

[0038] Specifically, it is required that the total number of communication devices (including unknown and known devices) that can establish communication with the target device at the same moment is not less than 3 in a two-dimensional scenario and not less than 4 in a three-dimensional scenario.

[0039] In some optional implementation manners of some embodiments, the above execution entity can construct a parameter vector to be estimated according to the above status update signal and the above distance information, and establish a position relationship equation based on the above parameter vector to be estimated: where A represents the phase distortion error to be estimated, d RTT represents the RTT ranging result obtained by the target device from an unknown communication device in the case of an unknown router position, AP(x1, y1, z1) represents the position of the communication device with an unknown position (AP, Access Point), P0(x2, y2, z2) represents the position of the target device to be obtained, a represents the system error, and the first parameter vector to be estimated taking a single communication device and the target device as an example is [x1 x2 y1 y2 z1 z2 A] TDetermine the rough position sets of the above target device and the above communication device according to the above relational equations.

[0040] Specifically, the above parameter vector to be estimated usually includes: phase distortion errors of all communication devices; position parameters of all communication devices to be solved; position parameters of the target device.

[0041] When taking a single communication device and a target device as an example, the above parameter vector to be estimated may include: assuming the position of the communication device with unknown position is AP(x1, y1, z1), and its approximate position is The superscript 0 in the upper right corner represents the data of the unknown position. The position of the target device to be solved is P0(x2, y2, z2), and its approximate position is A is the phase distortion error to be estimated, A0 is the approximate result of the phase distortion error, ρ0 is the distance between the approximate position of the target device and the reference position of the communication device, and the first parameter to be estimated is written in vector form as {x1 y1 z1 x2 y2 z2 A}; d RTT d represents the RTT ranging result obtained by the target device from the unknown communication device in the case of the unknown position of the communication device. The second parameter to be estimated is the correction amount Δx1, Δy1, Δz1 of the unknown communication device position, the correction amount Δx2, Δy2, Δz2 of the target device position to be solved, and the correction amount ΔA of the phase distortion error, written in vector form as {Δx1 Δy1 Δz1 Δx2 Δy2 Δz2 ΔA}.

[0042] Here, considering the system error, the relational equation is further rewritten to solve the residual vector and the position solution coefficient vector, and the following results are obtained:

[0043] r = d RTT + a - ρ0 - A0

[0044]

[0045] r = b · [Δx1 Δx2 Δy1 Δy2 Δz1 Δz2 ΔA] T

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] , where r is the residual vector, a is the system error term that can be calculated in advance according to device information, and b is the position solution coefficient vector. represents d RTT The result of taking the partial derivative of the relationship equation corresponding to the vector x1 to be estimated. Expressions of this form are all like this.

[0056] In some alternative implementation manners of some embodiments, the above-mentioned execution entity may construct a solution equation based on a sliding window; set the size of the above-mentioned sliding window; update the data filling in the above-mentioned sliding window according to epochs; and use the iterative least squares algorithm to solve the parameter vector to be estimated in the above-mentioned sliding window at different time periods, so as to obtain the rough position sets of the above-mentioned target device and the above-mentioned communication device.

[0057] In some alternative implementation manners of some embodiments, the solution equation of the above-mentioned sliding window is constructed according to the following formula:

[0058]

[0059]

[0060] where n represents the size of the sliding window, and k represents the total number of unknown communication devices. and The superscript represents the communication device number, and the subscript represents the data at the nth moment in the sliding window, corresponding to the real time t + n, where t is the real time corresponding to the first data in the sliding window. That is, it represents the residual vector between the target device and the kth base station at the nth moment in the sliding window. represents the coefficient vector between the target device and the kth base station at the nth moment in the sliding window, x2, y2, z2 are the positions of the target device to be obtained, x1, y1, z1 and A1 are the position and phase distortion error of the first unknown communication device, x3...x k+1 , y3...y k+1 , z3...z k+1 and A3...A k+1are the positions and phase distortion errors of the 2nd to the kth unknown communication devices, Δx2, Δy2, and Δz2 are the corrections of the target device position to be determined, Δx1, Δy1, Δz1, and ΔA1 are the corrections of the position and phase distortion error of the 1st unknown communication device, and Δx3...Δx k+1 、Δy3...Δy k+1 、Δz3...Δz k+1 and ΔA3...ΔA k+1 are the corrections of the positions and phase distortion errors of the 2nd to the kth unknown communication devices, jointly constituting the second parameter vector to be estimated

[0061] {Δx1 … Δx n Δy1 … Δy n Δz1 … Δz n ΔA1 ΔA3 … ΔA n}.

[0062] Specifically, for the sliding window, it is required that the total number of equations included in the window is not less than the total number of vector parameters to be estimated {1*3 + k*4}, corresponding to three variables of the target device coordinates and k unknown communication devices with four variables. The total number of equations refers to the product of the number of epochs and the number of equations in a single epoch, n*k.

[0063] Step 204: Determine the positions of the target device and the communication devices according to the above rough position set.

[0064] In some embodiments, the above execution entity may determine the positions of the target device and the communication devices according to the above rough position set.

[0065] Specifically, the above execution entity may use the rough estimation results of the positions of the communication devices and the target device to construct a fine estimation method based on SVM, converge the fine estimation of the positions of the communication devices, and obtain the positions of the target device and the communication devices.

[0066] In addition, the positioning results of the communication devices can be shared to a storage such as the cloud for storage and sharing as the positions of reference nodes, and can be used as known reference node information to provide conventional positioning services for other target devices in the future.

[0067] In some alternative implementations of some embodiments, the above-mentioned execution entity may determine the maximum number of classifications of the above-mentioned rough position set and the classification center of each classification, and perform the following determination steps: input the above-mentioned rough position set into a pre-trained support vector machine model, classify the above-mentioned rough position set according to the problem function, the above-mentioned maximum number of classifications, and the above-mentioned classification center to obtain a classification result, where the above-mentioned classification result includes at least one type of data set; construct a decision function and verify the above-mentioned classification result according to the above-mentioned decision function to obtain a verification result; determine the positions of the above-mentioned target device and the above-mentioned communication device according to the above-mentioned verification result.

[0068] Specifically, the rough position set is specifically described as step, representing the communication device serial number, h k j , represents the solution under the j-th window in the set obtained in step two and the average measured RTT result.

[0069] In some alternative implementations of some embodiments, the above-mentioned execution entity may, in response to the above-mentioned verification result satisfying a preset condition, for each type of data set in the above-mentioned classification result, determine the variance of the data in the data set, and perform a weighted average summation on the data set using a cost function to obtain a weighted result; perform a weighted summation using the above-mentioned variance and the above-mentioned weighted result to obtain the positions of the above-mentioned target device and the above-mentioned communication device.

[0070] In some alternative implementations of some embodiments, the above-mentioned execution entity may, in response to the above-mentioned verification result not satisfying a preset condition, adjust the maximum number of classifications of the above-mentioned rough position set and the classification center of each classification according to the above-mentioned verification result, and repeatedly execute the above-mentioned determination steps.

[0071] Specifically, the above-mentioned execution entity may determine the positions of the above-mentioned target device and the above-mentioned communication device according to the following steps:

[0072] Step 1: Limit the maximum number of classifications and randomly assign classification centers.

[0073] Step 2: Invoke the SVM model and solve the classification problem, and classify according to the functional distance between each data and the data center into each group.

[0074] Specifically, the problem function is:

[0075]

[0076]

[0077]

[0078] Among them, k is the coefficient vector characterizing the data center, and l j is the distance parameter of the data center, C i is the penalty parameter threshold, F i is the Gaussian kernel function, ρ i , A i is based on h k j The elements in are based on the obtained expected ranging quantity and distortion error, σ 2 is the variance of the ranging result, a i is the distance function characterization value to be solved.

[0079] Step 3: Construct a decision function and determine whether the classification result satisfies the threshold under the decision function. If it is satisfied, stop and proceed to Step 4; otherwise, continue the classification and return the result as a new set of solution sets to Step 1;

[0080] Specifically, let sgn = round(x), and the above decision function is:

[0081]

[0082] Among them, round(·) represents the rounding function, and the classification rule is to classify according to the proximity of the numerical values calculated by the corresponding decision function f j (T).

[0083] Step 4: Divide the classification results into several groups according to the decision function values, and perform weighted average summation for each group according to the cost function;

[0084] Specifically, the cost function is as follows:

[0085]

[0086] S u i ={u j |j = 1,..., m}

[0087]

[0088]

[0089] , where S u i represents the set of elements of the classification result of the i-th group, m represents the total number of elements, and the j-th element u u i in S j corresponds to [x k+1 x2 y k+1 y2 z k+1 z2 A k+1 d RTT ​j , f(u) represents the measurement state prediction function.

[0090] Step 5: According to the corresponding variance characteristics of each group, perform weighted summation on the weighted results obtained for each group again, and output the final position result.

[0091] Specifically, within the i-th classification group, calculate the variance magnitude of the positioning results of all communication devices included therein as the variance characteristic of this group.

[0092] One embodiment of the above various embodiments of the present disclosure has the following beneficial effects: Based on the rough estimation values of reference nodes (communication devices), high-precision estimation of the positions of unknown reference nodes and target devices is simultaneously achieved. The iterative LS method with a sliding window is used to solve the underdetermined problem equation, improve the smoothness of the positioning effect while correcting the phase distortion error, and give a rough estimation solution set of the problem; the classification and weighting method of SVM is used to constrain errors such as multipath effects in the rough estimation results, and further optimize the positioning results. Thus, without relying on other information source devices, high-precision estimation of the positions of reference nodes and target devices is quickly achieved in an indoor scenario where the position information of reference nodes is missing.

[0093] Further referring to Figure 3 , as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a device position determination device, and these device embodiments correspond to Figure 2 the method embodiments shown, and this device can be specifically applied to various electronic devices.

[0094] As Figure 3 shown, a device position determination device 300 of some embodiments includes: an acquisition unit 301, a monitoring unit 302, a first determination unit 303, and a second determination unit 304. Among them, the acquisition unit is configured to acquire the distance information between the target device and the communication device; the monitoring unit is configured to monitor the communication states of the target device and the communication device to obtain a status update signal; the first determination unit is configured to determine the rough position sets of the target device and the communication device according to the status update signal and the distance information; the second determination unit is configured to determine the positions of the target device and the communication device according to the rough position sets.

[0095] It can be understood that the various units described in this device 300 correspond to the respective steps in the method described with reference to Figure 2 . Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 300 and the units included therein, and will not be repeated here.

[0096] One embodiment among the above various embodiments of the present disclosure has the following beneficial effects: Based on the rough estimated values of the reference nodes (communication devices), high-precision estimation of the positions of both the unknown reference nodes and the target devices is achieved simultaneously. The iterative LS method with a sliding window is used to solve the underdetermination of the problem equation, improve the smoothness of the positioning effect while correcting the phase distortion error, and give a rough estimated solution set of the problem; the classification and weighting method of SVM is used to constrain errors such as multipath effects in the rough estimation results, and further optimize the positioning results. Thus, without relying on other information source devices, high-precision estimation of the positions of the reference nodes and the target devices is quickly achieved in an indoor scenario where the position information of the reference nodes is missing.

[0097] Reference is now made to Figure 4 , which shows a schematic structural diagram of an electronic device (such as a Figure 1 server in) 400 suitable for use in implementing some embodiments of the present disclosure. Figure 4 The electronic device shown is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

[0098] As Figure 4 shown, the electronic device 400 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 401, which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0099] Generally, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 4 the electronic device 400 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices may be alternatively implemented or had. Figure 4 Each block shown in

[0100] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program contains program codes for performing the methods shown in the flowcharts. In such some embodiments, the computer program can be downloaded and installed from the network via the communication device 409, or installed from the storage device 408, or installed from the ROM 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the methods of some embodiments of the present disclosure are performed.

[0101] It should be noted that the computer-readable medium in some embodiments of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0102] In some embodiments of the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program codes are carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.

[0103] The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable signal medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program codes contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0104] In some embodiments, the client and the server can communicate using any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0105] The above computer-readable medium can be included in the above electronic device; or can exist separately without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the electronic device, the electronic device is caused to: obtain distance information between a target device and a communication device; monitor the communication status of the above target device and the above communication device to obtain a status update signal; determine a rough location set of the above target device and the above communication device according to the above status update signal and the above distance information; and determine the locations of the above target device and the above communication device according to the above rough location set.

[0106] Computer program code for performing the operations of some embodiments of the present disclosure can be written in one or more programming languages or combinations thereof. The above programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0107] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in an order different from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0108] The units described in some embodiments of the present disclosure can be implemented in software or in hardware. The described units can also be provided in a processor. For example, it can be described as: a processor includes an acquisition unit, a monitoring unit, a first determination unit, and a second determination unit. Among them, the names of these units do not constitute a limitation on the unit itself in some cases. For example, the acquisition unit can also be described as "the unit for acquiring the distance information between the target device and the communication device".

[0109] The functions described above can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), Systems on Chip (SOCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0110] The above description is only some preferred embodiments of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, technical solutions formed by mutually replacing the above features with (but not limited to) technical features having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for determining the location of a device, comprising: Obtaining distance information between a target device and a communication device; Monitoring the communication status of the target device and the communication device to obtain a status update signal; Determining a rough location set of the target device and the communication device according to the status update signal and the distance information; Determining the locations of the target device and the communication device according to the rough location set.

2. The method according to claim 1, wherein, The step of determining the rough location set of the target device and the communication device according to the status update signal and the distance information includes: Constructing an estimated parameter vector according to the status update signal and the distance information; Establishing a location relationship equation according to the estimated parameter vector: where A represents the phase distortion error to be estimated, and d RTT represents the RTT ranging result obtained by the target device from an unknown communication device when the position of the unknown router is not known. AP(x1, y1, z1) represents the position of the communication device at an unknown location, P0(x2, y2, z2) represents the position of the target device to be determined, and a represents the systematic error; Determining the rough location set of the target device and the communication device according to the relationship equation.

3. The method according to claim 2, wherein The step of determining the rough location set of the target device and the communication device according to the relationship equation includes: Constructing a solution equation based on a sliding window; Setting the size of the sliding window; Updating the data filling within the sliding window according to epochs; Using an iterative least squares algorithm to solve the estimated parameter vector of the sliding window at different time periods to obtain the rough location set of the target device and the communication device.

4. The method according to claim 3, wherein, The solution equation of the sliding window is constructed according to the following formula: where n represents the size of the sliding window, and k represents the total number of unknown communication devices. and The superscript of represents the communication device number, and the subscript represents the data at the nth moment within the sliding window. The corresponding real time is t + n, where t is the real time corresponding to the first data in the sliding window. represents the residual vector of the target device and the kth base station at the nth moment in the sliding window. represents the coefficient vector of the target device and the kth base station at the nth moment in the sliding window. x2, y2, z2 are the positions of the target device to be determined. x1, y1, z1 and A1 are the position and phase distortion error of the first unknown communication device. x3…x k+1 , y3…y k+1 , z3…z k+1 and A3…A k+1 are the positions and phase distortion errors of the second to kth unknown communication devices. Δx2, Δy2, Δz2 are the corrections to the positions of the target device to be determined. Δx1, Δy1, Δz1 and ΔA1 are the corrections to the position and phase distortion error of the first unknown communication device. Δx3…Δx k+1 , Δy3…Δy k+1 , Δz3…Δz k+1 and ΔA3…ΔA k+1 are the corrections to the positions and phase distortion errors of the second to kth unknown communication devices, jointly constituting the second vector of parameters to be estimated {Δx1…Δx n Δy1…Δy n Δz1...Δz n ΔA1 ΔA3...ΔA n}.

5. The method according to claim 1, wherein The step of determining the locations of the target device and the communication device according to the location estimation set includes: Determining the maximum number of classifications of the rough location set and the classification center of each classification and performing the following determination steps: Inputting the rough location set into a pre-trained support vector machine model, classifying the rough location set according to the problem function, the maximum number of classifications, and the classification center to obtain a classification result, where the classification result includes at least one type of data set; Constructing a decision function and verifying the classification result according to the decision function to obtain a verification result; Determining the locations of the target device and the communication device according to the verification result.

6. The method according to claim 5, wherein, The step of determining the locations of the target device and the communication device according to the verification result includes: In response to the verification result satisfying a preset condition, for each type of data set in the classification result, determining the variance of the data in the data set, and performing weighted average summation on the data set using a cost function to obtain a weighted result; Performing weighted summation using the variance and the weighted result to obtain the locations of the target device and the communication device.

7. The method according to claim 5, wherein, The step of determining the locations of the target device and the communication device according to the verification result includes: In response to the verification result not satisfying the preset condition, adjusting the maximum number of classifications of the rough location set and the classification center of each classification according to the verification result, and repeating the determination steps.

8. A device location determination device, comprising: An acquisition unit configured to obtain distance information between a target device and a communication device; A monitoring unit configured to monitor the communication status of the target device and the communication device to obtain a status update signal; A first determination unit, configured to determine a rough position set of the target device and the communication device according to the status update signal and the distance information; A second determination unit, configured to determine the positions of the target device and the communication device according to the rough position set.

9. An electronic device, comprising: One or more processors; A storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-7.

10. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, the method according to any one of claims 1-7 is implemented.

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