A fast positioning method based on non-line-of-sight wireless signals from a mobile single station

Abstract

The present invention relates to the field of wireless positioning technology, and discloses a rapid positioning method based on non-line-of-sight wireless signals of a mobile single station, which is applied to an analysis device. The method includes: a positioning platform is arranged on the mobile single station, and the method includes: obtaining path data and signal data for fingerprint positioning; establishing a fingerprint database based on the path data and signal data; controlling the movement of the mobile single station based on a planning algorithm, and obtaining a first fingerprint feature of a target device through the positioning platform during the movement; obtaining at least one second fingerprint feature matching the first fingerprint feature from the database; and determining the location information of the target device based on a reference point corresponding to at least one second fingerprint feature. The fingerprint matching algorithm and path planning algorithm designed by the present invention can achieve passive positioning of contactless and connectionless wireless devices, and can achieve better positioning effects for the mobile single station under a shorter moving path than a random moving method.

Description

Technical Field

[0001] The present invention relates to the field of wireless positioning technology, and in particular to a rapid positioning method based on a mobile single-station non-line-of-sight wireless signal. Background Art

[0002] Currently, the market demand related to indoor positioning services is expanding. In modern cities, people spend about 80% of their lives in indoor environments.

[0003] Wireless positioning technology is widely used in indoor positioning due to its high accuracy and easy deployment. In addition to improving the accuracy of indoor positioning through various algorithms and hardware devices, how to reduce the complexity of the positioning system and make the indoor positioning system more universal and applicable to more practical scenarios is also one of the current challenges of indoor positioning.

[0004] In indoor wireless positioning, fingerprint positioning methods suitable for indoor use are usually used. In complex indoor environments, under low-cost conditions, spatiotemporal attributes such as angles and arrival times will produce large errors. Therefore, indoor positioning is usually achieved using fingerprints.

[0005] Traditional indoor wireless fingerprint positioning requires the deployment of a large number of wireless infrastructures (wireless sites) in the indoor environment to achieve reliable positioning. However, many indoor environments cannot meet such conditions, such as residential places. In addition, the indoor environment changes rapidly, such as store decoration or shopping mall upgrades, so it is very important to deploy a positioning system with low complexity.

[0006] Single-station positioning can solve the problem that wireless positioning requires the deployment of a large amount of infrastructure. A single base station can simplify deployment and configuration. Only one wireless base station needs to be deployed in the target area, without considering the location relationship and time synchronization between multiple base stations. However, current single-station positioning often relies on multiple antennas or the use of CSI signals, which means that single-station positioning usually requires some special hardware to support it.

[0007] In large indoor environments or in harsh environments such as large shopping malls or disaster relief sites, it is necessary to consider whether the wireless signal can cover the entire scene. Incomplete signal coverage will result in the inability to locate certain areas. In addition, general single-station positioning is mostly based on line-of-sight positioning, which is not suitable for complex indoor environments.

[0008] The limitations of traditional single-station positioning are that it requires special hardware, usually uses line-of-sight for positioning, and has a small coverage area. Summary of the invention

[0009] In view of this, the purpose of the present invention is to provide a rapid positioning method based on a mobile single-station non-line-of-sight wireless signal, so that it can solve the problem that the existing single-point positioning method is not suitable for indoor environments with complex environments.

[0010] The present invention solves the above technical problems by the following technical means:

[0011] In a first aspect, the present application provides a positioning method based on a non-line-of-sight wireless signal of a mobile single station, which is applied to an analysis device, wherein the analysis device includes a mobile single station and a positioning platform, and the positioning platform is arranged on the mobile single station. The method includes:

[0012] Obtain path data and signal data for fingerprint positioning;

[0013] Establishing a fingerprint database based on the path data and the signal data; wherein the path data is composed of a plurality of test points;

[0014] Controlling the movement of the mobile station based on a planning algorithm, and obtaining a first fingerprint feature of a target device through the positioning platform during the movement, wherein the first fingerprint feature includes signal data and path data;

[0015] Acquire at least one second fingerprint feature matching the first fingerprint feature from the database, where the second fingerprint feature is a fingerprint feature of a pre-recorded reference point;

[0016] Based on a reference point corresponding to at least one second fingerprint feature, location information of the target device is determined.

[0017] According to a preferred embodiment, the establishing of a fingerprint database based on the path data and the signal data comprises:

[0018] Establishing a fingerprint model based on the path data and the signal data;

[0019] Determining a fingerprint feature of a preset reference point corresponding to the trajectory based on the fingerprint model;

[0020] A fingerprint database is established based on the fingerprint features, and the database records reference point serial numbers, test point serial numbers and fingerprint features.

[0021] According to a preferred embodiment, before acquiring at least one second fingerprint feature matching the first fingerprint feature from the database based on the first fingerprint feature of the target device, the method further includes:

[0022] The signal data and path data in the first fingerprint feature are normalized and filtered to obtain a preprocessed first fingerprint feature.

[0023] According to a preferred embodiment, obtaining at least one second fingerprint feature matching the first fingerprint feature from the database includes:

[0024] Matching the first fingerprint feature with the fingerprint features of all reference points recorded in the database to obtain the similarity between the first fingerprint feature and the fingerprint features of all reference points;

[0025] The fingerprint feature of at least one reference point with the greatest similarity is determined as the second fingerprint feature.

[0026] According to a preferred embodiment, matching the first fingerprint feature with the fingerprint features of all reference points recorded in the database to obtain the similarity between the first fingerprint feature and the fingerprint features of all reference points includes:

[0027] The similarity between the first fingerprint feature and the fingerprint features of all reference points is determined by a similarity calculation formula, wherein the similarity calculation formula is:

[0028]

[0029] Where W represents the similarity between the first fingerprint feature and the fingerprint features of all reference points, e represents the trajectory edge label, and q∈[1,Q] represents the sampling point sequence number.

[0030] According to a preferred embodiment, determining the location information of the target device based on a reference point corresponding to at least one second fingerprint feature includes:

[0031] Recording a reference point corresponding to at least one second fingerprint feature as a target reference point, and acquiring position information corresponding to the second fingerprint feature of the target reference point based on an association relationship between the fingerprint feature and the position;

[0032] Based on the location information of the target reference point, the location information of the target device is determined.

[0033] According to a preferred embodiment, the method further comprises: repeatedly performing the following steps for a specified number of times:

[0034] Controlling the movement of the mobile station based on a planning algorithm, and obtaining a first fingerprint feature of a target device through the positioning platform during the movement, wherein the first fingerprint feature includes signal data and path data;

[0035] Acquire at least one second fingerprint feature matching the first fingerprint feature from the database, where the second fingerprint feature is a fingerprint feature of a pre-recorded reference point;

[0036] Based on a reference point corresponding to at least one second fingerprint feature, location information of the target device is determined.

[0037] In a second aspect, a rapid positioning method based on a non-line-of-sight wireless signal of a mobile single station is applied to a positioning device, the device is applied to an analysis device, the analysis device includes a mobile single station and a positioning platform, the positioning platform is arranged on the mobile single station, and the device includes:

[0038] A collection unit, which obtains path data and signal data for fingerprint positioning;

[0039] A modeling unit, which establishes a fingerprint database based on the path data and the signal data; wherein the path data is composed of a plurality of test points;

[0040] A control unit controls the movement of the mobile station based on a planning algorithm, and during the movement, obtains a first fingerprint feature of the target device through the positioning platform, wherein the first fingerprint feature includes signal data and path data;

[0041] an analyzing unit, acquiring from the database at least one second fingerprint feature matching the first fingerprint feature, wherein the second fingerprint feature is a fingerprint feature of a pre-recorded reference point;

[0042] The processing unit determines the location information of the target device based on a reference point corresponding to the at least one second fingerprint feature.

[0043] In a third aspect, the present application provides a computer storage medium, in which a computer program is stored. When the computer program is run on a computer, it can execute a rapid positioning method based on a mobile single-station non-line-of-sight wireless signal as described above.

[0044] Beneficial effects of the present invention:

[0045] 1. The present invention can automatically perform signal acquisition, feature extraction, data processing, fingerprint matching positioning, and path planning through a wireless mobile single station. Based on the combination of path and fingerprint positioning, a fingerprint model of path features can be designed, and feature extraction and matching positioning can be achieved through the movement of a mobile single station, which makes it unnecessary to deploy a large amount of infrastructure in the online stage of wireless fingerprint positioning, reducing the cost and complexity of wireless fingerprint positioning. The combination of mobile single station and wireless fingerprint matching breaks through the limitations of single station positioning, which has a limited positioning range and can only be performed in line-of-sight mode, and does not require the use of special hardware such as multiple antennas and CSI equipment.

[0046] 2. In terms of mobile single-station positioning, the fingerprint matching algorithm and path planning algorithm designed by the present invention can achieve passive positioning of contactless and connectionless wireless devices, while allowing the mobile single station to achieve better positioning effects in a shorter moving path than random movement.

[0047] 3. By combining mobile single station and wireless fingerprint positioning, there is no need to deploy a large amount of wireless infrastructure, and universal positioning can be achieved in special indoor scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The flowchart of the positioning method based on the non-line-of-sight wireless signal of a mobile single station of the present invention is as follows:

[0049] Figure 2 A comparison diagram of the positioning of a single station in the prior art of the present invention and the positioning of a mobile single station in the present invention;

[0050] Figure 3 It is a schematic diagram of the framework of the positioning method based on the non-line-of-sight wireless signal of a mobile single station of the present invention;

[0051] Figure 4 It is a schematic diagram of filtering and normalization in the method of the present invention;

[0052] Figure 5 It is a flow chart of controlling the movement of a single mobile station based on matching similarity in the method of the present invention;

[0053] Figure 6 A block diagram of the positioning of the present invention based on non-line-of-sight wireless signals of a single mobile station;

[0054] in,

[0055] 10. Acquisition unit; 20. Modeling unit; 30. Control unit; 40. Analysis unit; 50. Processing unit. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] The same or similar numbers in the figures of the embodiments of the present invention correspond to the same or similar parts. In the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "back", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the figure. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the figures are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0058] like Figure 1-5 As shown, the present application provides a fast positioning method based on non-line-of-sight wireless signals of a single mobile station, which is applied to analysis equipment, such as Figure 2 The analysis equipment includes a mobile station and a positioning platform, and the positioning platform is arranged on the mobile station. The mobile station includes a movable device and a single wireless site. The wireless site is deployed on the movable device to form a mobile station. The positioning platform is deployed on the mobile station to monitor and collect signal data of the target device to be located and run the positioning algorithm.

[0059] The method as a whole includes an offline stage and an online stage. In the offline stage, an offline fingerprint model about the correspondence between position and fingerprint is established, and the path data and signal data of all maps are collected at each reference point. These signal features are subject to data processing, which mainly includes filtering and normalization. Finally, a fingerprint database is established for matching in the online stage.

[0060] In the online stage, the mobile single-station movement is controlled based on the planning algorithm. The positioning platform extracts the fingerprint features of the target device in the single-station movement, matches and locates it with the fingerprint data in the fingerprint database after simple filtering and normalization, and obtains the corresponding matching results. Using the matching results of each online stage movement trajectory about the target device, with the help of path planning, a fastest path that can guarantee the positioning effect is found, and finally the position of the target device can be estimated through continuous movement.

[0061] Specifically: The method includes:

[0062] S1, obtaining path data and signal data for fingerprint positioning;

[0063] S2, establishing a fingerprint database based on the path data and the signal data; wherein the path data is composed of a plurality of test points;

[0064] S3, controlling the movement of the mobile station based on the planning algorithm, and obtaining a first fingerprint feature of the target device through the positioning platform during the movement, where the first fingerprint feature includes signal data and path data;

[0065] S4, acquiring at least one second fingerprint feature matching the first fingerprint feature from a database, where the second fingerprint feature is a fingerprint feature of a pre-recorded reference point;

[0066] S5. Determine location information of the target device based on a reference point corresponding to at least one second fingerprint feature.

[0067] Specifically, in step S1, path data and signal data for fingerprint positioning are obtained. The original data includes path data [(x, y), t] and signal data [RSS, t].

[0068] It is understandable that a fingerprint database is established based on the path data and the signal data; wherein the path data is composed of a plurality of test points. Specifically, the following steps are included:

[0069] S210, establishing a fingerprint model based on the path data and the signal data;

[0070] S220, deriving fingerprint features of the trajectory corresponding to the preset reference point based on the fingerprint model;

[0071] S230, establishing a fingerprint database based on the fingerprint features, wherein the database records reference point numbers, test point numbers and fingerprint features.

[0072] Each path data segment is represented by m discrete sampling points. The complex signal data is converted into a series of RSS signal data corresponding to the reference point on the path through normalization. Then filtering is performed. Horizontal filtering refers to removing abnormal data generated in the time sequence of the signal data according to the isolation forest method. Vertical filtering refers to removing abnormal data generated by multiple measurements, also using the isolation forest method. Finally, the fingerprint feature RSS of each reference point position on each path segment is obtained. The RSS is represented by the change value of m discrete RSS signal values ​​about the average value, recorded as RSSvary.

[0073] Each route is represented by Node and Edge. Node represents a set of sampling points, Node∈[1,N], Edge represents a set of edges, Edge∈[1,E]. RP and TP represent reference points and sampling points, RP∈[1,Q], TP∈[1,P]. The fingerprint database finally records the following data [RP_Edge,Avgoffline,RSSvary]. Among them, RP_Edge represents the serial number of RP and Edge corresponding to the fingerprint, and Avgoffline represents the average of a series of RSS values ​​of the fingerprint. The fingerprint model is as follows,

[0074]

[0075] RSS q，e，m =[RSSS q，e，1 , …, RSS q，e，m ]

[0076] In step S3, the movement of the mobile station is controlled based on the planning algorithm, and the fingerprint characteristics and path data of the target device are obtained based on the positioning platform. The first fingerprint characteristics of the target device are obtained through the positioning platform, and the first fingerprint characteristics include signal data and path data. Specifically, in the online stage, the mobile station obtains the RSS data and path data of the target to be located by moving on different trajectories. After normalization and simple filtering, the RSS data and path data of the target to be located are at the same latitude as the fingerprint data obtained in the offline stage, and RSS_ON = [RSS1, RSS2, ..., RSSm] and the RSS average value Avg of this trajectory are obtained.

[0077] In step S4, at least one second fingerprint feature matching the first fingerprint feature is obtained from the database, and the second fingerprint feature is a fingerprint feature of a pre-recorded reference point; thus, the data obtained in the online stage can be matched with the fingerprint data in the fingerprint database to obtain the matching similarity. The matching similarity refers to the similarity between the RSS data generated by the position of the target device to be located and the RSS data recorded at each RP position in the database, and the similarity in position can be obtained based on the similarity of the RSS data.

[0078] To calculate the matching similarity, we need to first calculate the Euclidean distance between the data obtained in the online phase and the fingerprint stored in the offline phase database, O_Distance = [O1,1,…,Oq,e,…,OQ,E], which represents the Euclidean distance of the RSS matching of the online phase and the offline phase for the qth RP on the eth trajectory edge. The specific calculation method of the Euclidean distance is as follows: First, the RSS mean AvgOnline obtained in the online phase is compared with the AvgOffline of each RP corresponding to this edge in the offline database. Here, taking one RP as an example, the absolute value difference a is obtained, as shown below.

[0079] a=|Aυg Offline -Aυg Online |

[0080] Then, the difference between the m data of RSSvary in the offline phase and RSS_ON in the online phase is calculated in turn, and the difference b of the change value is obtained by the Rootmean square formula, as shown in the following formula.

[0081]

[0082] Add a and b with certain weights to obtain the Euclidean distance Oq,e on edge e with respect to RP q, as shown in the following formula.

[0083] O q，e =α*a+β*b β=1-α

[0084] It should be noted that RSS has time differences, that is, when other conditions except time are the same, the RSS change value is the same but the RSS absolute value may be different. The present invention believes that RPs with a mean absolute value difference of more than 10dbm do not enter the reference range of positioning, and the Euclidean distance of the RP is given a sufficiently large value (for example, 10000).

[0085] After normalization, we get the matching degree between the target device and each reference point RP on this trajectory, that is, the matching similarity. The Euclidean distance of the trajectory is normalized and weighted to get W = [W1, e, ... Wq, e, ..., WQ, e], q∈[1, Q], e represents the calculated trajectory edge number, and W represents the matching similarity of the trajectory on each RP. The specific algorithm is as follows:

[0086] w q sum=Σw q，e e∈E

[0087]

[0088] In step S5, the location information of the target device is determined based on the reference point corresponding to the at least one second fingerprint feature. After obtaining the matching similarity, the first K most likely RPs are obtained through the WKNN algorithm to estimate the location of the target device, as shown in the following formula:

[0089]

[0090]

[0091] On the other hand, the matching similarity will be passed to the subsequent path planning algorithm to calculate the next trajectory to move. Specifically, the path planning algorithm based on mobile single-station fingerprint positioning is designed as follows: after each trajectory, a path planning and positioning algorithm will be performed. The matching similarity obtained for each trajectory will be saved in the empirical matching similarity and continuously updated. The updating method is to add the matching similarity of each RP accordingly. In this way, after several moves, the matching similarity weights of the most likely RPs will become larger and larger. The purpose of selecting the next path is to achieve faster positioning success, and the key to improving the positioning effect is the size of the matching similarity. The greater the difference in the matching degree between certain RPs and other RPs, the faster the threshold condition can be met. Because the mean difference a has already screened RPs once, the key factor determining the size of the matching similarity lies in the change of the fingerprint RSS recorded in the offline stage.

[0092] Each time the next edge is selected, the first K most likely RPs (that is, the RPs with the largest matching similarity) will be selected from the latest empirical matching similarity through the WKNN algorithm. This application believes that for these K RPs with the largest RSS change in the next selectable trajectory, more prominent fingerprint features can be obtained compared to other selectable trajectories, resulting in better positioning results. Therefore, the fingerprint data of these K RPs corresponding to each selectable edge is weighted and summed in the offline fingerprint database, and the edge e with a larger PathDe weight will be selected by the algorithm, as shown in the following formula:

[0093]

[0094] PathDe=∑(W*RSSDq,e)q∈R

[0095] However, single-station movement requires a positioning endpoint. K RPs with the largest matching similarity will be selected from the latest empirical matching similarity. When the sum of the empirical matching similarities Wsum of the K RPs is greater than the set TH, the positioning ends and the positioning result obtained by the matching algorithm is the final positioning result. Figure 5 shown.

[0096] In the second aspect, the embodiment of the present application also provides a positioning device based on a mobile single-station non-line-of-sight wireless signal, and the positioning device includes at least one software function module stored in a storage module in the form of software or firmware (Firmware) or in an operating system (OS) solidified in a control device. The acquisition unit is used to execute the executable modules stored in the storage module, such as the software function modules and computer program modules included in the positioning device based on a mobile single-station non-line-of-sight wireless signal.

[0097] The positioning device is applied to the analysis equipment, which includes a mobile single station and a positioning platform. The positioning platform is arranged on the mobile single station and combined with the attached Figure 6 The device includes a collection unit 10, a modeling unit 20, a control unit 30, an analysis unit 40 and a processing unit 50, and the functions of each unit may be as follows:

[0098] A collection unit 10, collecting path data and signal data for fingerprint positioning;

[0099] A modeling unit 20, which establishes a fingerprint database based on the path data and the signal data; wherein the path data is composed of a plurality of test points;

[0100] The control unit 30 controls the movement of the mobile station based on the planning algorithm, and obtains the first fingerprint feature of the target device through the positioning platform during the movement, where the first fingerprint feature includes signal data and path data;

[0101] The analyzing unit 40 acquires at least one second fingerprint feature matching the first fingerprint feature from a database, where the second fingerprint feature is a fingerprint feature of a pre-recorded reference point;

[0102] The processing unit 50 determines the location information of the target device based on the reference point corresponding to the at least one second fingerprint feature.

[0103] The specific use principles of the collection unit 10, the modeling unit 20 and the control unit 30 can be referred to the positioning method based on the non-line-of-sight wireless signal of a mobile single station mentioned above, and will not be described in detail here.

[0104] In a third aspect, the present application also provides a computer storage medium, in which a computer program is stored. When the computer program runs on a computer, it can execute the above-mentioned rapid positioning method based on non-line-of-sight wireless signals of a mobile single station.

[0105] Through the description of the above implementation methods, technical personnel in this field can clearly understand that the present application can be implemented through hardware, and can also be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a simulation device, or a network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0106] In summary, the present application provides a rapid positioning method based on non-line-of-sight wireless signals of a mobile single station, which is applied to an analysis device, the analysis device includes a mobile single station and a positioning platform, the positioning platform is arranged on the mobile single station, and the method includes: obtaining path data and signal data for fingerprint positioning; establishing a fingerprint database based on the path data and the signal data; wherein the path data is composed of multiple test points; controlling the movement of the mobile single station based on a planning algorithm, and obtaining a first fingerprint feature of the target device through the positioning platform during the movement, wherein the first fingerprint feature includes signal data and path data; obtaining at least one second fingerprint feature matching the first fingerprint feature from the database, wherein the second fingerprint feature is a fingerprint feature of a pre-recorded reference point; determining the location information of the target device based on the reference point corresponding to at least one second fingerprint feature. In the present application, the signal collection, feature extraction, data processing, fingerprint matching positioning, and path planning processes can be automated through a wireless mobile single station. Based on the combination of path and fingerprint positioning, a fingerprint model of path features can be designed, and feature extraction and matching positioning can be realized by moving the mobile single station, which makes it unnecessary to deploy a large amount of infrastructure in the online stage of wireless fingerprint positioning, reducing the cost and complexity of wireless fingerprint positioning. The combination of mobile single station and wireless fingerprint matching breaks through the limitations of single-station positioning, which has limited positioning range and can only be performed in line-of-sight mode, and does not require the use of special hardware such as multi-antennas and CSI equipment.

[0107] In the embodiments provided in the present application, it should be understood that the disclosed device, system and method can also be implemented in other ways. The above-described device, system and method embodiments are merely schematic, for example, the flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a part of a module, a program segment or a code, and a part of a module, a program segment or a code includes one or more executable instructions for implementing a specified logical function. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented by a dedicated hardware-based system that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. In addition, each functional module in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0108] The above are only embodiments of the present application and are not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A rapid positioning method based on non-line-of-sight wireless signals from a single mobile station, characterized in that: Applied to an analysis device, the analysis device comprises a mobile single station and a positioning platform, the positioning platform is arranged on the mobile single station, and the method comprises: Obtain path data and signal data for fingerprint positioning; Establishing a fingerprint database based on the path data and the signal data; wherein the path data is composed of a plurality of test points; Controlling the movement of the mobile station based on a planning algorithm, and obtaining a first fingerprint feature of a target device through the positioning platform during the movement, wherein the first fingerprint feature includes signal data and path data; Acquire at least one second fingerprint feature matching the first fingerprint feature from the database, where the second fingerprint feature is a fingerprint feature of a pre-recorded reference point; Determining location information of the target device based on a reference point corresponding to at least one second fingerprint feature; Wherein, establishing a fingerprint database based on the path data and the signal data includes: Establishing a fingerprint model based on the path data and the signal data; Based on the fingerprint model, a fingerprint feature of a preset reference point corresponding to the mobile single station movement process is obtained; Establishing a fingerprint database based on the fingerprint features, wherein the database records reference point serial numbers, test point serial numbers and fingerprint features; The first fingerprint feature is matched with the fingerprint features of all reference points recorded in the database to obtain the similarity between the first fingerprint feature and the fingerprint features of all reference points, including: The similarity between the first fingerprint feature and the fingerprint features of all reference points is determined by a similarity calculation formula, wherein the similarity calculation formula is: Where W represents the similarity between the first fingerprint feature and the fingerprint features of all reference points, e represents the track edge label, and q∈[1,Q] represents the sampling point sequence number; Where O q,e = α * α + β * b, β = 1 - α, where: In the formula, O q,e represents the Euclidean distance between the fingerprint feature of the reference point q on the track edge e and the first fingerprint feature, α and β represent the weights set for a and b, and Avg Offline Represents the average offline RSS data of the reference point in the track edge e, Avg Online Represents the average value of RSS data in the first fingerprint feature, RSS q,e,i It represents the difference between the offline RSS data of the ith reference point among the m reference points and the average offline RSS data. Represents the RSS data in the first fingerprint feature corresponding to the i-th reference point.

2. The method according to claim 1, characterized in that Before acquiring at least one second fingerprint feature matching the first fingerprint feature from the database based on the first fingerprint feature of the target device, the method further includes: The signal data and path data in the first fingerprint feature are normalized and filtered to obtain a preprocessed first fingerprint feature.

3. The method according to claim 1, characterized in that: Acquiring at least one second fingerprint feature matching the first fingerprint feature from the database includes: Matching the first fingerprint feature with the fingerprint features of all reference points recorded in the database to obtain the similarity between the first fingerprint feature and the fingerprint features of all reference points; The fingerprint feature of at least one reference point with the greatest similarity is determined as the second fingerprint feature.

4. The method according to claim 2, characterized in that: Determining location information of the target device based on a reference point corresponding to at least one second fingerprint feature includes: Recording a reference point corresponding to at least one second fingerprint feature as a target reference point, and acquiring position information corresponding to the second fingerprint feature of the target reference point based on an association relationship between the fingerprint feature and the position; Based on the location information of the target reference point, the location information of the target device is determined.

5. The method according to claim 4, characterized in that The method further comprises: Repeat the following steps a specified number of times: Control the mobile station to move along a new path obtained based on the planning algorithm, and in the process of moving along the new path, obtain a first fingerprint feature of the target device through the positioning platform, wherein the first fingerprint feature includes signal data and path data; Acquire at least one second fingerprint feature matching the first fingerprint feature from the database, where the second fingerprint feature is a fingerprint feature of a pre-recorded reference point; Based on a reference point corresponding to at least one second fingerprint feature, location information of the target device is determined.

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