Three-dimensional space GPS navigation system

Abstract

The invention discloses a three-dimensional space GPS navigation system. A three-dimensional space GPS navigation system comprises: a map information collection device for acquiring a GPS satellite map of a target area; the unmanned aerial vehicle flying device flies to the high altitude of the target area and is in signal connection with the signal base station or a GPS satellite so as to position the position of the unmanned aerial vehicle in the high altitude; the high-altitude image acquisition module is used for acquiring high-altitude image data of the target area and establishing a three-dimensional scene model based on the high-altitude image data; and the navigation information generation module is used for determining the position in the three-dimensional scene model based on the position of the unmanned aerial vehicle and generating a target route according to the three-dimensional scene model. The navigation scheme provided by the invention can effectively reduce the influence of natural plant growth on navigation when being used in the field, and can accurately find the satellite route through the constructed three-dimensional scene model.

Description

Technical Field

[0001] This application relates to the technical field of neural network processing, and more particularly, to a three-dimensional space GPS navigation system. Background Art

[0002] When implementing navigation operations in complex mountain terrains, the application of satellite positioning technology faces dual technical constraints: insufficient signal stability: the effectiveness of satellite positioning technology highly depends on continuous and stable signal reception. However, in mountainous terrains, topographical features such as canyons and steep slopes are prone to forming signal shielding areas, resulting in the interruption of satellite signal links or a signal-to-noise ratio (SNR) lower than the receiver capture threshold (usually ≤ 25 dB-Hz). Lack of geographical information dimension: Traditional two-dimensional contour maps lack the ability to analyze three-dimensional terrain. During the stagnation of GNSS positioning data updates, path dynamic correction cannot be achieved through the spatial topological relationship between terrain elevation features and the travel trajectory, resulting in the cumulative expansion of navigation deviation.

[0003] The above technical defects lead to the following problems in actual operations: When there is no support from continuous positioning signals, two-dimensional maps cannot generate safe path guidance that conforms to the terrain undulations; affected by the multipath effect and the fluctuation of the number of visible satellites (Visible Satellites < 4), the positioning horizontal error can reach 10 - 30 meters, making it difficult to meet the navigation accuracy requirements for narrow ridges or cliff terrains; the positioning blind areas caused by signal shielding force operators to rely on experience to judge paths, significantly increasing the travel risks. Summary of the Invention

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

[0005] As the first aspect of this application, to solve the technical problems mentioned in the above background art section, some embodiments of this application provide a three-dimensional space GPS navigation system, including: A map information collection device that obtains a GPS satellite map of the target area; An unmanned aerial vehicle (UAV) flight device that flies to a high altitude in the target area and connects to a signal base station signal or a GPS satellite signal to locate the position of the UAV at high altitude; A high-altitude image acquisition module that acquires high-altitude image data of the target area and establishes a three-dimensional scene model based on the high-altitude image data; A navigation information generation module that determines the position in the three-dimensional scene model based on the position of the UAV and generates a target route according to the three-dimensional scene model.

[0006] In the technical solution provided by this application, according to the characteristics of difficult navigation in mountainous areas, an unmanned aerial vehicle (UAV) flight device is used to obtain high-altitude images. At the same time, taking advantage of the fact that there are fewer mountains at high altitudes and it is easier to obtain signals, the location of the UAV is obtained, and the location of the target is determined based on the location of the UAV, so as to generate an optimal path in the three-dimensional scene model. The navigation solution provided by this application can effectively reduce the impact of natural plant growth on navigation when used in the wild, and accurately find the satellite route through the constructed three-dimensional scene model.

[0007] Further, the GPS satellite map of the target area includes the average altitude information of each area.

[0008] In the technical solution provided by this application, the average altitude information of the target area is obtained, so the basic three-dimensional model of the target area can be determined, and the approximate contour of the target area is constructed using the average altitude information.

[0009] When positioning in the wild, due to signal occlusion between mountains and the shielding effect of a large number of shrubs and forests on signals. So even when flying into the sky, it is very difficult to accurately position. For this reason, this application provides the following technical solution:

[0010] The UAV flight device includes: A UAV body for flying into the high altitude of the target area; A base station information communication module for establishing a signal connection with surrounding signal base stations and obtaining the location information of surrounding base stations; A distance calculation module for determining the distance between the UAV body and surrounding base stations based on the communication loss with surrounding base stations; A positioning module for determining the location of the UAV body according to the distance between the UAV body and surrounding base stations.

[0011] In the technical solution provided by this application, taking advantage of the fact that the UAV can detect weak signals emitted by surrounding base stations at high altitudes to obtain the actual identity of the base stations, determining the locations of surrounding base stations according to the identities of the base stations, and then determining the distance between the UAV and surrounding base stations based on the signal loss situation, so as to accurately determine the location of the UAV body. After determining the location of the UAV body, the current location can be marked on the GPS satellite map to complete the direction guidance work.

[0012] When determining the current location based on the distance from the base station, sufficient positioning accuracy is required. For this reason, this application provides the following technical solution:

[0013] Further, the base station information communication module obtains the location information of base stations with the difference in three direction vectors exceeding a preset threshold.

[0014] In this solution, the base stations whose difference in three direction vectors exceeds a preset threshold are selected. The directions of these base stations are all different from the direction of the drone. Therefore, the vector angles formed by the connection lines between the drone and each base station are relatively large, and the positioning calculation has higher accuracy.

[0015] Furthermore, the calculation formula for the distance between the drone body and the surrounding base stations is as follows: ; where, L 0 is the reference distance, n is the environmental attenuation factor, d 0 is the path loss, X σ is the shadow fading margin, d represents the actual distance from the drone to the base station, d 0 represents the reference distance, and L represents the path loss.

[0016] In the technical solution provided by this application, according to the environmental attenuation factor, the signal interference degree received by the drone is predicted. In practice, because the drone is at a high altitude and the propagation medium between it and the signal base station is mostly air, the attenuation is stable, and thus the actual distance from the drone to the surrounding base stations can be accurately calculated.

[0017] The environment in each region is different, and when encountering complex field situations, the attenuation of signals may not be consistent. To address this issue, this application provides the following technical solution:

[0018] Furthermore, a long short-term memory model is pre-trained. In the long short-term memory model, the input data is the effective mountain length between the signal base station and the target connection line, and the output data are the shadow fading margin and the environmental attenuation factor.

[0019] In the technical solution provided by this application, the long short-term memory model is used to find the influence relationship between the shadow fading margin and the environmental attenuation factor and the signal blocked by the mountain, so that the shadow fading margin and the environmental attenuation factor can be accurately calculated. Furthermore, when calculating the distance between the drone body and the surrounding base stations subsequently, it has higher accuracy.

[0020] Furthermore, the long short-term memory model includes: an input layer for inputting the effective mountain length; The LSTM unit selectively retains or forgets information through a gating mechanism; An output layer that generates the shadow fading margin and the environmental attenuation factor based on the information output by the LSTM unit.

[0021] Furthermore, the LSTM unit includes: An input gate for controlling the inflow of new information: I t =σ(W xi xt +W hi h t-1 +b i ); Among them, I t表示 is the output of the input gate, t is the current time step, W xi represents the weight matrix input to the input gate, x t represents the input vector, W hi represents the weight matrix from the hidden state to the input gate, b i represents the input gate bias term; σ represents the Sigmoid function, h t-1 represents the hidden state at the previous moment Forget gate, used to determine the retention ratio of old information; f t = σ (W xf x t + W hf h t-1 + b f ); W xf represents the weight matrix input to the forget gate, W hf the weight matrix from the hidden state to the forget gate, b f represents the forget gate bias term, f t represents the output of the forget gate; Candidate cell state: C t = tanh (W xc x t + W hc h t-1 + b c ); W xc represents the weight matrix input to the candidate state, W hc represents the weight matrix from the hidden state to the candidate state, b c represents the candidate state bias term, C t represents the candidate state; Cell state update unit, used to integrate the input gate, forget gate and candidate cell state to generate output information; D t = f t ⊙ D t-1 + I t ⊙ C t , D t表示 is the output information of the LSTM unit, ⊙ represents element-wise multiplication.

[0022] In the technical solution provided by this application, by quantifying the reference distance into time tags, the information development law between the shadow fading margin and the environmental factor under different distance information is found. Furthermore, when the effective mountain length is known, the shadow fading margin and the environmental factor can be accurately calculated. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings constituting a part of this application are used to provide a further understanding of this application, making other features, objectives, and advantages of this application more obvious. The schematic embodiments and descriptions of the drawings of this application are used to explain this application and do not constitute an improper limitation to this application.

[0024] In addition, throughout the 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 components are not necessarily drawn to scale.

[0025] In the drawings: Figure 1 It is a schematic structural diagram of a three-dimensional space GPS navigation system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] Embodiments of this application will be described in more detail below with reference to the drawings. Although some embodiments of this application are shown in the drawings, it should be understood that this application 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 this application. It should be understood that the drawings and embodiments of this application are only for exemplary purposes and are not used to limit the protection scope of this application.

[0027] In addition, it should be noted that for the sake of convenience of description, only the parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in this application and the features in the embodiments can be combined with each other.

[0028] This application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0029] Refer to Figure 1 , the three-dimensional space GPS navigation system includes: a map information collection device, a drone flight device, an aerial image acquisition module, and a navigation information generation module. Among them, the map information collection device obtains the GPS satellite map of the target area; the drone flight device flies to the high altitude of the target area and is connected to the signal base station signal or the GPS satellite signal to locate the position of the drone at high altitude;

[0030] An aerial image acquisition module acquires aerial image data of a target area and establishes a three-dimensional scene model based on the aerial image data; a navigation information generation module determines its position in the three-dimensional scene model based on the position of the unmanned aerial vehicle (UAV) and generates a target route according to the three-dimensional scene model.

[0031] The GPS satellite map of the target area includes the average elevation information of each area. Specifically, the map information collection device obtains a 1:10,000 scale GPS satellite map of the target area from the National Geomatics Center of China, with a data accuracy better than 5 meters and a grid resolution of 10 meters × 10 meters. The ArcGIS Pro software is used to preprocess the original DEM data, including terrain smoothing filtering (using a 3×3 median filter kernel), hillshade generation (sun elevation angle 45°), and terrain feature extraction (ridge lines, cliff boundaries, slope grading).

[0032] The target area is the specific area required for navigation. For example, if navigation is required within a certain mountainous area, the map information of this mountainous area is pre-entered. Thus, the elevation of each position in this mountainous area and the actual situation of the corresponding three-dimensional scene information can be marked on the digital map.

[0033] When navigating in a mountainous area, maps alone are not sufficient for precise navigation, and the current actual position also needs to be obtained. However, satellite signals and base station signals are very weak in mountainous areas, making it impossible to accurately locate. Therefore, in this solution, a high-altitude UAV is used. After the high-altitude UAV ascends vertically to a preset height, it communicates with satellites or surrounding signal base stations, and determines the current position based on satellite signals or the signals of surrounding base stations.

[0034] Specifically, the UAV flight device includes: a UAV body, a base station information communication module, a distance calculation module, and a positioning module. The UAV body is used to fly into the high altitude of the target area; the base station information communication module is used to establish a signal connection with surrounding signal base stations and obtain the position information of surrounding base stations; the distance calculation module determines the distance between the UAV body and surrounding base stations based on the communication loss with surrounding base stations; the positioning module determines the location of the UAV body according to the distance between the UAV body and surrounding base stations.

[0035] Furthermore, the base station information communication module obtains the position information of base stations with the difference in three direction vectors exceeding a preset threshold.

[0036] Specifically, after the UAV body ascends to a height of 200 - 300 meters, it starts a 360° omnidirectional signal scan, and filters out effective base stations through the RSSI intensity threshold (≥ -90dBm). Three base stations with the optimal spatial distribution (azimuth angle difference ≥ 120°) are selected, and the precise coordinates are obtained through querying the LTE base station database. The distance calculation module uses a dual-frequency signal fusion algorithm.

[0037] The calculation formula for the distance between the UAV body and the surrounding base stations is as follows: ; Among them, L 0 is the reference distance, n is the environmental attenuation factor, d 0 is the path loss, X σ is the shadow fading margin, d represents the actual distance from the UAV to the base station, d 0 represents the reference distance, and L represents the path loss.

[0038] In this solution, the distance between the UAV body and the surrounding base stations is calculated according to the attenuation degree of the signal. After obtaining the distance between the UAV body and the surrounding base stations, the horizontal distance between the UAV body and each base station can be calculated based on the altitude of the current position of the UAV, so as to obtain the actual position of the UAV in the map information.

[0039] When calculating the actual distance between the UAV and each signal base station, accurate environmental attenuation factors and shadow fading margins need to be obtained. For this reason, in this solution; a long short-term memory model is pre-trained, and the input data in the long short-term memory model is the effective mountain length between the signal base station and the target connection line, and the output data is the shadow fading margin and the environmental attenuation factor.

[0040] Specifically, sufficient sample data is pre-obtained. The sample data includes input data and labels. The labels are the shadow fading margin and the environmental attenuation factor, and the input data is the effective mountain length.

[0041] In time, there is an obvious distance relationship between the shadow fading margin and the environmental attenuation factor and the length of the mountain body penetrated by the signal. For this reason, a long short-term memory model is used in this solution to process the relationship between the two, so as to accurately find the change relationship between the shadow fading margin and the environmental attenuation factor when the mountain body length changes.

[0042] Furthermore, the effective mountain length is the length of the mountain body penetrated by the connection line between the UAV body and the base station. In practice, because the specific position of the UAV is not clear. The possible position and direction of the UAV can be calculated first using the initially set shadow fading margin and environmental attenuation factor. Then, after the UAV body stabilizes at high altitude, the current altitude is read out with a barometer. In this way, the positions between the base station and the UAV can be roughly determined. Because the UAV body is at a high altitude in the mountainous area, the signal link between the UAV body and the base station only needs to penetrate the mountain body near the base station. In the case of obtaining the signal transmission direction, the distance of the mountain body that the signal needs to penetrate can be calculated according to the elevation information in the GPS satellite map, and this distance is used as the "effective mountain length".

[0043] The long short-term memory model includes: an input layer for inputting the effective mountain length; LSTM unit, selectively retaining or forgetting information through a gating mechanism; Output layer, generating shadow fading margin and environmental attenuation factor based on the information output by the LSTM unit.

[0044] Furthermore, the LSTM unit includes: Input gate, used to control the inflow of new information: I t =σ(W xi x t +W hi h t-1 +b i ); Among them, I t表示 is the output of the input gate, t is the current time step, W xi represents the weight matrix input to the input gate, x t represents the input vector, W hi represents the weight matrix from the hidden state to the input gate, b i represents the input gate bias term; σ represents the Sigmoid function, h t-1 represents the hidden state at the previous moment; Forget gate, used to determine the retention ratio of old information; f t =σ(W xf x t +W hf h t-1 +b f ); W xf represents the weight matrix input to the forget gate, W hf the weight matrix from the hidden state to the forget gate, b f represents the forget gate bias term, f t represents the output of the forget gate; Candidate cell state: C t =tanh(W xc x t +W hc h t-1 +b c );W xc represents the weight matrix input to the candidate state, W hc represents the weight matrix from the hidden state to the candidate state, b c represents the candidate state bias term, C t represents the candidate state; Cell state update unit, used to integrate the input gate, forget gate, and candidate cell state to generate output information; D t =f t⊙D t-1 +I t ⊙C t ,D t表示 The output information of the LSTM cell, where ⊙ represents element-wise product.

[0045] The hidden state output gate is signal-connected to the cell state update unit: ; ; Among them, W xo is the weight matrix corresponding to the input vector of the output gate, W ho is the weight matrix corresponding to the hidden state of the output gate, and b o is the bias vector of the output gate.

[0046] The above is the general structure of the LSTM cell. In actual use, the long short-term memory model needs to be trained. The specific method is as follows: Step 1: Pre-configure a large number of training samples. The input data in the training samples is the effective mountain length, and the label is "environmental attenuation factor and shadow fading margin". The number of samples is 1000.

[0047] Step 2: Divide the samples into a training set and a validation set. Input the training set into the long short-term memory model, and make the training set into an input sequence {x 1 , x 2 ,...}, and make the label into an output sequence {y 1 , y 2 ,...}.

[0048] Step 3: Forward propagation: For each time step t, calculate h t and D t , and map the finally output h t to the predicted value y'; Step 4: Use the mean squared error as the loss function and train the long short-term memory model through backpropagation through time.

[0049] In this way, using the long short-term memory model can accurately calculate the environmental attenuation factor and shadow fading margin.

[0050] After calculating the distances between the UAV body and several surrounding base stations, UAV body positioning is required. The specific method includes the following steps: S1: Obtain 3 base stations that can transmit signals with the UAV body, and obtain the longitude and latitude of each base station; S2: Select one base station as the origin of the plane rectangular coordinate system, and convert the longitude and latitude of each base station into the coordinates of the plane rectangular coordinate system.

[0051] S3: Calculate the distance weight information α of each base station with respect to the UAV body k , where k is the index of the base station; ; Among them, is the distance weight information of the k-th base station, represents the minimum effective RSSI, the maximum effective RSSI, represents the received signal strength of the k-th base station, represents the dilution of precision of the i-th emergency, represents the altitude of the UAV body (calculated by the barometer), represents the altitude of the k-th base station, and dk represents the distance between the k-th base station and the UAV body; S4: Calculate the coordinates of the UAV body in the plane rectangular coordinate system; ; ; Among them, Ep is the abscissa of the UAV body in the plane rectangular coordinate system, and Np is the ordinate of the UAV body in the plane rectangular coordinate system, is the abscissa of the k-th base station in the plane rectangular coordinate system, is the ordinate of the k-th base station in the plane rectangular coordinate system.

[0052] Because the UAV body takes off vertically, the abscissa and ordinate of the user in the plane rectangular coordinate system can be obtained. Then, by converting the abscissa and ordinate into longitude and latitude, the accurate position of the user can be obtained.

[0053] After obtaining the accurate position of the user, the current position can be marked on the GPS satellite map of the target area.

[0054] Because there are a large number of plants in mountainous areas, the roads will change. Therefore, it is necessary to obtain the surrounding terrain as much as possible. In this solution, a high-altitude image acquisition module is used to obtain the high-altitude image data of the target area and establish a three-dimensional scene model based on the high-altitude image data; a navigation information generation module determines the position in the three-dimensional scene model based on the position of the UAV and generates a target route according to the three-dimensional scene model.

[0055] Specifically, the high-altitude image data is directly obtained by the UAV body taking pictures at high altitude. After obtaining the high-altitude image data, the image data needs to be converted into three-dimensional point cloud data. Obtaining three-dimensional information from high-altitude image data is a prior art. The following is a possible implementation method.

[0056] Specifically, it includes the following steps: St1: The UAV body collects high-altitude image information in a zigzag flight path above the user.

[0057] St2: Use the SIFT algorithm to detect key points in the high-altitude image information.

[0058] St3: Quickly match different images in the high-altitude image information based on the key points to obtain matching feature points, and generate a matching matrix according to the feature matching points.

[0059] The so-called matching matrix is actually the position information that can be matched with each other in pictures from different perspectives.

[0060] St4: Perform linear triangulation on the matching feature points to generate a sparse point cloud.

[0061] St5: Use multi-view stereo matching to generate a dense point cloud, and generate three-dimensional model information based on the dense point cloud. The reconstruction of the above three-dimensional point cloud information is a prior art and will not be described in further detail here.

[0062] In satellite maps, generally only the actual altitude of the nearby ground is available. Therefore, it is actually impossible to accurately find the travel route using satellite maps. In this solution, three-dimensional model information will be generated, which describes the surrounding vegetation coverage, and thus can better perform navigation.

[0063] The navigation information generation module determines its position in the three-dimensional scene model based on the position of the UAV, and generates a target route according to the three-dimensional scene model. When in use, the navigation information generation module will try to select a route with a small height difference for the user's reference. In practice, the user can completely select a suitable route according to the current position and the satellite map.

[0064] The above description is only some preferred embodiments of the present application 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 application is not limited to the technical solutions formed by the specific combination of the above technical features, but 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, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in the embodiments of the present application.

Claims

1. A three-dimensional GPS navigation system, characterized in that: include: A map information collection device to obtain a GPS satellite map of the target area; The UAV flying device flies to the high altitude of the target area and connects with the signal base station signal or GPS satellite signal to locate the position of the UAV at high altitude; A high-altitude image acquisition module acquires high-altitude image data of the target area and establishes a three-dimensional scene model based on the high-altitude image data; The navigation information generation module determines the position of the UAV in the three-dimensional scene model based on the position of the UAV, and generates a target route according to the three-dimensional scene model.

2. The three-dimensional GPS navigation system according to claim 1, characterized in that: The GPS satellite map of the target area includes average altitude information of each area.

3. The three-dimensional GPS navigation system according to claim 2, characterized in that: The UAV flight device includes: The drone body is used to fly high into the target area; The base station information communication module is used to establish a signal connection with surrounding signal base stations and obtain the location information of surrounding base stations; The distance calculation module determines the distance between the drone and surrounding base stations based on the communication loss with surrounding base stations; The positioning module determines the location of the drone body according to the distance between the drone body and the surrounding base stations.

4. The three-dimensional GPS navigation system according to claim 3, characterized in that: The base station information communication module obtains the location information of the base stations whose 3 direction vector differences exceed a preset threshold.

5. The three-dimensional GPS navigation system according to claim 3, characterized in that: The calculation formula of the distance between the drone body and the surrounding base stations is as follows: ; Among them, L0 is the reference distance, n is the environmental attenuation factor, d0 is the path loss, X σ is the shadow fading margin, d represents the actual distance from the UAV to the base station, d0 represents the reference distance, and L represents the path loss.

6. The three-dimensional GPS navigation system according to claim 5, characterized in that: A long short-term memory model is pre-trained, wherein the input data of the long short-term memory model is the effective mountain length between the signal base station and the target line, and the output data is the shadow fading margin and the environmental attenuation factor.

7. The three-dimensional GPS navigation system according to claim 6, characterized in that: The long short-term memory model includes: Input layer, used to input effective mountain length; LSTM units selectively retain or forget information through a gating mechanism; The output layer generates shadow fading margin and environmental attenuation factor based on the information output by the LSTM unit.

8. The three-dimensional GPS navigation system according to claim 7, characterized in that: The LSTM unit consists of: Input gate, used to control the inflow of new information: I t =σ(W xi x t +W hi h t-1 +b i ); Among them, I t表示 The output of the input gate, t is the current time step, W xi represents the weight matrix input to the input gate, x t represents the input vector, W hi represents the weight matrix from the hidden state to the input gate, b i represents the input gate bias term; σ represents the Sigmoid function, h t-1 Indicates the hidden state at the previous moment; The forget gate is used to determine the proportion of old information to be retained; f t =σ(W xf x t +W hf h t-1 +b f ); W xf Represents the weight matrix input to the forget gate, W hf The weight matrix from hidden state to forget gate, b f represents the forget gate bias term, f t Represents the output of the forget gate; Candidate cell states: C t =tanh(W xc x t +W hc h t-1 +b c ); W xc Represents the weight matrix input to the candidate state, W hc represents the weight matrix from hidden state to candidate state, b c represents the candidate state bias term, C t Indicates candidate status; The cell state update unit is used to integrate the input gate, forget gate and candidate cell states to generate output information; D t =f t ⊙D t-1 +I t ⊙C t , D t表示 Output information of LSTM unit, ⊙ represents element product; The hidden state output gate is connected to the cell state update unit signal: ； ； Among them, W xo is the weight matrix of the output gate corresponding to the input vector, W ho is the weight matrix of the hidden state corresponding to the output gate, b o is the bias vector of the output gate.

9. The three-dimensional GPS navigation system according to claim 1, characterized in that: The positioning method of the drone body includes the following steps: S1: Obtain three base stations that can transmit signals to the drone body, and obtain the longitude and latitude of each base station; S2: Select a base station as the origin of the plane rectangular coordinate system, and convert the longitude and latitude of each base station into the coordinates of the plane rectangular coordinate system; S3: Calculate the distance weight information αk of each base station to the drone body, where k is the index of the base station; ； in, is the distance weight information of the kth base station, Indicates the minimum effective RSSI, Maximum effective RSSI, represents the received signal strength of the kth base station, represents the geometric precision factor of the ith emergency department, h p Indicates the altitude of the drone, h c k represents the altitude of the kth base station, d k represents the distance between the kth base station and the drone body; S4: Calculate the coordinates of the drone body in the plane rectangular coordinate system; ； ; Among them, Ep is the horizontal coordinate of the drone body in the plane rectangular coordinate system, and Np is the vertical coordinate of the drone body in the plane rectangular coordinate system. is the horizontal coordinate of the kth base station in the plane rectangular coordinate system, is the ordinate of the kth base station in the plane rectangular coordinate system.

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