Matching Method and System Based on 5G Network, Beidou Positioning and Non-Line-of-Sight Microwave

Abstract

The present invention discloses a matching method and system based on 5G network, Beidou positioning and non-line-of-sight microwave, which relates to the technical field of target signal positioning. The matching method obtains high-precision geographical location information and real-time signal strength data, constructs a calibration data set combining static and dynamic, supports the adaptive direction adjustment and rapid focusing of the antenna, and realizes efficient communication and precise positioning in complex scenarios. Training a calibration matching model based on the calibration data set improves the accuracy and adaptability of the model in multiple geographical environments and non-line-of-sight conditions, provides an efficient antenna positioning mechanism, and ensures the stability and real-time performance of the communication link; this method is particularly applicable to complex environments such as disaster relief, can quickly and accurately locate accident personnel, and significantly improve the rescue efficiency and the reliability of operations.

Description

Technical Field

[0001] The present invention relates to the field of target signal positioning technology, and in particular to a matching method and system based on 5G network and Beidou positioning and non-line-of-sight microwave. Background Art

[0002] With the frequent occurrence of large-scale gatherings and sudden disasters, the existing communication system is facing tremendous pressure, which may lead to communication interruption or even paralysis. Therefore, improving the resilience and response speed of emergency support has become the focus of the communications industry. By combining Beidou satellite positioning, 5G communication and non-line-of-sight microwave antenna technology, it is possible to quickly locate and dispatch emergency vehicles, personnel and materials to ensure the efficient operation of the emergency communication system. The system can support the monitoring and prediction of major emergencies, on-site command and emergency repairs, self-rescue and calls for help by people in disaster areas, and the satisfaction of communication needs in disaster areas, thereby improving emergency response capabilities and ensuring the rapid recovery and protection of communication services in disaster areas;

[0003] To sum up, how to improve emergency response capabilities and ensure the rapid recovery and protection of communication services in disaster areas is an urgent problem to be solved and optimized based on the matching system of 5G networks, Beidou positioning and non-line-of-sight microwaves. Summary of the invention

[0004] The present invention provides a matching method and system based on 5G network, Beidou positioning and non-line-of-sight microwave to solve the technical problem of how to improve emergency response capabilities and ensure the rapid recovery and protection of communication services in disaster areas.

[0005] In order to solve the above technical problems, the present invention provides a matching method and system based on 5G network and Beidou positioning and non-line-of-sight microwave. The specific technical solutions are as follows:

[0006] First, a matching method based on 5G network, Beidou positioning and non-line-of-sight microwave includes the following steps:

[0007] S100, acquiring high-precision geographic location information data to obtain a first calibration data set; and enabling the antenna to adaptively and quickly focus and adjust direction according to the first calibration data set;

[0008] S200, based on preliminary adjustment of the antenna direction, so that the antenna collects signals and analyzes changes in signal strength to obtain a second calibration data set; dynamically adjusting the antenna direction to match the target signal under non-line-of-sight conditions according to the second calibration data set;

[0009] S300, combining the first calibration data set and the second calibration data set to obtain a calibration data set; training the calibration data set to construct a calibration matching model; the calibration matching model outputs an identification result indicating a calibration match between antenna position information and target signal position information;

[0010] S400. Based on the output result of the calibration matching model, use non-line-of-sight microwaves in the disaster environment to determine the precise orientation of accident victims by receiving target signals with the antenna.

[0011] As a further optimization scheme of the present invention, according to the first calibration data set, to enable the antenna to adaptively and quickly focus and adjust the direction, including:

[0012] Based on the first calibration data set, preliminarily adjust the orientation of the antenna to obtain the initial reference direction of the antenna θ0 = (θ ref , φ ref ); where θ ref represents the initial elevation angle of the antenna and φ ref represents the initial azimuth angle of the antenna; the initial reference direction of the antenna indicates that the starting direction where the antenna is located will be calibrated and oriented according to the coordinates of the target position;

[0013] Based on the antenna receiving the target signal at the initial reference direction, through r(t) = A(t)·S(t) + n(t), perform interference processing on the received target signal to obtain the true target signal; where r(t) represents the vector of the true signal received by the antenna; A(t) represents the gain matrix, representing the attenuation and gain of the signal; S(t) represents the true transmitted signal; n(t) represents the noise or interference signal;

[0014] Based on receiving the true target signal, through Search for the preliminary adjustment direction of the antenna to enable the antenna to adaptively adjust its received signal orientation according to the initial reference direction benchmark; when the antenna automatically adjusts and optimizes the receiving angle of the target signal, determine the target pointing of the antenna; where θ * (t) represents the target pointing after the preliminary adjustment of the antenna; argmax r(t) represents the true target signal strength; represents the adjustment function of the initial reference direction of the antenna;

[0015] Based on the antenna's preliminary focusing, focus on the signal source in the target area; through preliminary focusing, the antenna receives the target signal and feeds back and adjusts the direction accuracy in real time to make the receiving error between the antenna and the target signal relatively small.

[0016] As a further optimization scheme of the present invention, based on the preliminary adjustment of the antenna direction, enable the antenna to collect signals and analyze the change in signal strength to obtain the second calibration data set; according to the second calibration data set, dynamically adjust the initial direction of the antenna to match the target signal under non-line-of-sight conditions, including:

[0017] When the antenna in the initial reference direction receives the target signal in real time, mark the target signals received multiple times to obtain a signal reception mark point set; the signal reception mark point set includes the signal emission intensity, the signal intensity when the antenna receives the signal, and the antenna angle of the received signal.

[0018] Based on the signal reception mark point set, record and compare the information data of multiple signal reception mark points to obtain the signal intensity differences of multiple signal reception mark points; according to the signal intensity differences of the signal mark points, preliminarily adjust the initial reference direction of the antenna.

[0019] Based on the signal reception mark point set, for the initial signal intensity P = P0 + 10nlog 10 (d) + X α Further analyze to obtain a second calibration data set; where P represents the received initial signal intensity; P0 represents the reference distance; n represents the path loss exponent; d represents the actual distance from the antenna; X α represents lognormal shadow fading, which varies randomly due to changes in large-scale environmental attenuation.

[0020] As a further optimization scheme of the present invention, recording and comparing the information data of multiple signal reception mark points to obtain the signal intensity differences of multiple signal reception mark points includes:

[0021] Based on the signal reception mark point set, to obtain multiple triple data sets formed by the received mark points (P out , P recv , θ recv ), where P out represents the signal emission intensity; P recv represents the received signal intensity; θ recv represents the received signal angle;

[0022] Based on the obtained triple data sets; compare multiple triple data sets to form signal reception mark point information pairs; each pair consists of two triple data sets (P out，i , P recv，i , θ recv，i ) and (P out，j , P recv，j , θ recv，j ); where i and j respectively represent data items in different triple data sets;

[0023] Based on the signal reception mark point information pairs, through to obtain the logarithmic signal intensity difference of the signal reception mark points; where ΔP i，jIndicates the logarithmic difference in signal strength of the signal reception marker points; the logarithmic difference in signal strength of the signal reception marker points is the signal strength difference of the signal reception marker points;

[0024] As a further optimization scheme of the present invention, the initial signal strength is further analyzed to obtain a second calibration data set, including:

[0025] Obtain the initial true target signal strength at different positions and different time periods, and analyze the signal change situation by comparing the signal strength data at different times or positions;

[0026] According to the signal change situation, by Obtain the mean and standard deviation of the signal strength at different positions or different time periods to evaluate the signal stability and change range. In the formula, μ represents the mean of the signal strength; h represents the standard deviation of the signal strength; N represents the number of signal reception marker points; P i Represents the signal strength data collected in real time;

[0027] The smaller the standard deviation h or the higher the signal-to-noise ratio SNR, the more stable the signal; the change range ΔP range = P recv，max - P recv，min ; In the formula, ΔP range Represents the change range; P recv，max Represents the upper limit of the signal strength change; P recv，min Represents the lower limit of the intensity change;

[0028] According to the signal stability and change range, by To determine the relationship between the signal strength and the antenna direction; in the formula, Represents the correlation coefficient between the signal strength and the antenna direction; f(θ) represents the fitting function;

[0029] Through the correlation coefficient between the signal strength and the antenna direction, to obtain the relationship between the signal strength and the antenna direction. Under non-line-of-sight conditions, the signal strength usually changes with the change of the antenna angle; through the known relationship between the signal strength and the antenna direction, the antenna is dynamically adjusted twice to optimize the pointing of the received target signal.

[0030] As a further optimization scheme of the present invention, the calibration data set is trained to construct a calibration matching model; the calibration matching model outputs an identification result representing the calibration matching of the antenna position information and the target signal position information, including:

[0031] Generate structural data from the historical calibration data set, and encode the structural data into sequence data to train the calibration matching model;

[0032] Input the sequence data into the calibration matching model; the calibration matching model includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer, and an output layer. Transmit the intermediate representation data of multiple hidden layers to the output layer, and the output layer outputs an identification result indicating the calibration matching between the antenna position information and the target signal position information;

[0033] Input the newly obtained calibration data set into the calibration matching model to output a prediction result of the newly obtained output layer indicating the calibration matching between the antenna position information and the target signal position information.

[0034] As a further optimized solution of the present invention, based on the output result of the calibration matching model, utilize non-line-of-sight microwaves in the disaster environment to realize the judgment of the accurate azimuth of accident personnel by the antenna receiving the target signal, including:

[0035] Based on the prediction result of the calibration matching between the antenna position information and the target signal position information; and combine the dynamic adjustment of the antenna direction and the signal strength of the target signal to perform signal modeling through the non-line-of-sight microwave frequency to describe the signal band change under non-line-of-sight propagation in multipath propagation and obstacle reflection and diffraction;

[0036] Obtain the channel condition of the antenna in the complex environment by combining the signal band change with the signal strength, and then according to the terrain complexity and obstacle distribution; dynamically adjust the antenna modulation and coding signals according to the channel condition; and perform diversity on the signal receiving environment to obtain spatial diversity, time diversity, and frequency diversity;

[0037] Based on the obtained signal receiving spatial diversity, time diversity, and frequency diversity, obtain the signal band amplitudes of different diversity information; integrate the three diversity information band amplitudes of the corresponding nodes to obtain a band amplitude data integration set; according to the band amplitude data integration set, obtain the highest point band amplitude integration data; the highest point data of the band amplitude is the preliminary predicted target position of the accident personnel;

[0038] Based on the multi-source data of the antenna receiving signals and combining with the calibration matching model, to real-time update the accurate position of the predicted accident personnel.

[0039] As a further optimized solution of the present invention, based on the obtained signal receiving spatial diversity, time diversity, and frequency diversity, obtain the signal band amplitudes of different diversity information; integrate the three diversity information band amplitudes of the corresponding nodes to obtain a band amplitude data integration set, including:

[0040] The spatial diversity means that multiple antennas are arranged and adjusted to direct the received signals to receive the target signal; the time diversity means sampling the target signals at different time nodes; the frequency diversity means multiple frequency channels;

[0041] Based on the spatial diversity, the temporal diversity, and the frequency diversity, extract the band amplitudes of the signals for the diversity cases of the three signals; and perform alignment and normalization processing on the signal band amplitude data; to obtain unified standard band amplitude data;

[0042] Integrate the unified standard band amplitude data into a unified data set to enhance the signals in the unified data set; and optimize the communication in complex environments and track the target positions of accident personnel.

[0043] As a further optimization scheme of the present invention, based on the multi-source data of the antenna received signals and combined with the calibration matching model, to update the accurate position of the predicted accident personnel in real time, including:

[0044] By constructing a three-dimensional terrain map of the accident area and combining signal modeling to obtain the target position signal information of the accident personnel for preliminary prediction of the accident personnel; using the diversity of the channel conditions and the signal reception environment to verify the initial prediction of the spatial position of the signal information of the accident personnel; to judge the accurate orientation of the victims;

[0045] And through X k =X k-1 +K k ·(z k -H·X k-1 ) to update the positioning data of the target personnel in real time to improve the accuracy of the predicted target position; where X k represents the predicted position; z k represents the measured position; K k represents the filtering gain; H represents the observation matrix.

[0046] In a second aspect, the system is provided with an electronic device including a memory, a processor, and a matching method program based on 5G network and Beidou positioning and non-line-of-sight microwave stored on the memory and executable on the processor. When the matching method program based on 5G network and Beidou positioning and non-line-of-sight microwave is executed by the processor, it implements the steps of the matching method based on 5G network and Beidou positioning and non-line-of-sight microwave. The system includes:

[0047] Information acquisition module: It is used to obtain high-precision geographical location information data to obtain a first calibration data set; according to the first calibration data set, to enable the antenna to adaptively focus quickly and adjust the direction;

[0048] Detection and adjustment module: It is used to perform a preliminary adjustment on the antenna direction based on which the antenna collects signals and analyzes the signal strength change to obtain a second calibration data set; according to the second calibration data set, to dynamically adjust the antenna direction to match the target signal under non-line-of-sight conditions;

[0049] Calibration matching module: It is used to combine the first calibration data set and the second calibration data set to obtain a calibration data set; train and construct a calibration matching model with the calibration data set; the calibration matching model outputs an identification result indicating the calibration matching between the antenna position information and the target signal position information.

[0050] Positioning prediction module: It is used for the output result of the calibration matching model, and uses non-line-of-sight microwaves in the disaster environment to realize the accurate orientation judgment of accident personnel by the antenna receiving the target signal.

[0051] The present invention has at least the following beneficial effects: The present invention forms the first calibration data set by acquiring high-precision geographical location information data; provides an initial benchmark for the adaptive adjustment of the antenna to ensure the accuracy of subsequent steps; the high-precision position information can effectively reduce the misalignment of the antenna direction caused by the initial error and improve the calibration efficiency; then, according to the first calibration data set, the antenna is adaptively and quickly focused and directionally adjusted to quickly align the antenna direction; the antenna quickly captures the target direction, shortening the debugging time; the accurate focusing and direction adjustment can avoid multiple tests, improve the efficiency and reduce resource consumption; it lays a foundation for signal acquisition and analysis and avoids the decline of signal quality caused by direction error.

[0052] The antenna collects signals and analyzes the signal strength change to obtain the second calibration data set; based on the data of the real-time signal strength change, the adaptability of the antenna to the environment is optimized; it provides a basis for dynamic adjustment to ensure that the antenna can still maintain efficient communication in complex scenarios (such as non-line-of-sight conditions); enhances the ability to identify and adapt to environmental interference factors.

[0053] According to the second calibration data set, the antenna direction is dynamically adjusted to match the signal under non-line-of-sight conditions; the dynamic adjustment mechanism ensures that the antenna can automatically optimize the signal path in non-line-of-sight scenarios; improves the connection stability of the antenna, overcomes the influence of complex terrain or obstacles; ensures the reliability of the communication link and reduces signal loss or weak signal problems.

[0054] Combining the first calibration data set and the second calibration data set to form a calibration data set; combining static geographical location information with dynamic signal strength changes improves the integrity and accuracy of the data set; provides a more comprehensive calibration benchmark to support the training of the subsequent calibration matching model; reduces the model deviation caused by a single data source and enhances the adaptability of the model; based on the calibration data set, train and construct a calibration matching model; using multi-dimensional calibration data improves the accuracy of the calibration matching model; the model can adapt to different geographical environments and communication conditions, enhancing the prediction ability; provides an efficient antenna position prediction mechanism and shortens the positioning response time.

[0055] Calibrate and match the output antenna position information with the prediction results of position matching; achieve high-precision prediction of antenna positions, providing a basis for subsequent communication optimization; provide real-time position information support to ensure the accuracy of antenna adjustment; quickly respond in dynamic communication scenarios, improving the real-time performance and reliability of the overall system.

[0056] Through non-line-of-sight microwave communication technology, achieve precise connection of antennas and stability of communication links in complex terrains or situations blocked by obstacles; overcome the limitations of traditional line-of-sight communication, ensuring communication capabilities in complex scenarios; improve the adaptability and deployment scope of the system, suitable for special environments such as disaster relief; ensure the stability of communication links, providing continuous data support for subsequent precise positioning.

[0057] Combine the calibration and matching model to predict the precise positions of accident victims in real time; through the efficient prediction ability of the model, quickly determine the positions of accident victims in complex environments; improve rescue efficiency and reduce rescue time losses caused by information delays; provide stable and reliable personnel position data support, improving the accuracy and safety of rescue operations. Brief Description of the Drawings

[0058] Figure 1 is a schematic flowchart of the matching method based on 5G network, Beidou positioning and non-line-of-sight microwave provided by an embodiment of the present invention;

[0059] Figure 2 is a schematic diagram of the matching system based on 5G network, Beidou positioning and non-line-of-sight microwave provided by an embodiment of the present invention;

[0060] Figure 3 is a schematic diagram of the terminal architecture based on 5G network, Beidou positioning and non-line-of-sight microwave provided by an embodiment of the present invention. Detailed Embodiment

[0061] The following further describes the present application in detail with reference to the drawings. It is necessary to point out here that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the protection scope of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0062] The matching method and system based on 5G network, Beidou positioning and non-line-of-sight microwave provided in this embodiment are specifically implemented as follows:

[0063] As Figure 1 shown, the matching method based on 5G network, Beidou positioning and non-line-of-sight microwave includes the following steps:

[0064] S100. Obtain high-precision geographical location information data to obtain a first calibration data set; according to the first calibration data set, enable the antenna to adaptively focus quickly and adjust the direction.

[0065] S200. Based on the preliminary adjustment of the antenna direction, enable the antenna to collect signals and analyze the signal strength change to obtain a second calibration data set; according to the second calibration data set, dynamically adjust the antenna direction to match the target signal under non-line-of-sight conditions.

[0066] S300. Combine the first calibration data set and the second calibration data set to obtain a calibration data set; train and construct a calibration matching model with the calibration data set; the calibration matching model outputs an identification result indicating the calibration matching between the antenna position information and the target signal position information.

[0067] S400. Based on the output result of the calibration matching model, utilize non-line-of-sight microwaves in the disaster-stricken environment to realize the judgment of the accurate orientation of accident personnel by the antenna receiving the target signal.

[0068] In the embodiment, step S100 mainly collects high-precision geographical location information of the target area; obtains and combines geographical information through a satellite positioning system (such as GPS) or a geographical information system (GIS) and transmits the geographical information to the system through the 5G network for real-time update. This information helps to clarify the initial position of the antenna, the geographical distribution of the target area, and possible signal interference sources in the environment; through this step, the antenna can perform preliminary calibration according to its initial position and geographical information, providing basic data for subsequent steps; providing a basis for subsequent antenna direction adjustment and signal reception; enhancing antenna adaptability: using high-precision geographical information helps the antenna to focus and adjust the direction more accurately, avoiding blind adjustment.

[0069] After obtaining the first calibration data set through step S100 in step S200, the antenna will perform a preliminary direction adjustment according to this data set. Then, the antenna starts to collect the target signal and monitor the signal strength change in real time; the change in this signal strength reflects the relative position between the antenna and the target and the signal quality. Through the change in the signal strength data, the system can further infer the position of the target and generate a second calibration data set; by monitoring the change in the signal strength in real time, the antenna can automatically adjust according to the environmental change to ensure the best signal reception; improving the signal reception quality: accurate signal strength analysis can help the antenna quickly adapt to the non-line-of-sight environment and reduce interference.

[0070] S300 combines the geographical location information (the first calibration dataset) in S100 with the signal strength data (the second calibration dataset) in S200 to generate a complete calibration dataset; by inputting these datasets into a machine learning algorithm, a calibration matching model is constructed. This model can identify the matching relationship between the position information of the antenna and the position information of the target signal; by combining geographical information and signal strength data, environmental factors can be comprehensively considered for accurate positioning and matching; the machine learning model can automatically extract effective features from the data to improve the calibration accuracy; after the model is trained, it can be dynamically adjusted to automatically optimize the antenna calibration process according to environmental changes.

[0071] Finally, based on the constructed calibration matching model, step S400 will receive signals through non-line-of-sight microwave technology (such as signals in the UHF / VHF band). This technology can work effectively in disaster-affected environments (such as in buildings, obstacles, or other complex terrains); the output result of the calibration matching model can accurately determine the relationship between the antenna position and the target signal, thereby inferring the precise position of the target, such as the specific location of accident victims; it can achieve precise positioning of the target signal in complex and obstacle environments, such as accident victims or distress signals in the disaster area; microwave technology can effectively penetrate obstacles and maintain high efficiency in disaster-affected or harsh environments; the system can output the target position information in real time, greatly improving the rescue efficiency.

[0072] The above steps cooperate with each other. Through multi-stage calibration and signal acquisition steps, the antenna can more accurately locate the target in a non-line-of-sight environment, especially suitable for disaster rescue scenarios. The working principle of each step supports the improvement of the antenna's adaptive ability, signal reception quality, and positioning accuracy, and through dataset training and optimization of the calibration model, it ensures the effectiveness of the system in complex environments. Finally, it realizes the accurate judgment of the orientation of accident victims under non-line-of-sight conditions, improving the efficiency and accuracy of disaster rescue.

[0073] In a preferred embodiment of the present invention, step S100 further includes the following steps:

[0074] S101, based on the first calibration dataset, preliminarily adjust the orientation of the antenna to obtain the initial reference direction of the antenna θ0 = (θ ref , φ ref ); in the formula, θ ref represents the initial elevation angle of the antenna and φ ref represents the initial azimuth angle of the antenna; the initial reference direction of the antenna indicates that the starting direction where the antenna is located will be calibrated and oriented according to the coordinates of the target position;

[0075] S102, Receive the target signal based on the initial reference direction of the antenna. Through r(t) = A(t)·S(t) + n(t), the received target signal is subjected to interference processing to obtain the true target signal. In the formula, r(t) represents the vector of the true signal received by the antenna; A(t) represents the gain matrix, representing the attenuation and gain of the signal; S(t) represents the true transmitted signal; n(t) represents the noise or interference signal.

[0076] S103, Based on receiving the true target signal, through Search for the preliminary adjustment direction of the antenna to enable the antenna to adaptively adjust the azimuth of its received signal according to the initial reference direction benchmark. When the antenna automatically adjusts and optimizes the reception angle of the target signal, the target pointing of the antenna is determined. In the formula, θ * (t) represents the target pointing after the preliminary adjustment of the antenna; argmax r (t) represents the true target signal strength. Represents the adjustment function of the initial reference direction of the antenna.

[0077] S104, Based on the antenna performing preliminary focusing, focus on the signal source in the target area. Through preliminary focusing, the antenna receives the target signal and real-time feedback adjusts the direction accuracy to make the reception error between the antenna and the target signal relatively small.

[0078] In the embodiment, step S101 uses the first calibration data set (usually data from a high-precision positioning system) obtained in step S100 to calculate the initial direction of the antenna. This includes calculating the initial elevation angle (θ ref ) and azimuth angle (φ ref ) of the antenna, that is, the starting angle of the antenna relative to the target position; by using this data, the initial reference direction of the antenna is determined and calibrated to a starting direction (θ ref , φ ref ). This reference direction ensures that the adjustment of the antenna starts from a known starting point; thus, it can provide an accurate direction basis for subsequent signal reception and analysis; by accurately calibrating the initial azimuth, the problem of inaccurate initial reception caused by the antenna position error is reduced; the initial orientation of the antenna is determined according to the coordinates of the target, enabling the antenna to better receive the target signal.

[0079] Step S102 enables the antenna to receive the target signal according to the initial reference direction determined in S101. The received signal will contain various interferences (such as noise or other non-target signals), so interference processing is required to extract the true target signal.

[0080] In the mathematical model, r(t) represents the signal vector received by the antenna, which includes the target signal and the interference signal; A(t) represents the gain matrix, which is used to describe the attenuation and gain of the signal; S(t) is the true signal of the target; n(t) is the noise or interference signal. By filtering and denoising the signal, the true target signal can be separated from the interference signal; the interference signal can be effectively filtered to ensure that the received signal is as close as possible to the true target signal; by adjusting the gain and suppressing the interference, the quality of the received signal can be improved, thereby improving the accuracy of subsequent processing; the influence of environmental noise or other external interferences can be reduced to ensure signal stability.

[0081] After obtaining the true target signal through step S102 in step S103, the antenna is further fine-tuned according to the initial reference direction to ensure that the antenna can best align with the target signal source. This fine-tuning process is an automatic adjustment process, and the antenna automatically optimizes the direction according to the change in the received signal strength to maximize the received signal strength as much as possible; by adjusting the direction of the antenna, the target pointing of the antenna (i.e., the pointing angle of the antenna) is precisely optimized. Mathematically, the target pointing after the antenna adjustment can be expressed as a function and is fine-tuned with the change in the signal strength. The goal of the adjustment function is to align the antenna with the signal source to minimize the reception error; through automatic adjustment, the antenna can automatically optimize the reception angle according to the real-time received signal strength to ensure precise alignment with the target; the optimization of the signal strength enables the antenna to minimize the deviation and accurately receive the target signal; the antenna can be dynamically adjusted according to different environmental conditions (such as obstacles, interference, etc.) without manual intervention.

[0082] In step S104, the antenna needs to focus on the signal source in the target area through preliminary focusing. The antenna starts fine-tuning to reduce the error when receiving the signal; after receiving the target signal, the antenna continues to adjust the direction and provides real-time feedback on the direction accuracy. Through this fine-tuning, the antenna can focus on the strongest target signal source to ensure the minimization of the reception error. The feedback mechanism ensures that the antenna can be adjusted in real time to continuously reduce the reception error; through fine-tuning, the antenna can more precisely focus on the target signal source and reduce any possible reception error; the system can monitor and optimize the antenna direction in real time to ensure that the quality of the received signal reaches the best state; as the adjustment process progresses, the reception error gradually decreases, improving the positioning accuracy and ensuring the reliability of signal acquisition.

[0083] These steps together form a complete process from initial positioning to precisely receiving the target signal. Each step involves fine-tuning of the antenna direction and the quality of the received signal, ensuring that the antenna can adapt to environmental changes and ultimately achieving high-precision reception of the target signal; step S101 ensures the accuracy of the initial reference direction of the antenna, avoiding errors in the initial reception; step S102 provides effective signal processing and anti-interference techniques to improve the quality of the received target signal; step S103 automatically adjusts the antenna direction so that the antenna can be optimally aligned with the target signal source; step S104 further reduces the reception error through fine focusing to ensure the best signal reception effect.

[0084] In a preferred embodiment of the present invention, step S200 further includes the following steps:

[0085] S201, when the antenna in the initial reference direction receives the target signal in real time, mark the target signals received multiple times to obtain a signal reception mark point set; the signal reception mark point set includes the signal emission intensity, the signal intensity when received by the antenna, and the antenna angle of the received signal;

[0086] S202, based on the signal reception mark point set, record and compare the information data of multiple signal reception mark points to obtain the signal intensity difference of multiple signal reception mark points; according to the signal intensity difference of the signal mark points, preliminarily adjust the initial reference direction of the antenna;

[0087] S203, based on the signal reception mark point set, further analyze the initial signal intensity P = P0 + 10nlog 10 (d)+X α to obtain a second calibration data set; where P represents the initial signal intensity received; P0 represents the reference distance; n represents the path loss exponent; d represents the actual distance from the antenna; X α represents log-normal shadow fading, which varies randomly due to changes in large-scale environmental attenuation.

[0088] In the embodiment, in step S201, the antenna is located in an initial reference direction and starts to receive the target signal in real time. Each received target signal is marked, and a mark point set is recorded. This mark point set contains the following key information:

[0089] The signal emission intensity (for example, the power output of the transmitting device);

[0090] The signal intensity received by the antenna (the intensity of the signal after path propagation and attenuation);

[0091] The antenna angle when receiving the signal (here refers to the directivity parameter of the antenna);

[0092] Through such a signal marking process, the propagation characteristics of signals under different conditions can be recorded.

[0093] Provide multi-dimensional signal information (intensity, angle, etc.), providing basic data for subsequent calibration and adjustment; ensure the time serialization and spatial marking of signals, making it convenient for comparison and analysis; provide an objective signal basis for optimizing the initial direction of the antenna.

[0094] In step S202, based on the signal reception marking point set obtained in S201, the recorded multiple signal reception data are compared. The core is to calculate the signal intensity difference between each signal reception marking point; by comparing the received signal intensities in the same direction or different directions, the change patterns in signal propagation are identified; based on the characteristics of the intensity difference, the signal propagation path, the impact of obstacles in the environment on the signal, and the deviation of the initial antenna reference direction can be inferred; finally, using the signal intensity difference information, the initial reference direction of the antenna is preliminarily adjusted to approach a more ideal direction.

[0095] Improve the accuracy of the antenna direction: Through preliminary adjustment, the antenna is closer to the direction with the strongest signal, improving the signal reception quality; when there is a significant deviation in the signal intensity difference, it may indicate an abnormal area with signal occlusion or attenuation in the path; preliminary adjustment can reduce the complexity of subsequent analysis and optimization and accelerate the calibration convergence speed.

[0096] In step S203, by further analyzing the initial signal intensity, quantitative analysis of the main parameters in the signal propagation process can be carried out. The signal intensity is obtained from the marking point set, and curve fitting analysis is performed on the received signal intensity to obtain a second calibration data set for more accurate optimization of the antenna direction and signal quality; by introducing a path loss model and random attenuation factors, the calibration of the antenna direction takes into account a more complex propagation environment; for example, the characteristics of the path loss exponent and shadow attenuation can reflect the characteristics of the propagation environment and help optimize the communication quality; through the second calibration, errors caused by factors such as the environment and antenna position can be further eliminated to ensure optimal signal reception.

[0097] The above three steps cooperate with each other. S201 provides the basic data for target signal position positioning for the whole process, recording the signal propagation information through a multi-dimensional marking point set; S202 conducts preliminary analysis and adjustment, optimizing the initial direction based on the intensity difference and reducing the complexity of subsequent analysis; S203 performs precise calibration, further analyzing the signal characteristics, accurately calculating the calibration data, and making the signal reception quality reach the optimal; from rough adjustment to fine adjustment, errors are reduced through step-by-step analysis; by combining the signal intensity difference with the path loss model, it can better adapt to the dynamically changing propagation environment; the optimized antenna direction makes the signal reception stronger and more stable, thereby improving the communication quality and reliability.

[0098] In a preferred embodiment of the present invention, step S202 further includes the following steps:

[0099] S2021, based on the signal reception marker point set, to form multiple triple data sets (P out , P recv , θ recv ) with the received marker points, where P out represents the signal emission intensity; P recv represents the received signal intensity; θ recv represents the received signal angle;

[0100] S2022, based on the obtained triple data sets; comparing multiple said triple data sets to form signal reception marker point information pairs; each pair consists of two triple data sets (P out，i , P recv，i , θ recv，i ) and (P out，j , P recv，j , θ recv，j ); where i and j respectively represent data items in different triple data sets;

[0101] S2023, based on the signal reception marker point information pairs, by to obtain the signal intensity logarithmic difference of the signal reception marker points; where ΔP i，j represents the signal intensity logarithmic difference of the signal reception marker points; the signal intensity logarithmic difference of the signal reception marker points is the signal intensity difference of the signal reception marker points.

[0102] In the embodiment, in step S2021, based on the signal reception marker point set obtained in step S201 (signal data received from the antenna), the following three key information is extracted from each marker point and combined into a triple data set: the emission intensity of the signal (the signal intensity at the transmitting end), the received signal intensity (the received signal intensity after attenuation through the propagation path), and the antenna direction angle when receiving the signal (the azimuth angle of the received signal).

[0103] Each received marker point will form such a triple, and after all received marker points are processed, multiple triple data sets are formed; by converting each received marker point into a standardized triple form, it is possible to simplify subsequent data analysis, comparison, and processing; this triple contains important information such as signal intensity and reception angle, making the description of signal propagation more comprehensive and helping to accurately analyze signal characteristics; the structured form of the triple data is convenient for comparison, calculation, and optimization in subsequent steps, providing an accurate basis for signal analysis.

[0104] In step S2022, based on the multiple triple data sets formed in step S2021, a comparison operation is performed. Each pair of data sets consists of two different triples, forming a signal reception marker point information pair. Specifically, for any two triple data sets (i and j) in the set, by comparing their corresponding values, an information pair is formed; this comparison is mainly to discover the relationship or difference between the two triples. In this way, the propagation characteristics of the signal can be understood more deeply, especially the interaction between different reception angles and reception intensities; by comparing multiple triples, the differences in the signal at different reception angles or different transmission powers can be identified, helping to determine the regularity of signal propagation; generating data pairs: each pair of signal reception marker point information pairs can be used as the basis for further signal analysis, such as calculating the logarithmic difference of signal strength, etc.; by comparing different triples, the signal propagation situation under different conditions can be better understood, such as the relationship between reception intensity and transmission intensity, and the influence of reception angle on signal strength.

[0105] In step S2023, based on the already formed signal reception marker point information pairs, the logarithmic difference of signal strength is further calculated. The core of this step is to quantify the change in signal reception intensity by calculating the logarithmic difference; ΔP i，j represents the logarithmic difference in signal strength between these two marker points, reflecting the relative strength difference between them; this logarithmic difference can help us analyze the degree of signal attenuation and the influence of environmental factors on signal propagation; the logarithmic difference method can better handle the comparison of signal strength differences because signal strength usually spans a large dynamic range, and after digital processing, it can simplify the analysis and improve the calculation accuracy; the logarithmic difference has a certain debiasing property, which can more effectively remove the non-linear influence caused by environmental changes in the data, thus making the signal strength analysis more accurate; by calculating the logarithmic difference of signal strength, the attenuation characteristics of the signal at different positions or under different conditions can be more intuitively understood. This is very useful for tasks such as optimizing antenna adjustment and predicting signal coverage; the logarithmic difference in signal strength is a commonly used parameter in signal propagation models (such as the free space path loss model), and further analysis of these differences can help optimize antenna layout, transmission power, and the communication quality of the device.

[0106] The above steps cooperate with each other. Through the comparison of triple data and the calculation of logarithmic differences, the propagation characteristics of the signal can be analyzed more meticulously, and the key factors affecting signal quality, such as propagation loss, interference, etc., can be identified; through the analysis, a theoretical basis is provided for antenna adjustment and signal optimization, enabling the signal strength difference to be accurately quantified, thereby optimizing the performance of the communication system; by deeply analyzing the logarithmic difference in signal strength, the attenuation phenomena (such as multipath propagation, shadow attenuation, etc.) in a complex propagation environment can be better dealt with, thereby improving the robustness and stability of the system.

[0107] In a preferred embodiment of the present invention, step S203 further includes the following steps:

[0108] S2031, obtain the initial true target signal strength at different positions and different time periods, and analyze the signal change situation by comparing the signal strength data at different times or positions;

[0109] S2032, according to the signal change situation, by obtain the mean value and standard deviation of the signal strength at different positions or different time periods to evaluate the signal stability and change range. In the formula, μ represents the mean value of the signal strength; h represents the standard deviation of the signal strength; N represents the number of signal reception marking points; P i represents the signal strength data collected in real time;

[0110] S2033, the smaller the standard deviation h or the higher the signal-to-noise ratio SNR, the more stable the signal; the change range ΔP range = P recv，max - P recv，min ; in the formula, ΔP range represents the change range; P recv，max represents the upper limit of the signal strength change; P recv，min represents the lower limit of the intensity change;

[0111] S2034, according to the signal stability and change range, by to determine the relationship between the signal strength and the antenna direction; in the formula, represents the correlation coefficient between the signal strength and the antenna direction; f(θ) represents the fitting function;

[0112] S2035, through the correlation coefficient between the signal strength and the antenna direction, to obtain the relationship between the signal strength and the antenna direction. Under non-line-of-sight conditions, the signal strength usually changes with the change of the antenna angle; through the known relationship between the signal strength and the antenna direction, the antenna dynamically adjusts the antenna direction for the second time to optimize the pointing of the received target signal.

[0113] In the embodiment, step S2031 collects the signal strength in real time at different positions and different time periods, and records the intensity data of the target signal. The purpose of doing this is to obtain the preliminary change situation of the signal under various environmental conditions; by covering different spatial and time ranges, representative signal strength data can be obtained. These data provide a basis for subsequent analysis of signal changes, stability evaluation, and antenna adjustment.

[0114] Step S2032 obtains the mean value (μ) and standard deviation (h) of the signal strength at different time periods or different positions based on the signal strength data obtained in step S2031. The mean value reflects the central tendency of the signal strength, while the standard deviation represents the volatility of the signal strength; by calculating the mean value and the standard deviation, the stability of the signal can be quantified. If the standard deviation is small, it indicates that the signal strength is relatively stable; if the standard deviation is large, it indicates that the signal strength fluctuates greatly. This information can help to judge the quality of the signal.

[0115] Step S2033 calculates the change range of the signal strength by comparing the upper and lower limits of the signal strength change. The change range reflects the fluctuation amplitude of the signal at different times and positions; thus, analyzing the change range and stability of the signal helps to understand the quality of the signal. If the change range is too large, it may indicate the existence of interference or unstable signal sources, and further adjusting the antenna direction or optimizing the receiving system can effectively reduce these problems.

[0116] Step S2034 further studies the correlation between the signal strength and the antenna direction based on the stability and change range of the signal. By analyzing historical data, a function model between the signal strength and the antenna direction is fitted; the purpose of this step is to identify the relationship between the signal strength and the antenna direction and find the optimal antenna pointing angle. Through precise correlation analysis, the quality of signal reception can be significantly improved, interference can be reduced, and the system performance can be enhanced.

[0117] Step S2035, according to the established relationship model between the signal strength and the antenna direction, in practical applications, adjusts the direction (or angle) of the antenna to maximize the reception of the signal with the strongest strength. This is usually accomplished by dynamically adjusting the antenna direction twice; by optimizing the antenna pointing, the signal reception quality can be effectively improved under non-line-of-sight conditions. Especially in complex environments (such as with obstacles, interference sources, etc.), by intelligently adjusting the antenna direction, the stability and strength of the signal can be greatly enhanced, thereby improving the reliability and performance of the communication system.

[0118] By collaborating with each other in the above-mentioned steps, collecting signal data at different positions and different time periods, and calculating the mean and standard deviation, the stability and variation range of the signal can be comprehensively analyzed. This provides a quantitative basis for judging the signal quality; by analyzing the relationship between the signal strength and the antenna direction, an accurate model can be established to help optimize the antenna pointing, reduce signal loss and interference; by utilizing the relationship between the signal strength and the antenna direction, the direction of the antenna can be dynamically adjusted to achieve the best signal reception. This can effectively improve the communication quality under non-line-of-sight conditions, especially applicable to scenarios with high requirements for signal quality such as wireless communication and satellite communication; thereby improving the stability and quality of the signal, optimizing the pointing angle of the antenna, reducing signal loss caused by interference or environmental factors, and enhancing the efficiency and reliability of the wireless communication system, especially under complex environments or dynamic conditions.

[0119] In a preferred embodiment of the present invention, step S300 further includes the following steps:

[0120] S301, generating structural data from the historical calibration data set, encoding the structural data into sequence data, and training to obtain the calibration matching model;

[0121] S302, inputting the sequence data into the calibration matching model; the calibration matching model includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer, and an output layer, transmitting the intermediate representation data of multiple hidden layers to the output layer, and the output layer outputs an identification result indicating the calibration matching between the antenna position information and the target signal position information;

[0122] S303, inputting the newly obtained calibration data set into the calibration matching model to output a prediction result indicating the calibration matching between the antenna position information and the target signal position information output by the output layer.

[0123] In the embodiment, step S301 processes the historical calibration data set to form structured data. The calibration data usually includes information such as the antenna position, the target signal position, and the signal strength; encoding the structured data into a sequence form for input into the deep learning model. The sequence data is a format with sequential characteristics in time or space, facilitating the capture of dynamic relationships between data.

[0124] Example: Encode the data into a time series of [antenna position, signal position, signal strength], and use a deep learning framework (such as a multi-layer neural network) to train the sequence data. The model receives data through the input layer, gradually extracts features through multiple hidden layers, and finally generates a calibrated and matched recognition result at the output layer; encode the calibration data into sequence data to facilitate the model to extract relevant features of the time series or spatial series; by training the calibration matching model, the implicit relationship between the antenna position and the target signal position can be learned from historical data, laying a foundation for subsequent calibration matching.

[0125] Step S302 inputs the sequence data generated in step S301 into the calibration matching model, and the model structure is as follows:

[0126] Input layer: Receive sequence data, such as [antenna position, signal position, signal strength].

[0127] Hidden layer:

[0128] The first hidden layer: Initially extract the underlying features of the input data.

[0129] The second hidden layer: Further extract complex non-linear features.

[0130] The third hidden layer: Integrate the features of the previous two layers and generate an intermediate representation.

[0131] Output layer: Based on the intermediate representation data of the hidden layer, output the result of calibration matching.

[0132] Intermediate representation transmission: The data transmitted between the hidden layers gradually extracts the matching relationship features between the antenna position and the signal position.

[0133] Output layer result: The output layer generates a recognition result, indicating the calibration matching degree between the antenna position information and the target signal position information.

[0134] Through hierarchical processing of the data by multiple hidden layers, multi-level information in the data is extracted; the calibration matching result generated by the output layer can reflect the accurate matching relationship between the antenna and the signal position; the multi-layer structure of the deep learning model can effectively capture non-linear relationships and improve the accuracy of calibration.

[0135] Step S303 inputs the newly obtained calibration data set into the already trained calibration matching model; through the transmission and calculation of the input layer, hidden layer, and output layer, the model makes predictions on the new data according to the feature relationships learned during the previous training process; the output layer generates a prediction result, indicating the calibration matching degree between the new antenna position information and the target signal position information.

[0136] The trained model can quickly process new data without retraining and directly generate calibration matching results. It can update the matching results in real time according to new calibration data, improving the dynamic adaptability of the system. The model trained based on historical data can handle complex calibration matching tasks and provide high calibration accuracy even in a dynamic environment.

[0137] In a preferred embodiment of the present invention, step S400 further includes the following steps:

[0138] S401, based on the prediction results of the calibration matching between the antenna position information and the target signal position information; and combining the dynamic adjustment of the antenna direction and the signal strength of the target signal, signal modeling is performed through non-line-of-sight microwave frequencies to describe the signal band changes under non-line-of-sight propagation in multipath propagation and obstacle reflection and diffraction;

[0139] S402, combining the signal band changes with the signal strength, and then according to the terrain complexity and obstacle distribution, to obtain the channel conditions of the antenna in a complex environment; dynamically adjusting the antenna modulation and coding signals according to the channel conditions; and diversifying the signal reception environment to obtain spatial diversity, temporal diversity and frequency diversity;

[0140] S403, based on the obtained signal reception spatial diversity, temporal diversity and frequency diversity, to obtain the signal band amplitudes of different diversity information; integrating the three diversity information band amplitudes of the corresponding nodes to obtain a band amplitude data integration set; according to the band amplitude data integration set, obtaining the highest point band amplitude integration data; the highest point data of the band amplitude is the target position of the preliminary predicted accident personnel;

[0141] S404, based on the multi-source data of the antenna received signals and combining with the calibration matching model, to update the accurate position of the predicted accident personnel in real time.

[0142] In the embodiment, step S401 uses the known position of the antenna and the adjusted optimal pointing to receive the target signal, and then uses the signal emission intensity, arrival intensity and signal arrival time information to perform calibration matching with the position information of the target signal; by dynamically adjusting the direction of the antenna to maximize the signal strength and optimize the received target signal to determine the specific azimuth of the target signal.

[0143] Non-line-of-sight (NLOS) signal modeling: In non-line-of-sight propagation, the signal is affected by multipath effects, such as reflection, diffraction and scattering of obstacles; the modeling process combines the change characteristics of the signal band with multipath propagation and the obstacle environment to describe the band changes in signal propagation; it improves the perception ability of the positioning system for target signals in non-line-of-sight environments; in a complex environment of multipath propagation and obstacles, it improves the recognition accuracy and anti-interference ability of the antenna for target signals.

[0144] In step S402, the impact of the propagation path on signal characteristics is evaluated based on the signal band change and signal strength data received by the antenna. Using terrain data and obstacle distribution information, channel conditions including path loss, scattering gain, and occlusion effect are calculated. According to the channel conditions, the modulation method (such as QAM or PSK) and encoded signal of the antenna are optimized to ensure the reliability and efficiency of signal transmission. Through spatial diversity (multi-antenna reception), time diversity (multiple measurements), and frequency diversity (multi-band reception), multi-dimensional characteristics of signal propagation are obtained. The modulation and encoding methods are dynamically optimized to make signal transmission more efficient in complex environments, enhancing the anti-interference and robustness of the system. Spatial, time, and frequency diversity techniques improve the signal reception quality and reduce signal loss caused by single-path fading.

[0145] In step S403, the signal band amplitudes in spatial diversity, time diversity, and frequency diversity are measured respectively to obtain the amplitude data for each diversity. The diversity information of different nodes is integrated to form a complete set of band amplitude data. In the integrated data, the highest point of the band amplitude is extracted as the preliminary positioning point of the target signal. Through the fusion of multi-diversity information, the influence of environmental noise and interference on single-diversity information is effectively eliminated, significantly improving the positioning accuracy. The method of the highest point of the band amplitude is simple and direct, which can quickly provide a preliminary position prediction of the target and lay a foundation for subsequent precise positioning.

[0146] In step S404, based on the multi-source data of the signal received by the antenna (the known position of the antenna and the adjusted optimal pointing to receive the target signal, and then using information such as signal emission intensity, arrival intensity, and signal arrival time), the dynamic characteristics of the target signal are analyzed in real time. Using the calibration matching model established in S401, the target position prediction is adjusted in real time to correct the deviation caused by non-line-of-sight propagation or multi-path effect. Through the real-time updated prediction algorithm, the target position coordinates are dynamically adjusted to provide a more accurate positioning result. Combining multi-source data and real-time model update can quickly respond to environmental changes, making the positioning result more real-time and reliable. The error caused by signal path change or noise is reduced, making the predicted target position more accurate, especially suitable for personnel rescue in complex scenarios. The positioning accuracy and efficiency of the target in non-line-of-sight complex environments are improved. By dynamically adjusting the modulation and encoding method of the antenna, the adaptability of the system and the stability of signal transmission are enhanced. Using signal diversity technology and real-time model update, it can quickly respond to dynamic environmental changes and ensure high-precision positioning.

[0147] The above steps are designed based on steps S100, S200, S300 and their sub-steps to cooperate with each other for scenarios such as disaster rescue and complex terrain positioning, which helps to find the location of accident victims faster and more accurately, thereby improving the rescue efficiency and success rate; and can quickly respond to environmental changes, making the positioning results more real-time and reliable; reducing errors caused by signal path changes or noise, and making the predicted target location more accurate.

[0148] In a preferred embodiment of the present invention, step S403 further includes the following steps:

[0149] S4031, the space diversity means that multiple antennas are arranged and adjusted to direct the received signal towards the received target signal; the time diversity means sampling the target signal at different time nodes; the frequency diversity means multiple frequency channels;

[0150] S4032, based on the space diversity, the time diversity and the frequency diversity, extract the band amplitude of the signal for the diversity of the three signals; and perform alignment and normalization processing on the signal band amplitude data; to obtain unified standard band amplitude data;

[0151] S4033, integrate the unified standard band amplitude data into a unified data set to enhance the signal of the unified data set; and optimize the communication in complex environments and track the target location of accident victims.

[0152] In the embodiment, step S4031 arranges multiple antenna arrays through space diversity, adjusts the receiving direction of each antenna to cover different spatial ranges; using the characteristic that the signals received by multiple antennas are independent of each other, the influence of channel fading can be effectively reduced; the received signals of multiple antennas are optimized for the spatial distribution characteristics of the target signal through beamforming technology.

[0153] Through time diversity, sample the target signal at different times and record the changes in signal intensity and amplitude; through multiple samplings, the influence of multipath fading can be balanced and the dynamic channel characteristics can be captured.

[0154] Through frequency diversity, receive the target signal on multiple frequency channels and analyze the propagation characteristics at different frequencies; different frequency channels have different responses to obstacles, reflection and diffraction, and the frequency diversity can be used to enhance the stability of the signal; the space diversity improves the spatial coverage of the signal and reduces the loss of the received signal caused by occlusion or scattering; the time diversity smooths the influence of dynamic environmental changes on the signal through signal sampling at different times; the frequency diversity utilizes the characteristics of multi-band channels to enhance the anti-interference ability and propagation stability of the signal.

[0155] In step S4032, the band amplitudes of the signals are extracted from the signals of spatial diversity, temporal diversity, and frequency diversity respectively. The band amplitude is the energy distribution of the signal intensity over a specific frequency or spatial range, which is used to reflect the propagation characteristics of the signal. Since the data from spatial, temporal, and frequency diversity may have different time bases or spatial distribution characteristics, it is necessary to align the signal band amplitude data. During the alignment process, algorithms such as interpolation and time synchronization are used to ensure that the data is unified under the same spatio-temporal reference. The extracted signal band amplitudes are normalized so that their amplitude ranges are limited to a standard range (e.g., [0,1]). Normalization can eliminate the amplitude range differences of different signal diversities, facilitating subsequent data fusion. Data alignment makes the signal amplitudes extracted by different diversity methods consistent, avoiding errors caused by spatio-temporal asynchronization. The normalization process eliminates the scale differences between data amplitudes, ensuring the reliability and stability of subsequent data fusion. The extracted and standardized band amplitude data provides more accurate signal characteristics for locating the target.

[0156] In step S4033, the normalized band amplitude data of spatial diversity, temporal diversity, and frequency diversity are integrated to form a unified data set. During the integration process, weighted average or feature fusion methods are used to adjust the weights of different diversity signals according to the importance of the diversity methods. Through data integration, the complementary characteristics of the diversity signals are utilized to enhance the overall amplitude intensity of the target signal. After data integration, the noise of the signal is further suppressed, while the characteristics of the target signal are significantly amplified. For signal propagation problems in complex environments (such as multipath fading, obstacle occlusion, etc.), the integrated data can optimize the signal propagation characteristics through modeling analysis. While optimizing, the target position of the accident personnel is further tracked, and the position information is dynamically updated.

[0157] Thus, through data integration, by utilizing the diversity and complementarity of the diversity signals, the overall quality and stability of the signal are improved; signal enhancement effectively amplifies the amplitude characteristics of the target signal while reducing the influence of background noise; complex environment optimization enhances the anti-interference ability of the system in non-line-of-sight environments, making the target position tracking more accurate and real-time.

[0158] In a preferred embodiment of the present invention, step S404 further includes the following steps:

[0159] S4041, by constructing a three-dimensional terrain map of the accident area and combining signal modeling, obtain the target position signal information of the accident personnel to preliminarily predict the accident personnel; use the diversity of channel conditions and signal reception environments to verify the initial prediction of the spatial position of the signal information of the accident personnel; to judge the accurate orientation of the victims.

[0160] S4042, and through X k = X k-1 + Kk ·(z k -H·X k-1 ) updates the positioning data of the target person in real time to improve the accuracy of predicting the target position; where X k represents the predicted position; z k represents the measured position; K k represents the filtering gain; H represents the observation matrix.

[0161] In the embodiment, step S4041 uses remote sensing data (such as LiDAR, SAR images) or on-site data acquisition technology to scan the accident area to generate a three-dimensional terrain model; the three-dimensional terrain map accurately depicts the terrain characteristics of the accident area (such as undulations, obstacle distribution, etc.), providing a physical scene basis for signal modeling; according to the three-dimensional terrain map, combined with the radio wave propagation model (such as multipath fading, diffraction, and reflection effects), the propagation characteristics of signals in a complex environment are modeled; during the modeling process, the signals sent or reflected by the accident personnel (such as RFID signals or radio wave signals) are used to estimate the possible range of their target positions; according to the channel conditions (such as channel gain, signal-to-noise ratio) and the signal reception environment (such as the layout of receiving antennas, occlusion environment), the initially predicted target positions are verified; the verification process is based on multi-diversity techniques (such as space, time, frequency diversity), and by matching the predicted positions with the characteristics of the actual received signals, the position prediction results with large errors are excluded; the three-dimensional terrain map provides a real physical scene for signal modeling, making the signal propagation analysis closer to reality; signal modeling combined with terrain information can effectively compensate for the impact of complex terrain on signal propagation, thereby improving the accuracy of target position prediction; channel condition verification further reduces the prediction errors caused by channel noise or environmental interference, making the initial position estimation more reliable; this step can provide a high-precision initial position estimate for subsequent position tracking, laying the foundation for precise positioning.

[0162] S4042 updates the position data in real time based on the movement characteristics of accident victims and the dynamic changes of received signals. By calculating the measurement error covariance and the prediction error covariance, it dynamically adjusts the filtering gain to make the position update closer to the true position. When the channel quality is high, it trusts the measurement data more; when the channel conditions are poor, it trusts the prediction model more. By combining real-time signal data and the prediction model, it continuously corrects the position information of the target person and gradually converges to the true position. Thus, it effectively integrates the long-term trend of signal modeling and the short-term dynamic changes of real-time measurement. By correcting the predicted position in real time, it keeps the target position estimation dynamically accurate. It adjusts the weight distribution of prediction and measurement according to the channel conditions to ensure better positioning accuracy in different environments. By comprehensively using long-term trend prediction and short-term dynamic measurement, it avoids the bias caused by a single data source. The real-time updated positioning result makes the position tracking of accident victims more accurate and provides reliable position information support for rescue operations.

[0163] As Figure 2 shown, a matching system based on 5G network, Beidou positioning and non-line-of-sight microwave includes:

[0164] Information acquisition module: It is used to obtain high-precision geographical location information data to obtain a first calibration data set; according to the first calibration data set, it enables the antenna to adaptively focus quickly and adjust the direction.

[0165] Detection and adjustment module: It is used to preliminarily adjust the antenna direction based on it, so that the antenna collects signals and analyzes the signal strength change to obtain a second calibration data set; according to the second calibration data set, it dynamically adjusts the antenna direction to match the target signal under non-line-of-sight conditions.

[0166] Calibration and matching module: It is used to combine the first calibration data set and the second calibration data set to obtain a calibration data set; it trains and constructs a calibration and matching model with the calibration data set; the calibration and matching model outputs an identification result indicating the calibration and matching of the antenna position information and the target signal position information.

[0167] Positioning and prediction module: It is used for the output result of the calibration and matching model, and uses non-line-of-sight microwave in the disaster area environment to realize the judgment of the accurate azimuth of accident victims by the antenna receiving the target signal.

[0168] Specifically, by combining Beidou positioning and signal detection and analysis, it can achieve rapid and accurate alignment of non-line-of-sight microwave antennas in complex environments, ensuring the stability of the communication link in mobile environments or unpredictable complex environments. The key technologies for rapid alignment of Beidou + 5G non-line-of-sight microwave antennas are as follows:

[0169] 1) Beidou positioning technology: Install a Beidou positioning module on the communication end and use the high-precision positioning capability of the Beidou system to quickly align the antenna even in adverse weather conditions, reducing signal attenuation or loss caused by position errors.

[0170] 2) 5G communication technology: 5G portable terminals use 5G networks for end-to-end data exchange, ensuring that location information between antennas can be transmitted in real time under non-line-of-sight conditions. 5G's high rate and low latency characteristics help to adjust the direction of the antenna in time to maintain the best connection.

[0171] 3) Signal maximum detection technology: During the alignment process, the signal maximum detection technology is used to further optimize the direction of the antenna. Based on the maximum likelihood estimation algorithm for signal detection, the received signal r(t) is considered to be composed of ideal signals. By adjusting the antenna direction to find the maximum value of the target signal, that is, the position where the signal is the strongest, the direction of the antenna can be accurately corrected to achieve the optimal signal transmission effect.

[0172] This portable terminal is suitable for emergency response after natural disasters, field operations, and any scenario where an emergency communication link needs to be established quickly.

[0173] like Figure 3 As shown in the figure, the 5G portable terminal integrates 5G base station equipment, power equipment, transmission equipment, antenna and other equipment, and is lightweight and flexible. The terminal adopts an integrated design and is usually used for emergency communications, temporary network construction or temporary 5G coverage in remote areas. The terminal consists of:

[0174] 5G base station equipment, which has a miniaturized 5G base station built in, supports data and voice communications and usually includes the functions of the core network part, such as EPC, so that it can operate independently or interconnect with the existing network.

[0175] Power supply equipment integrates batteries or other forms of portable power to ensure normal operation without mains power, and can be assisted by solar panels or other renewable energy sources to extend the working time of the equipment.

[0176] Transmission equipment: The terminal has a built-in satellite communication module or non-line-of-sight microwave communication equipment to transmit data in places where there is a lack of ground network infrastructure. It supports multiple transmission protocols and technical modules, such as Wi-Fi, Bluetooth, etc., to communicate with other devices over short distances.

[0177] The antenna is compact and easy to install. It can be automatically deployed under special circumstances, especially in complex geographical environments. It consists of an antenna, an antenna pan-tilt mount, and an actuator installed on the antenna mount. Its functions are as follows:

[0178] Antenna. The antenna is one of the core components of the entire system, responsible for transmitting and receiving radio signals. It is designed as a high-gain antenna to ensure good signal transmission performance even under non-line-of-sight conditions.

[0179] Antenna pan-tilt mount. The antenna pan-tilt mount is a mechanical structure that supports the antenna and allows it to rotate in multiple axes, including a horizontal rotation mechanism (azimuth angle adjustment) and a vertical rotation mechanism (elevation angle adjustment), enabling the antenna to adjust its direction as needed. The mount has sufficient stability and precision to ensure that the antenna does not produce unnecessary vibrations or offsets during adjustment.

[0180] Actuating elements mounted on the antenna mount. The actuating elements are mechanical components that drive the antenna pan-tilt mount, including motors, servo systems, etc. These elements are usually driven by the control system through electronic signals to achieve precise azimuth and elevation angle adjustments. The actuating elements need to have the capabilities of high precision and fast response to meet the requirements of rapid alignment.

[0181] Fast alignment and precise matching application terminal. Based on Beidou precise positioning technology and signal maximum value detection technology, it can achieve adaptive fast focusing, greatly improving the response speed and transmission rate of the system, and realizing the adaptive fast alignment of non-line-of-sight microwave antennas. It can play a key role in a variety of application scenarios, especially in occasions where a high-quality wireless communication link needs to be established quickly.

[0182] The system terminal can adaptively adjust the antenna direction according to the change of the received signal strength to achieve fast focusing. This adaptive mechanism enables the antenna to maintain the best signal reception state in a dynamic environment; the system can establish an effective communication connection faster, reducing the delay caused by misalignment of the antenna. This is particularly important for applications that require real-time communication, such as video backhaul, remote control, etc.; when the antenna is precisely aligned, the signal strength is the maximum, directly improving the quality and efficiency of data transmission. A stronger signal means a lower bit error rate and higher data throughput.

[0183] After a natural disaster occurs, quickly restoring communication is crucial. This device supports rapid deployment and establishing a communication link; in places without fixed infrastructure, it can help establish and maintain communication with the headquarters in the front; in an emergency environment, fire and other emergency departments can utilize this device to ensure smooth communication in an emergency. In short, the fast alignment and precise matching application terminal combines Beidou positioning and signal detection technologies, providing an efficient and reliable wireless communication solution, which is especially suitable for application scenarios that require rapid deployment and high-quality communication.

[0184] When the functions of the above-mentioned modules are implemented in the form of software functional units and used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0185] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device may be a well-known general-purpose intelligent device. Therefore, the object of the present invention can also be achieved only by providing a program product containing program codes for implementing the method or system. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium may be any well-known storage medium or any storage medium developed in the future. It should also be noted that in the devices and methods of the present invention, obviously, each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present invention. And, the steps of performing the above series of processes can naturally be executed in chronological order according to the described order, but it is not necessary to execute them in chronological order. Certain steps can be executed in parallel or independently of each other.

Claims

1. A matching method based on 5G network, Beidou positioning and non-line-of-sight microwave, characterized in that It includes the following steps: S100, obtain high-precision geographical location information data to obtain a first calibration data set; according to the first calibration data set, enable the antenna to adaptively focus quickly and adjust the direction; Among them, according to the first calibration data set, enabling the antenna to adaptively focus quickly and adjust the direction includes: Based on the first calibration data set, the azimuth of the antenna is initially adjusted to obtain the initial reference direction of the antenna θ0 = (θ ref , φ ref ); where θ ref represents the initial elevation angle of the antenna and φ ref represents the initial azimuth angle of the antenna; the initial reference direction of the antenna is calibrated based on the target position coordinates to determine its starting orientation; The antenna based on the initial reference direction receives the target signal, and through r(t)=A(t)·S(t)+n(t), perform interference processing on the received target signal to obtain the real target signal; in the formula, r(t) represents the real signal vector received by the antenna; A(t) represents the gain matrix, representing the attenuation and gain of the signal; S(t) represents the real transmitted signal; n(t) represents the noise or interference signal; Based on receiving a real target signal, through the search antenna initially adjusts its direction so that the antenna adaptively adjusts the azimuth of its received signal according to the initial reference direction benchmark; when the antenna automatically adjusts and optimizes the reception angle of the target signal, to determine the target pointing of the antenna; where, θ * (t) represents the target pointing after the initial adjustment of the antenna; argmax r(t) represents the real target signal strength; represents the adjustment function of the initial reference direction of the antenna; Based on the antenna for preliminary focusing, focus on the signal source in the target area; through preliminary focusing, the antenna receives the target signal and real-time feedback adjusts the direction accuracy, so that the reception error between the antenna and the target signal is relatively small; S200, based on the preliminary adjustment of the antenna direction, enable the antenna to collect signals and analyze the signal strength change to obtain a second calibration data set; according to the second calibration data set, dynamically adjust the antenna direction to match the target signal under non-line-of-sight conditions; S300, combine the first calibration data set and the second calibration data set to obtain a calibration data set; train and construct a calibration matching model with the calibration data set; the calibration matching model outputs an identification result indicating the calibration matching of the antenna position information and the target signal position information; S400, based on the output result of the calibration matching model, use non-line-of-sight microwaves in the disaster environment to realize the judgment of the accurate azimuth of the accident personnel by the antenna receiving the target signal; Among them, based on the output result of the calibration matching model, using non-line-of-sight microwaves in the disaster environment to realize the judgment of the accurate azimuth of the accident personnel by the antenna receiving the target signal includes: Based on the output result of the calibration matching of the antenna position information and the target signal position information; and combine the dynamic adjustment of the antenna direction and the signal strength of the target signal, and perform signal modeling through the non-line-of-sight microwave frequency to describe the signal band change under non-line-of-sight propagation in multipath propagation and obstacle reflection and diffraction; Through the combination of the signal band change and the signal strength, and then according to the terrain complexity and obstacle distribution, obtain the channel condition of the antenna in the complex environment; dynamically adjust the antenna modulation and coding signals according to the channel condition; and diversify the signal reception environment to obtain space diversity, time diversity and frequency diversity; Based on the obtained signal reception space diversity, time diversity and frequency diversity, obtain the signal band amplitudes of different diversity information; integrate the three diversity information band amplitudes of the corresponding nodes to obtain a band amplitude data integration set; according to the band amplitude data integration set, obtain the highest point band amplitude integration data; the highest point data of the band amplitude is the preliminary predicted target position of the accident personnel; Based on the multi-source data of the antenna receiving signals, and combined with the calibration matching model, the accurate position of the predicted accident personnel is updated in real time.

2. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 1, wherein Based on the preliminary adjustment of the antenna direction, the antenna is made to collect signals and analyze the change in signal strength to obtain a second calibration data set; According to the second calibration data set, the initial direction of the antenna is dynamically adjusted to match the target signal under non-line-of-sight conditions, including: When the antenna at the initial reference direction receives the target signal in real time, the target signals received multiple times are marked to obtain a signal reception marker point set; the signal reception marker point set includes the signal emission intensity, the signal intensity when received by the antenna, and the antenna angle of the received signal; Based on the signal reception marker point set, the information data of multiple signal reception marker points are recorded and compared to obtain the signal strength differences of multiple signal reception marker points; according to the signal strength differences of the signal marker points, the initial reference direction of the antenna is preliminarily adjusted; Based on the signal reception marker point set, for the initial signal strength P = P0 + 10nlog 10 (d) + X α Further analyze to obtain a second calibration data set; where P represents the received initial signal strength; P0 represents the reference distance; n represents the path loss exponent; d represents the actual distance from the antenna; X α represents the lognormal shadow fading, which varies randomly due to the change of large-scale environmental attenuation.

3. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 2, wherein Recording and comparing the information data of multiple signal reception marker points to obtain the signal strength differences of multiple signal reception marker points, including: Based on the signal reception marker point set, multiple triple data sets (P out , P recv , θ recv ) are formed with the received marker points, where P out represents the signal emission intensity; P recv represents the received signal intensity; θ recv represents the received signal angle; Based on the obtained triple data sets; comparing multiple said triple data sets to form signal reception marker point information pairs; each pair consists of two triple data sets (P out，i , P recv，i , θ recv，i ) and (P out，j , P recv，j , θ recv，j ); where i and j respectively represent data items in different triple data sets; Based on the signal reception marker point information pair, by to obtain the logarithmic difference in signal strength of the signal reception marker points; where ΔP i，j represents the logarithmic difference in signal strength of the signal reception marker points; the logarithmic difference in signal strength of the signal reception marker points is the signal strength difference of the signal reception marker points.

4. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 3, characterized in that Further analyzing the initial signal strength to obtain a second calibration data set, including: Obtaining the initial true target signal strength at different positions and different time periods, and analyzing the change of the signal by comparing the signal strength data at different times or positions; According to the change of the signal, by obtaining the mean value and standard deviation of the signal strength at different positions or different time periods to evaluate the signal stability and change range. In the formula, μ represents the mean value of the signal strength; h represents the standard deviation of the signal strength; N represents the number of signal reception marking points; P i represents the signal strength data collected in real time; The smaller the standard deviation h or the higher the signal-to-noise ratio SNR, the more stable the signal; the change range ΔP range = P recv，max - P recv，min ; where, ΔP range represents the change range; P revb，max represents the upper limit of the signal intensity change; P recv，min represents the lower limit of the intensity change; According to the signal stability and variation range, by to determine the relationship between the signal strength and the antenna direction; where represents the correlation coefficient between the signal strength and the antenna direction; f(θ) represents the fitting function; Through the correlation coefficient between the signal strength and the antenna direction, the relationship between the signal strength and the antenna direction is obtained. Under non-line-of-sight conditions, the signal strength usually changes with the change of the antenna angle; through the known relationship between the signal strength and the antenna direction, the antenna is dynamically adjusted twice to optimize the pointing of the received target signal.

5. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 1, wherein Training and constructing a calibration matching model with the calibration data set; the calibration matching model outputs an identification result representing the calibration matching of the antenna position information and the target signal position information, including: Generating structural data from the historical calibration data set, encoding the structural data into sequence data, and training to obtain the calibration matching model; Inputting the sequence data into the calibration matching model; the calibration matching model includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer, and an output layer, transmitting the intermediate representation data of multiple hidden layers to the output layer, and the output layer outputs an identification result representing the calibration matching of the antenna position information and the target signal position information; Inputting the newly obtained calibration data set into the calibration matching model to output a predicted result representing the calibration matching of the antenna position information and the target signal position information output by the output layer.

6. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 1, wherein Based on the obtained signal reception spatial diversity, time diversity, and frequency diversity, the signal band amplitudes of different diversity information are obtained; Integrating the band amplitudes of the three diversity information of the corresponding nodes to obtain a band amplitude data integration set, including: The spatial diversity means that multiple antennas are arranged and adjusted to direct the received signal to receive the target signal; the time diversity means sampling the target signal at different time nodes; the frequency diversity means multiple frequency channels; Based on the spatial diversity, the temporal diversity, and the frequency diversity, extract the band amplitudes of the signals for the diversity cases of the three signals; and perform alignment and normalization processing on the signal band amplitude data; to obtain unified standard band amplitude data; Integrate the unified standard band amplitude data into a unified data set to enhance the signals in the unified data set; and optimize the communication in complex environments and track the target positions of accident victims.

7. The matching method based on 5G network, Beidou positioning and non-line-of-sight microwave according to claim 6, characterized in that Based on the multi-source data of the signals received by the antenna and combined with the calibration matching model, to update the accurate position of the predicted accident victims in real time, including: Construct a three-dimensional terrain map of the accident area and combine signal modeling to obtain the target position signal information of the accident victims for preliminary prediction; use the diversity of the channel conditions and the signal reception environment to verify the initial prediction of the spatial position of the signal information of the accident victims; to judge the accurate orientation of the victims; And update the positioning data of the target person in real time through X k = X k-1 + K k ·(z k - H·X k-1 ) to improve the accuracy of predicting the target position; where X k represents the predicted position; z k represents the measured position; K k represents the filtering gain; H represents the observation matrix.

8. A matching system based on 5G network, Beidou positioning and non-line-of-sight microwave, characterized in that The system sets an electronic device including a memory, a processor, and a matching method program based on 5G network, Beidou positioning, and non-line-of-sight microwave stored on the memory and executable on the processor. When the matching method program based on 5G network, Beidou positioning, and non-line-of-sight microwave is executed by the processor, it realizes the steps of the matching method based on 5G network, Beidou positioning, and non-line-of-sight microwave as described in any one of claims 1-7. The system includes: Information acquisition module: It is used to obtain high-precision geographical location information data to obtain a first calibration data set; according to the first calibration data set, enable the antenna to adaptively focus quickly and adjust the direction; Among them, according to the first calibration data set, enabling the antenna to adaptively focus quickly and adjust the direction includes: Based on the first calibration data set, perform a preliminary adjustment on the azimuth of the antenna to obtain the initial reference direction of the antenna θ0 = (θ ref , φ ref ); where, in the formula, θ ref represents the initial elevation angle of the antenna and φ ref represents the initial azimuth angle of the antenna; the initial reference direction of the antenna is calibrated based on the target position coordinates to determine its starting orientation; Based on the antenna receiving the target signal in the initial reference direction, through r(t)=A(t)·S(t)+n(t), perform interference processing on the received target signal to obtain the real target signal; where r(t) represents the real signal vector received by the antenna; A(t) represents the gain matrix, representing the attenuation and gain of the signal; S(t) represents the real transmitted signal; n(t) represents the noise or interference signal; Based on receiving a real target signal, by the search antenna initially adjusts its direction so that the antenna adaptively adjusts the azimuth of its received signal according to the initial reference direction benchmark; when the antenna automatically adjusts and optimizes the reception angle of the target signal, to determine the target pointing of the antenna; where θ * (t) represents the target pointing after the initial adjustment of the antenna; argmax r(t) represents the real target signal strength; represents the adjustment function of the initial reference direction of the antenna; Based on the antenna for preliminary focusing, focus on the signal source in the target area; through preliminary focusing, the antenna receives the target signal and real-time feedback adjusts the direction accuracy to make the reception error between the antenna and the target signal relatively small; Detection and adjustment module: It is used to based on the preliminary adjustment of the antenna direction, enable the antenna to collect signals and analyze the signal strength change to obtain a second calibration data set; according to the second calibration data set, dynamically adjust the antenna direction to match the target signal under non-line-of-sight conditions; Calibration matching module: It is used to combine the first calibration data set and the second calibration data set to obtain a calibration data set; train and construct a calibration matching model with the calibration data set; the calibration matching model outputs an identification result representing the calibration matching of the antenna position information and the target signal position information; Positioning prediction module: It is used to based on the output result of the calibration matching model, use non-line-of-sight microwave in the disaster environment to realize the judgment of the accurate orientation of the accident victims by the antenna receiving the target signal; Among them, based on the output result of the calibration matching model, non-line-of-sight microwaves are used in the disaster environment to determine the precise position of accident personnel by receiving target signals with an antenna, including: Based on the output result of the calibration and matching of the antenna position information and the target signal position information; and combining the dynamic adjustment of the antenna direction and the signal strength of the target signal, signal modeling is carried out through the non-line-of-sight microwave frequency to describe the signal band change under non-line-of-sight propagation in multipath propagation and obstacle reflection and diffraction; Combining the signal band change with the signal strength, and then according to the terrain complexity and obstacle distribution, to obtain the channel condition of the antenna in the complex environment; dynamically adjusting the antenna modulation and coding signals according to the channel condition; and diversifying the signal receiving environment to obtain spatial diversity, time diversity and frequency diversity; Based on the obtained signal receiving spatial diversity, time diversity and frequency diversity, to obtain the signal band amplitudes of different diversity information; integrating the band amplitudes of the three diversity information of the corresponding nodes to obtain a band amplitude data integration set; according to the band amplitude data integration set, obtaining the integrated data of the highest point band amplitude; the highest point data of the band amplitude is the preliminary predicted target position of the accident personnel; Based on the multi-source data of the antenna received signal, and combining with the calibration matching model, to real-time update the precise position of the predicted accident personnel.

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