A method, device and system for reconstructing stereo spectrum data with combined drive

Abstract

The present invention provides a method, apparatus and system for reconstructing three-dimensional spectrum data with combined drive. The method includes calculating the total received signal strength of all sampling points; calculating the azimuth weight between the point to be reconstructed and the sampling points; calculating the weight of the sampling points; calculating the distance weight between the sampling points and the point to be reconstructed; calculating the received signal strength value of the point to be reconstructed; calculating the distance weight of the received signal strength value of the sampling points on the reconstruction result; calculating the path loss between the sampling points and the point to be reconstructed; calculating the total weight of the path loss; calculating the influence weight of the path loss between the sampling points and the point to be reconstructed on the point to be reconstructed; calculating the distance weight in the comprehensive weight; calculating the azimuth weight in the comprehensive weight; calculating the path loss model weight in the comprehensive weight; calculating the comprehensive weight; and calculating the received signal strength value of the point to be reconstructed. The present invention can effectively improve the accuracy of three-dimensional spectrum data reconstruction and realize the reconstruction of three-dimensional spectrum data.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication, and particularly relates to a method, device and system for reconstructing three-dimensional spectrum data with combined drive. Background Art

[0002] With the rapid development of radio communication technology and the advent of the 5G era, electromagnetic spectrum cognition technology has become one of the core technologies in the current information era. However, at present, the global mobile traffic is increasing continuously, and the limited spectrum resources are becoming increasingly scarce; phenomena such as black radio, pseudo base stations, and illegal radio equipment emerge in an endless stream, and the security of spectrum resources faces severe problems; in addition, spectrum resources play a crucial role in modern warfare, and the electromagnetic spectrum confrontation between countries is becoming increasingly fierce. In order to face the three major problems of spectrum resource shortage, spectrum security severity, and spectrum confrontation intensity, it is necessary to master the spatial distribution information of spectrum resources. Therefore, an efficient spectrum data reconstruction method is the key.

[0003] Traditional spectrum data reconstruction methods mainly include two categories: data-driven and model-driven, mainly including tensor completion method, inverse distance weighting method, and method based on emission source position estimation, etc. When traditional spectrum data reconstruction methods are used for spectrum data reconstruction, most of them are only applicable to the two-dimensional plane spectrum data reconstruction scenario. If the traditional methods are directly used for three-dimensional spectrum data reconstruction, these methods usually have the disadvantages of low reconstruction accuracy and unsatisfactory performance. Therefore, it is urgent to study a high-precision spectrum reconstruction method applicable to the three-dimensional spectrum data reconstruction scenario and a prefabricated matching hardware system. Summary of the Invention

[0004] Aiming at the deficiencies in the prior art, the present invention provides a method, device and system for reconstructing three-dimensional spectrum data with combined drive.

[0005] In a first aspect, the present invention provides a method for reconstructing three-dimensional spectrum data with combined drive, including:

[0006] Calculating the total received signal strength of all sampling points;

[0007] Calculating the azimuth weight between the point to be reconstructed and the sampling points according to the total received signal strength of all sampling points;

[0008] Calculating the weight of the sampling points;

[0009] Calculating the distance weight between the sampling points and the point to be reconstructed according to the weight of the sampling points;

[0010] Calculating the received signal strength value of the point to be reconstructed according to the distance weight between the sampling points and the point to be reconstructed;

[0011] Calculating the distance weight of the received signal strength value of the sampling points to the reconstruction result;

[0012] Calculate the path loss between the sampling point and the point to be reconstructed according to the distance between the sampling point and the point to be reconstructed;

[0013] Calculate the total weight of the path loss;

[0014] Calculate the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed according to the total weight of the path loss;

[0015] Calculate the distance weight in the comprehensive weight according to the received signal strength value of the sampling point for the distance weight of the reconstruction result;

[0016] Calculate the azimuth weight in the comprehensive weight according to the azimuth weight between the point to be reconstructed and the sampling point;

[0017] Calculate the path loss model weight in the comprehensive weight according to the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed;

[0018] Calculate the comprehensive weight according to the distance weight in the comprehensive weight, the azimuth weight in the comprehensive weight and the path loss model weight in the comprehensive weight;

[0019] Calculate the received signal strength value of the point to be reconstructed according to the received signal strength value of the sampling point and the comprehensive weight.

[0020] Further, the calculating the azimuth weight between the point to be reconstructed and the sampling point according to the total received signal strength of all sampling points includes:

[0021] Calculate the azimuth weight between the point to be reconstructed and the sampling point according to the following formula:

[0022]

[0023] where a m is the azimuth weight between the point to be reconstructed and the sampling point; N is the total number of sampling points; m = 1, 2,..., N; n = 1, 2,..., N; n ≠ m; P i is the received signal strength value of the sampling point; is the direction vector from the point to be reconstructed to the m-th sampling point; is the direction vector from the point to be reconstructed to the n-th sampling point; φ mn is and the included angle between; Ω φ is the total received signal strength of all sampling points;

[0024] Calculate the total received signal strength of all sampling points according to the following formula:

[0025]

[0026] Further, calculating the received signal strength value of the point to be reconstructed according to the distance weight between the sampling point and the point to be reconstructed includes:

[0027] Calculating the received signal strength value of the point to be reconstructed according to the following formula:

[0028]

[0029] where P′ m is the received signal strength value of the point to be reconstructed; N is the total number of sampling points; ω m is the distance weight between the sampling point and the point to be reconstructed; P m is the received signal strength value of the sampling point; ΔP is the slope of the interpolation function;

[0030] Calculating the distance weight between the sampling point and the point to be reconstructed according to the following formula:

[0031]

[0032] where is the distance between the sampling point and the point to be reconstructed; Ω d is the weight of the sampling point; κ is the attenuation factor of the distance;

[0033] Calculating the weight of the sampling point according to the following formula:

[0034]

[0035] Further, calculating the distance weight of the received signal strength value of the sampling point on the reconstruction result includes:

[0036] Calculating the distance weight of the received signal strength value of the sampling point on the reconstruction result according to the following formula:

[0037]

[0038] where q m is the distance weight of the received signal strength value of the sampling point on the reconstruction result; d m is the distance between the sampling point and the point to be reconstructed; R m is the radius of the circular influence area centered on the sampling point.

[0039] Further, calculating the path loss between the sampling point and the point to be reconstructed according to the distance between the sampling point and the point to be reconstructed includes:

[0040] Calculating the path loss between the sampling point and the point to be reconstructed according to the following formula:

[0041]

[0042] Among them, is the path loss between the sampling point and the point to be reconstructed; β is the path loss exponent; f is the center frequency of the spectrum data; d m is the distance between the sampling point and the point to be reconstructed; FSPL(f, d0) = 20log 10 (4π / c) is the free space loss relative to the reference distance d0 = 1m; c is the speed of light; is a Gaussian random variable with a mean of zero and a standard deviation of σ.

[0043] Furthermore, calculating the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed according to the total weight of the path loss includes:

[0044] Calculating the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed according to the following formula:

[0045]

[0046] where, l m is the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed; is the path loss between the sampling point and the point to be reconstructed; Ω p is the total weight of the path loss;

[0047] Calculating the total weight of the path loss according to the following formula:

[0048]

[0049] Furthermore, calculating the comprehensive weight according to the distance weight in the comprehensive weight, the azimuth angle weight in the comprehensive weight, and the path loss model weight in the comprehensive weight includes:

[0050] Calculating the comprehensive weight according to the following formula:

[0051] Ω = ω r ω d ω p / Ω d ;

[0052] where, Ω is the comprehensive weight; ω r is the distance weight in the comprehensive weight; ω d is the azimuth angle weight in the comprehensive weight; ω p is the path loss model weight in the comprehensive weight; Ω d is the weight of the sampling point;

[0053] Calculating the weight of the sampling point according to the following formula:

[0054]

[0055] Calculate the distance weight in the comprehensive weight according to the following formula:

[0056]

[0057] where α1 is a hyperparameter for the influence of the distance factor on the total weight of spectrum reconstruction; q m is the distance weight of the received signal strength value of the sampling point on the reconstruction result;

[0058] Calculate the azimuth weight in the comprehensive weight according to the following formula:

[0059]

[0060] where α2 is a hyperparameter for the influence of the azimuth factor on the total weight of spectrum reconstruction; a m is the azimuth weight between the point to be reconstructed and the sampling point;

[0061] Calculate the path loss model weight in the comprehensive weight according to the following formula:

[0062]

[0063] where both α3 and δ are hyperparameters of the radio propagation path loss factor for the total weight of spectrum reconstruction; l m is the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed.

[0064] Furthermore, calculating the received signal strength value of the point to be reconstructed according to the received signal strength value of the sampling point and the comprehensive weight includes:

[0065] Calculate the received signal strength value of the point to be reconstructed according to the following formula:

[0066]

[0067] where P0 is the received signal strength value of the point to be reconstructed; Ω is the comprehensive weight; P m is the received signal strength value of the sampling point.

[0068] In a second aspect, the present invention provides a jointly driven three-dimensional spectrum data reconstruction device, including:

[0069] A first calculation module for calculating the total received signal strength of all sampling points;

[0070] A second calculation module for calculating the azimuth weight between the point to be reconstructed and the sampling point according to the total received signal strength of all sampling points;

[0071] A third calculation module for calculating the weight of the sampling point;

[0072] A fourth calculation module, configured to calculate a distance weight between a sampling point and a point to be reconstructed according to the weight of the sampling point.

[0073] A fifth calculation module, configured to calculate a received signal strength value of the point to be reconstructed according to the distance weight between the sampling point and the point to be reconstructed.

[0074] A sixth calculation module, configured to calculate a distance weight of the received signal strength value of the sampling point on the reconstruction result.

[0075] A seventh calculation module, configured to calculate a path loss between the sampling point and the point to be reconstructed according to the distance between the sampling point and the point to be reconstructed.

[0076] An eighth calculation module, configured to calculate a total weight of the path loss.

[0077] A ninth calculation module, configured to calculate an influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed according to the total weight of the path loss.

[0078] A tenth calculation module, configured to calculate a distance weight in the comprehensive weight according to the distance weight of the received signal strength value of the sampling point on the reconstruction result.

[0079] An eleventh calculation module, configured to calculate an azimuth weight in the comprehensive weight according to the azimuth weight between the point to be reconstructed and the sampling point.

[0080] A twelfth calculation module, configured to calculate a path loss model weight in the comprehensive weight according to the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed.

[0081] A thirteenth calculation module, configured to calculate a comprehensive weight according to the distance weight in the comprehensive weight, the azimuth weight in the comprehensive weight, and the path loss model weight in the comprehensive weight.

[0082] A fourteenth calculation module, configured to calculate a received signal strength value of the point to be reconstructed according to the received signal strength value of the sampling point and the comprehensive weight.

[0083] In a third aspect, the present invention provides a jointly-driven three-dimensional spectrum data reconstruction system, including:

[0084] An air flight module, a spectrum data acquisition module, a signal transmission module, and a data processing module;

[0085] The GPS receiving unit of the air flight module is configured to collect GPS data of the air flight module in a measurement area and transmit the GPS data to the spectrum data acquisition module;

[0086] The image processing unit of the aerial flight module is used to collect image data of the aerial flight module within the measurement area and transmit the image data to the spectrum data acquisition module;

[0087] The control unit of the aerial flight module is used to collect status data of the aerial flight module within the measurement area and transmit the status data to the spectrum data acquisition module;

[0088] The spectrum data acquisition module is used to transmit spectrum data to the spectrum data receiving unit and the measurement antenna unit, and transmit the spectrum data to the airborne data link terminal;

[0089] The signal transmission module is used to transmit the GPS data, image data and status data of the aerial flight module to the data processing module;

[0090] The data processing module is used to perform data reconstruction by using the three-dimensional spectrum data reconstruction method described in the first aspect, and output the obtained three-dimensional spectrum map.

[0091] The present invention provides a combined drive three-dimensional spectrum data reconstruction method, device and system. The method includes calculating the total received signal strength of all sampling points; calculating the azimuth weight between the point to be reconstructed and the sampling points according to the total received signal strength of all sampling points; calculating the weight of the sampling points; calculating the distance weight between the sampling points and the point to be reconstructed according to the weight of the sampling points; calculating the received signal strength value of the point to be reconstructed according to the distance weight between the sampling points and the point to be reconstructed; calculating the distance weight of the received signal strength value of the sampling points on the reconstruction result; calculating the path loss between the sampling points and the point to be reconstructed according to the distance between the sampling points and the point to be reconstructed; calculating the total weight of the path loss; calculating the influence weight of the path loss between the sampling points and the point to be reconstructed on the point to be reconstructed according to the total weight of the path loss; calculating the distance weight in the comprehensive weight according to the distance weight of the received signal strength value of the sampling points on the reconstruction result; calculating the azimuth weight in the comprehensive weight according to the azimuth weight between the point to be reconstructed and the sampling points; calculating the path loss model weight in the comprehensive weight according to the influence weight of the path loss between the sampling points and the point to be reconstructed on the point to be reconstructed; calculating the comprehensive weight according to the distance weight in the comprehensive weight, the azimuth weight in the comprehensive weight and the path loss model weight in the comprehensive weight; calculating the received signal strength value of the point to be reconstructed according to the received signal strength value of the sampling points and the comprehensive weight. The present invention comprehensively considers the weights of the three factors of azimuth, distance and model in the process of spectrum data reconstruction; has the characteristics of flexibility and easy operation, and can effectively improve the accuracy of three-dimensional spectrum data reconstruction in combination with the reconstruction method; combines the three-dimensional spectrum data reconstruction method with the aerial spectrum data acquisition system to realize three-dimensional spectrum data reconstruction and overcome the shortcomings of the traditional two-dimensional spectrum data reconstruction method. Description of the Drawings

[0092] To more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0093] Figure 1 It is a flowchart of a method for reconstructing three-dimensional spectrum data with combined drive provided by an embodiment of the present invention;

[0094] Figure 2 It is a comparison chart of the existing reconstruction performance under different acquisition spectrum data uniformity conditions provided by an embodiment of the present invention;

[0095] Figure 3 It is a comparison chart of the existing reconstruction performance under different center frequencies of radiation sources provided by an embodiment of the present invention;

[0096] Figure 4 It is a reconstruction simulation chart under different acquisition spectrum data uniformity conditions provided by an embodiment of the present invention;

[0097] Figure 5 It is a reconstruction simulation chart under different center frequencies provided by an embodiment of the present invention;

[0098] Figure 6 It is a structural schematic diagram of a device for reconstructing three-dimensional spectrum data with combined drive provided by an embodiment of the present invention;

[0099] Figure 7 It is a structural schematic diagram of a system for reconstructing three-dimensional spectrum data with combined drive provided by an embodiment of the present invention;

[0100] Figure 8 It is a structural schematic diagram of a three-dimensional spectrum map output by a data processing module provided by an embodiment of the present invention. Detailed implementation manners

[0101] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0102] As Figure 1 shown, an embodiment of the present invention provides a method for reconstructing three-dimensional spectrum data with combined drive, including:

[0103] Step 101, calculate the total received signal strength of all sampling points.

[0104] Calculate the total received signal strength of all sampling points according to the following formula:

[0105]

[0106] where, Ω φ is the total received signal strength of all sampling points; P i is the received signal strength value of the sampling point.

[0107] Step 102, calculate the azimuth weight between the point to be reconstructed and the sampling points according to the total received signal strength of all sampling points.

[0108] Let the coordinates of the point to be reconstructed be (x0, y0, z0), the coordinates of the m-th sampling point be (x m , y m , z m ), and the coordinates of the n-th sampling point be (x n , y n , z n ).

[0109] Calculate the azimuth weight between the point to be reconstructed and the sampling points according to the following formula:

[0110]

[0111] where, a m is the azimuth weight between the point to be reconstructed and the sampling points; N is the total number of sampling points; m = 1, 2,..., N; n = 1, 2,..., N; n ≠ m; P i is the received signal strength value of the sampling point; is the direction vector from the point to be reconstructed to the m-th sampling point; is the direction vector from the point to be reconstructed to the n-th sampling point; φ mn is and the included angle between them.

[0112] Step 103, calculate the weight of the sampling points.

[0113] Calculate the weight of the m-th sampling point according to the following formula:

[0114]

[0115] where, Ω d is the weight of the m-th sampling point; is the distance between the sampling point and the point to be reconstructed; κ is the attenuation factor of the distance.

[0116] Step 104, calculate the distance weight between the sampling point and the point to be reconstructed according to the weight of the sampling point.

[0117] Calculate the distance weight between the sampling point and the point to be reconstructed according to the following formula:

[0118]

[0119] where ω m is the distance weight between the m-th sampling point and the point to be reconstructed.

[0120] Step 105: Calculate the received signal strength value of the point to be reconstructed according to the distance weight between the sampling point and the point to be reconstructed.

[0121] Calculate the received signal strength value of the point to be reconstructed according to the following formula:

[0122]

[0123] where P′ m is the received signal strength value of the point to be reconstructed; N is the total number of sampling points; P m is the received signal strength value of the m-th sampling point; ΔP is the slope of the interpolation function.

[0124] Combined with the improved Shepard method, establish a nodal equation, and use all sampling points (x m , y m , z m ) in the measurement area to perform global interpolation on the point to be reconstructed (x0, y0, z0).

[0125] Step 106: Calculate the distance weight of the received signal strength value of the sampling point on the reconstruction result.

[0126] Considering the received signal strength value centered on the point to be reconstructed (x0, y0, z0), calculate the distance weight of the received signal strength value of the sampling point on the reconstruction result according to the following formula:

[0127]

[0128] where q m is the distance weight of the received signal strength value of the sampling point on the reconstruction result; d m is the distance between the sampling point and the point to be reconstructed; R m is the radius of the circular influence area centered on the sampling point.

[0129] Step 107: Calculate the path loss between the sampling point and the point to be reconstructed according to the distance between the sampling point and the point to be reconstructed.

[0130] Calculate the path loss between the sampling point and the point to be reconstructed according to the following formula:

[0131]

[0132] Among them, is the path loss between the sampling point and the point to be reconstructed; β is the path loss exponent; f is the center frequency of the spectrum data; d m is the distance between the sampling point and the point to be reconstructed; FSPL(f, d0) = 20log 10 (4π / c) is the free space loss relative to the reference distance d0 = 1m; c is the speed of light; is a Gaussian random variable with a mean of zero and a standard deviation of σ, and the unit is dB.

[0133] Step 108, calculate the total weight of the path loss.

[0134] Calculate the total weight of the path loss according to the following formula:

[0135]

[0136] Among them, Ω p is the total weight of the path loss.

[0137] Step 109, calculate the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed according to the total weight of the path loss.

[0138] Calculate the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed according to the following formula:

[0139]

[0140] Among them, l m is the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed; is the path loss between the sampling point and the point to be reconstructed.

[0141] Step 1010, calculate the distance weight in the comprehensive weight according to the received signal strength value of the sampling point for the reconstruction result.

[0142] Calculate the distance weight in the comprehensive weight according to the following formula:

[0143]

[0144] Among them, α1 is the hyperparameter for the influence of the distance factor on the total weight of spectrum reconstruction; q m is the distance weight of the received signal strength value of the sampling point for the reconstruction result; ω r is the distance weight in the comprehensive weight.

[0145] Step 1011, calculate the azimuth weight in the comprehensive weight according to the azimuth weight between the point to be reconstructed and the sampling point.

[0146] Calculate the azimuth weight in the comprehensive weight according to the following formula:

[0147]

[0148] where α2 is the hyperparameter of the influence of the azimuth factor on the total weight of spectrum reconstruction; ω d is the azimuth weight in the comprehensive weight.

[0149] Step 1012, calculate the path loss model weight in the comprehensive weight according to the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed.

[0150] Calculate the path loss model weight in the comprehensive weight according to the following formula:

[0151]

[0152] where α3 and δ are both hyperparameters of the radio propagation path loss factor for the total weight of spectrum reconstruction; ω p is the path loss model weight in the comprehensive weight.

[0153] Step 1013, calculate the comprehensive weight according to the distance weight in the comprehensive weight, the azimuth weight in the comprehensive weight, and the path loss model weight in the comprehensive weight.

[0154] Calculate the comprehensive weight according to the following formula:

[0155] Ω = ω r ω d ω p / Ω d .

[0156] where Ω is the comprehensive weight.

[0157] Calculate the weight of the sampling point according to the following formula:

[0158]

[0159] Step 1014, calculate the received signal strength value of the point to be reconstructed according to the received signal strength value of the sampling point and the comprehensive weight.

[0160] Calculate the received signal strength value of the point to be reconstructed according to the following formula:

[0161]

[0162] where P0 is the received signal strength value of the point to be reconstructed; Ω is the comprehensive weight; P m is the received signal strength value of the sampling point.

[0163] Such as Figure 2 andFigure 3 As shown, the three-dimensional spectrum data reconstruction method provided by the present invention is compared with the tensor completion method and the traditional inverse distance weighting method; see the spectrum sampling point distribution and the reconstructed three-dimensional spectrum data diagram in Figure 4 and Figure 5 , where Figure 4 (a), (c), and (e) are the spectrum data sampling conditions when the uniformity is 15%, 50%, and 90%; Figure 4 (b), (d), and (f) are the three-dimensional spectrum data completion results when the uniformity is 15%, 50%, and 90%; Figure 5 (a), (b), (c), and (d) respectively represent the three-dimensional spectrum data completion results when the center frequencies are 100 MHz, 600 MHz, 1100 MHz, and 1500 MHz. It can be seen that when the uniformity of the collected spectrum data is low, the performance of the present invention in data reconstruction is greatly improved compared with the traditional method. As the uniformity of the collected spectrum data increases, the performance of the method of the present invention gradually improves. At different center frequencies, the reconstruction accuracy of the spectrum data of the present invention is still better than that of the traditional method.

[0164] Based on the same inventive concept, an embodiment of the present invention further provides a jointly driven three-dimensional spectrum data reconstruction device. Since the principle of solving problems by this device is similar to that of the foregoing jointly driven three-dimensional spectrum data reconstruction method, the implementation of this device can refer to the implementation of the jointly driven three-dimensional spectrum data reconstruction method, and the repeated parts will not be elaborated.

[0165] The jointly driven three-dimensional spectrum data reconstruction device provided by an embodiment of the present invention, as Figure 6 shown, includes:

[0166] The first calculation module 10 is used to calculate the total received signal strength of all sampling points.

[0167] The second calculation module 20 is used to calculate the azimuth weight between the point to be reconstructed and the sampling points according to the total received signal strength of all sampling points.

[0168] The third calculation module 30 is used to calculate the weight of the sampling points.

[0169] The fourth calculation module 40 is used to calculate the distance weight between the sampling points and the point to be reconstructed according to the weight of the sampling points.

[0170] The fifth calculation module 50 is used to calculate the received signal strength value of the point to be reconstructed according to the distance weight between the sampling points and the point to be reconstructed.

[0171] The sixth calculation module 60 is used to calculate the distance weight of the received signal strength value of the sampling points on the reconstruction result.

[0172] The seventh calculation module 70 is configured to calculate the path loss between the sampling point and the point to be reconstructed according to the distance between the sampling point and the point to be reconstructed.

[0173] The eighth calculation module 80 is configured to calculate the total weight of the path loss.

[0174] The ninth calculation module 90 is configured to calculate the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed according to the total weight of the path loss.

[0175] The tenth calculation module 100 is configured to calculate the distance weight in the comprehensive weight according to the received signal strength value of the sampling point for the distance weight of the reconstruction result.

[0176] The eleventh calculation module 110 is configured to calculate the azimuth weight in the comprehensive weight according to the azimuth weight between the point to be reconstructed and the sampling point.

[0177] The twelfth calculation module 120 is configured to calculate the path loss model weight in the comprehensive weight according to the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed.

[0178] The thirteenth calculation module 130 is configured to calculate the comprehensive weight according to the distance weight in the comprehensive weight, the azimuth weight in the comprehensive weight, and the path loss model weight in the comprehensive weight.

[0179] The fourteenth calculation module 140 is configured to calculate the received signal strength value of the point to be reconstructed according to the received signal strength value of the sampling point and the comprehensive weight.

[0180] For the more specific working processes of the above various modules, reference may be made to the corresponding content disclosed in the foregoing embodiments, and details will not be elaborated herein.

[0181] As Figure 7 and Figure 8 shown, an embodiment of the present invention further provides a jointly-driven three-dimensional spectrum data reconstruction system, including:

[0182] An air flight module 1, a spectrum data acquisition module 2, a signal transmission module 3, and a data processing module 4.

[0183] The GPS receiving unit 11 of the air flight module 1 is configured to collect GPS data of the air flight module 1 in the measurement area and transmit the GPS data to the spectrum data acquisition module 2.

[0184] The image processing unit 12 of the air flight module 1 is configured to collect image data of the air flight module 1 in the measurement area and transmit the image data to the spectrum data acquisition module 2.

[0185] The control unit 13 of the aerial flight module 1 is used to collect the status data of the aerial flight module 1 within the measurement area and transmit the status data to the spectrum data acquisition module 2.

[0186] The spectrum data acquisition module 2 is used to transmit the spectrum data to the spectrum data receiving unit 21 and the measurement antenna unit 22, and transmit the spectrum data to the airborne data link terminal.

[0187] The signal transmission module 3 is used to transmit the GPS data, image data and status data of the aerial flight module 1 to the data processing module 4.

[0188] The data processing module 4 is used to perform data reconstruction by using the joint-driven three-dimensional spectrum data reconstruction method and output the obtained three-dimensional spectrum map.

[0189] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For the devices and systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and reference can be made to the description of the method part for related parts.

[0190] The present invention has been described in detail above in combination with specific embodiments and exemplary examples, but these descriptions should not be construed as limiting the present invention. Those skilled in the art understand that without departing from the spirit and scope of the present invention, various equivalent substitutions, modifications or improvements can be made to the technical solutions and their implementation manners of the present invention, and all of these fall within the scope of the present invention. The protection scope of the present invention shall be subject to the appended claims.

Claims

1. A method for reconstructing three-dimensional spectral data with combined drive, characterized in that, Including: Calculate the total received signal strength of all sampling points; Calculate the azimuth weight between the point to be reconstructed and the sampling points according to the total received signal strength of all sampling points; Calculate the weight of the sampling points; Calculate the distance weight between the sampling points and the point to be reconstructed according to the weight of the sampling points; Calculate the received signal strength value of the point to be reconstructed according to the distance weight between the sampling points and the point to be reconstructed; Calculate the distance weight of the received signal strength value of the sampling points on the reconstruction result; Calculate the path loss between the sampling points and the point to be reconstructed according to the distance between the sampling points and the point to be reconstructed; Calculate the total weight of the path loss; Calculate the influence weight of the path loss between the sampling points and the point to be reconstructed on the point to be reconstructed according to the total weight of the path loss; Calculate the distance weight in the comprehensive weight according to the distance weight of the received signal strength value of the sampling points on the reconstruction result; Calculate the azimuth weight in the comprehensive weight according to the azimuth weight between the point to be reconstructed and the sampling points; Calculate the path loss model weight in the comprehensive weight according to the influence weight of the path loss between the sampling points and the point to be reconstructed on the point to be reconstructed; Calculate the comprehensive weight according to the distance weight in the comprehensive weight, the azimuth weight in the comprehensive weight and the path loss model weight in the comprehensive weight; Calculate the received signal strength value of the point to be reconstructed according to the received signal strength value of the sampling points and the comprehensive weight.

2. The three-dimensional spectrum data reconstruction method according to claim 1, characterized in that The calculating the azimuth weight between the point to be reconstructed and the sampling points according to the total received signal strength of all sampling points includes: Calculate the azimuth weight between the point to be reconstructed and the sampling points according to the following formula: where a m is the azimuth weight between the point to be reconstructed and the sampling point; N is the total number of sampling points; m = 1, 2,..., N; n = 1, 2,..., N; n ≠ m; P i is the received signal strength value of the sampling point; is the direction vector from the point to be reconstructed to the m-th sampling point; is the direction vector from the point to be reconstructed to the n-th sampling point; φ mn is and the included angle between; Ω φ is the total received signal strength of all sampling points; Calculate the total received signal strength of all sampling points according to the following formula:

3. The three-dimensional spectrum data reconstruction method according to claim 2, characterized in that The calculating the received signal strength value of the point to be reconstructed according to the distance weight between the sampling points and the point to be reconstructed includes: Calculate the received signal strength value of the point to be reconstructed according to the following formula: where P' m is the received signal strength value of the point to be reconstructed; N is the total number of sampling points; ω m is the distance weight between the sampling point and the point to be reconstructed; P m is the received signal strength value of the sampling point; ΔP is the slope of the interpolation function; Calculate the distance weight between the sampling points and the point to be reconstructed according to the following formula: wherein, is the distance between the sampling point and the point to be reconstructed; Ω d is the weight of the sampling point; κ is the attenuation factor of the distance; Calculate the weight of the sampling points according to the following formula:

4. The method for reconstructing three-dimensional spectrum data according to claim 3, wherein The calculating the distance weight of the received signal strength value of the sampling points on the reconstruction result includes: Calculate the distance weight of the received signal strength value of the sampling points on the reconstruction result according to the following formula: where q m is the distance weight of the received signal strength value of the sampling point to the reconstruction result; d m is the distance between the sampling point and the point to be reconstructed; R m is the radius of the circular influence area centered on the sampling point.

5. The three-dimensional spectrum data reconstruction method according to claim 4, wherein The calculating the path loss between the sampling points and the point to be reconstructed according to the distance between the sampling points and the point to be reconstructed includes: Calculate the path loss between the sampling points and the point to be reconstructed according to the following formula: Among them, is the path loss between the sampling point and the point to be reconstructed; β is the path loss exponent; f is the center frequency of the spectrum data; d m is the distance between the sampling point and the point to be reconstructed; FSPL(f, d0) = 20log 10 (4π / c) is the free space loss relative to the reference distance d0 = 1m; c is the speed of light; is a Gaussian random variable with a mean of zero and a standard deviation of σ.

6. The method for reconstructing three-dimensional spectral data according to claim 5, wherein The calculating the influence weight of the path loss between the sampling points and the point to be reconstructed on the point to be reconstructed according to the total weight of the path loss includes: Calculate the influence weight of the path loss between the sampling points and the point to be reconstructed on the point to be reconstructed according to the following formula: Among them, l m is the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed; is the path loss between the sampling point and the point to be reconstructed; Ω p is the total weight of the path loss; Calculate the total weight of the path loss according to the following formula:

7. The method for reconstructing three-dimensional spectrum data according to claim 6, wherein The calculating the comprehensive weight according to the distance weight in the comprehensive weight, the azimuth weight in the comprehensive weight and the path loss model weight in the comprehensive weight includes: Calculate the comprehensive weight according to the following formula: Ω = ω r ω d ω p / Ω d ; Among them, Ω is the comprehensive weight; ω r is the distance weight in the comprehensive weight; ω d is the azimuth weight in the comprehensive weight; ω p is the path loss model weight in the comprehensive weight; Ω d is the weight of the sampling point; Calculate the weight of the sampling points according to the following formula: Calculate the distance weight in the comprehensive weight according to the following formula: Among them, α1 is a hyperparameter for the influence of the distance factor on the total weight of spectrum reconstruction; q m is the distance weight of the received signal strength value of the sampling point on the reconstruction result; Calculate the azimuth weight in the comprehensive weight according to the following formula: Among them, α2 is a hyperparameter that represents the influence of the azimuth factor on the total weight of spectrum reconstruction; a m is the azimuthal angle weight between the point to be reconstructed and the sampling point; Calculate the path loss model weight in the comprehensive weight according to the following formula: Among them, both α3 and δ are hyperparameters of the radio propagation path loss factor for the total weight of spectrum reconstruction; l m is the influence weight of the path loss between the sampling point and the point to be reconstructed on the point to be reconstructed.

8. The three-dimensional spectrum data reconstruction method according to claim 7, wherein The calculating the received signal strength value of the point to be reconstructed according to the received signal strength value of the sampling points and the comprehensive weight includes: Calculate the received signal strength value of the point to be reconstructed according to the following formula: Among them, P0 is the received signal strength value of the point to be reconstructed; Ω is the comprehensive weight; P m is the received signal strength value of the sampling point.

9. A three-dimensional spectrum data reconstruction device with combined drive, characterized in that, Including: The first calculation module is used to calculate the total received signal strength of all sampling points; The second calculation module is used to calculate the azimuth weight between the point to be reconstructed and the sampling points according to the total received signal strength of all sampling points; The third calculation module is used to calculate the weight of the sampling points; The fourth calculation module is used to calculate the distance weight between the sampling points and the point to be reconstructed according to the weight of the sampling points; The fifth calculation module is used to calculate the received signal strength value of the point to be reconstructed according to the distance weight between the sampling points and the point to be reconstructed; The sixth calculation module is used to calculate the distance weight of the received signal strength value of the sampling points on the reconstruction result; The seventh calculation module is used to calculate the path loss between the sampling points and the point to be reconstructed according to the distance between the sampling points and the point to be reconstructed; The eighth calculation module is used to calculate the total weight of the path loss; The ninth calculation module is used to calculate the influence weight of the path loss between the sampling points and the point to be reconstructed on the point to be reconstructed according to the total weight of the path loss; The tenth calculation module is used to calculate the distance weight in the comprehensive weight according to the distance weight of the received signal strength value of the sampling points on the reconstruction result; The eleventh calculation module is used to calculate the azimuth weight in the comprehensive weight according to the azimuth weight between the point to be reconstructed and the sampling points; The twelfth calculation module is used to calculate the path loss model weight in the comprehensive weight according to the influence weight of the path loss between the sampling points and the point to be reconstructed on the point to be reconstructed; The thirteenth calculation module is used to calculate the comprehensive weight according to the distance weight in the comprehensive weight, the azimuth weight in the comprehensive weight and the path loss model weight in the comprehensive weight; The fourteenth calculation module is used to calculate the received signal strength value of the point to be reconstructed according to the received signal strength value of the sampling points and the comprehensive weight.

10. A stereo spectrum data reconstruction system with combined drive, characterized in that, The three-dimensional spectrum data reconstruction system includes an air flight module, a spectrum data acquisition module, a signal transmission module and a data processing module; The GPS receiving unit of the air flight module is used to collect the GPS data of the air flight module in the measurement area and transmit the GPS data to the spectrum data acquisition module; The image processing unit of the air flight module is used to collect the image data of the air flight module in the measurement area and transmit the image data to the spectrum data acquisition module; The control unit of the air flight module is used to collect the status data of the air flight module in the measurement area and transmit the status data to the spectrum data acquisition module; The spectrum data acquisition module is used to transmit the spectrum data to the spectrum data receiving unit and the measurement antenna unit, and transmit the spectrum data to the airborne data link terminal; The signal transmission module is used to transmit the GPS data, image data and status data of the air flight module to the data processing module; The data processing module is used to perform data reconstruction by using the three-dimensional spectrum data reconstruction method described in any one of claims 1-8, and output the obtained three-dimensional spectrum map.

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