Electromagnetic environment information representation and organization method based on electromagnetic multi-domain grid model

Abstract

The present invention provides an electromagnetic environment information characterization and organization method of an electromagnetic multi-domain grid model, comprising the following steps: Step 1: Firstly, the GeoSOT-3D spatial grid partitioning framework is used to perform multi-scale division of the spatial domain information in the electromagnetic environment six-domain characterization model, and then the geographic space grid division concept is used to discretize the six-domain representation, and a corresponding encoding method is designed to form an electromagnetic multi-domain grid together with the spatial domain grid, and a single multi-domain grid is used as the basic unit for organizing electromagnetic environment data; Step 2: Based on the constructed electromagnetic multi-domain grid, a corresponding electromagnetic information multi-domain grid storage structure is designed as a physical layer implementation of data organization and storage; Step 3: Based on the constructed electromagnetic multi-domain grid, a coding algebraic operation system is used to calculate the propagation path of the electromagnetic radiation source and the radar coverage range, and the electromagnetic data calculated or measured are expressed in true three-dimensional form with optional scale.

Description

Technical Field

[0001] The present invention belongs to the field of electromagnetic environment technology, and in particular relates to an electromagnetic environment information characterization and organization method of an electromagnetic multi-domain grid model. Background Art

[0002] With the transformation of war forms and military construction towards informatization, humans have opened up the fifth dimension of space, the electromagnetic space, on the basis of the four-dimensional battlefield space of land, sea, air and space, forming a new battlefield environment, the electromagnetic environment. It is precisely because of the opening up of the electromagnetic space that the relatively independent sky, air, land and sea have become a complete large three-dimensional battlefield, and the use and control of the electromagnetic spectrum has become the core capability element of modern warfare. At the same time, the development trend of informatization, intelligence and precision warfare in modern warfare has brought about an exponential growth of electromagnetic environment information, and put forward new requirements for the management of electromagnetic environment information: it is necessary to build a basic data model of the global electromagnetic environment, meet the rapid update of real-time perception of electromagnetic environment detection data, and have the ability of intelligent deduction. In this way, the electromagnetic environment can be marked with global frequency use like surveying and mapping, and a three-dimensional global space electromagnetic grid can be drawn; it can also perceive and predict the electromagnetic environment in all dimensions like meteorology; it can also be marked with electromagnetic tags like the enemy identification system to achieve rapid and autonomous frequency interaction between different systems. Therefore, accurate perception and fine characterization of the electromagnetic environment are the key.

[0003] In the past, we usually described the electromagnetic environment of a certain area in a flat way, mainly based on the distribution of electromagnetic radiation sources, and the time-varying characteristics were not obvious. There were problems that the three-dimensional electromagnetic environment information was difficult to organize uniformly, calculate efficiently, and express in an integrated manner. Taking aviation airspace information management as an example, during the flight, the aircraft takes off, cruises, and the time, space, and other factors according to the established route and speed produce a superposition effect on the spectrum characteristics, and interact with the electromagnetic environment along the route. However, at present, due to the lack of a unified spatial description of electromagnetic environment information, the efficiency of isomorphism analysis is poor, and it is difficult to integrate and organize electromagnetic environment data with flight route data. In addition, the existing spatial information calculation model based on longitude and latitude elevation points has problems such as inconsistent grid division, weak geographical correlation, and poor real-time calculation. It is impossible to process electromagnetic environment data information in multiple domains (time domain, frequency domain, air domain, energy domain, etc.) in real time, and it is difficult to effectively describe the internal structure and motion characteristics of multi-domain spatial objects, which in turn affects the efficient application of electromagnetic environment data and the reasonable allocation of electromagnetic equipment resources. In addition, the change of electromagnetic environment data is also accompanied by the generation of time information data. These time information come from a wide range of sources, have different structures and scales. Using the existing time coding model, time identification may be ambiguous, multi-scale time information is difficult to organize uniformly, and the efficiency of multi-scale time calculation needs to be improved.

[0004] From the above analysis, it can be seen that, whether in the military or civilian fields, the core bottleneck problems of electromagnetic environment information, such as the lack of unified time and space information and low computing efficiency, have put forward major application requirements for electromagnetic environment information: "unified organization, unified expression, and unified control". To meet the above requirements, the core is to solve the problem of how to efficiently organize, calculate and express multi-domain electromagnetic environment information and its time dimension.

[0005] In order to improve the ability of efficient and precise expression of electromagnetic environment information, it is necessary to describe the electromagnetic environment in a grid-like manner, solve the problem of electromagnetic environment data description in the time domain, frequency domain, and air domain, and build a global unified electromagnetic environment data organization framework. This invention attempts to establish a time-space electromagnetic environment data model. On the basis of the three-dimensional earth segmentation data model, the present invention uses multi-domain grids as units, discretizes electromagnetic environment information in the time domain, frequency domain, and air domain, and constructs a unified electromagnetic environment data field. Based on this, the visualization of electromagnetic environment data and the calculation of radiation source propagation paths are studied, providing a new theoretical basis and technical support for the construction of the next generation of intelligent electromagnetic environment information systems. Summary of the invention

[0006] The purpose of the present invention is to propose a method for characterizing and organizing electromagnetic environment information of an electromagnetic multi-domain grid model.

[0007] The technical solution adopted by the present invention is:

[0008] The electromagnetic environment information representation and organization method of the electromagnetic multi-domain grid model includes the following steps:

[0009] Step 1: First, use the GeoSOT-3D spatial grid partitioning framework to perform multi-scale division of the airspace information in the six-domain representation model of the electromagnetic environment. Then, use the geographic space grid partitioning concept to discretize the six-domain representation and design the corresponding encoding method. Together with the airspace grid, it constitutes an electromagnetic multi-domain grid. A single multi-domain grid is used as the basic unit for organizing electromagnetic environment data.

[0010] Step 2: Based on the constructed electromagnetic multi-domain grid, design the corresponding electromagnetic information multi-domain grid storage structure as the physical layer implementation of data organization and storage;

[0011] Step 3: Based on the constructed electromagnetic multi-domain grid, a coded algebraic operation system is used to calculate the propagation path of the electromagnetic radiation source and the radar coverage range, and to express the calculated or measured electromagnetic data in true three-dimensional form with selectable scale.

[0012] Preferably, in step 1, the six-domain representation includes time domain, frequency domain, space domain, energy domain and modulation domain representation, and the basic feature of the electromagnetic environment data is that it is constantly changing in the time domain, frequency domain and space domain. The three spaces of time domain, frequency domain and space domain are relatively independent. By associating the time domain, frequency domain and space domain, data association can be achieved in any time period, any frequency band and any space in the electromagnetic environment.

[0013] Preferably, in step 2, the time domain, frequency domain, space domain, and energy domain data of the electromagnetic environment in each grid are discretized by encoding, and the multi-domain grid is used as a basic unit. The encoding process is as follows:

[0014] (1) The time domain segmentation and decoding process is as follows:

[0015] GeoSOT time grid coding is used to discretize time into time intervals of different lengths and assign unique binary codes, so that time is discretized into time periods with lengths, forming a time discrete framework in the organization of electromagnetic environment information;

[0016] (2) The frequency domain is split and encoded, that is, the frequency split decoding process is as follows:

[0017] Frequency decoding is based on segmented expression. The frequency range from 0.1Hz to 1PHz is divided into 16 segments in units of 10 octaves, and each frequency segment is encoded in order from low to high. The encoding includes the following steps:

[0018] Step 1: According to the frequency value f to be expressed, determine the segment number FS where it is located and obtain the quantization frequency QF FS , QF FS =SF 7 ×10 -7 , among which, SF 7 is the starting frequency of the frequency band;

[0019] Step 2: Quantize the frequency value, express the quantization result in hexadecimal and retain 7 valid digits. If it is less than 7 digits, add 0 in front to get the frequency quantization value f Q ;

[0020] Step 3: Combine the frequency band code and the frequency quantization value, add the frequency signal flag word 0x46, and get the final frequency code.

[0021] (3) The spatial domain is divided and decoded, that is, the spatial grid encoding process is as follows: the longitude and latitude of the earth are expanded three times, from 360°×180° to 512°×512°, 1° to 64', and 1' to 64", and on this basis, 2n recursive division is implemented to obtain a 32-level longitude and latitude integer grid series with centimeter-level accuracy;

[0022] (4) The energy domain is divided and decoded, that is, the power grid encoding process is:

[0023] Power coding is also based on segmented expression, that is, the power range of 1aW to 100MW is divided into 13 segments in units of 100 times the frequency band, and each power segment is encoded in order from low to high. The encoding includes the following steps:

[0024] Step 1: According to the power value p to be expressed, determine the frequency segment number PS where it is located and obtain the quantized power unit QP PS , QP Ps =SP 2 ×10 -2 , where SP BP is the starting power of the power range;

[0025] Step 2: Quantize the power value, express the quantization result in hexadecimal and retain 4 significant digits. If it is less than 4 digits, add 0 in front to get the power quantization value p Q ;

[0026] Step 3: Combine the power segment code and the power quantization value, add the power information flag word 0x50, and get the final power code;

[0027] (5) Split and encode the modulation domain

[0028] The electromagnetic signal in space is a radar or communication signal. The signal is a continuous wave or a pulse sequence. The modulation methods are divided into analog modulation, 0 and digital modulation. Modulation usually transforms the three parameters of amplitude, phase and frequency. The modulation information coding is designed as follows: the first bit of the code distinguishes between radar and communication signals, the second bit distinguishes between continuous signals and pulse signals, the third bit distinguishes between analog modulation and digital modulation, and the fourth and fifth bits distinguish between frequency modulation, amplitude modulation, phase modulation and other types of modulation; bits 6 to 16 determine the specific modulation methods under different signal style categories;

[0029] (6) Split and encode the polarization domain

[0030] Polarization modes are divided into linear polarization, circular polarization and elliptical polarization, and circular polarization and elliptical polarization are further divided into left-hand circular polarization and right-hand circular polarization, left-hand elliptical polarization and right-hand elliptical polarization;

[0031] For linear polarization, the polarization angle must be determined to uniquely determine the polarization mode; for circular polarization and elliptical polarization, it is necessary not only to distinguish between left-hand and right-hand polarization, but also to quantify the polarization angle. A combination of classification and quantification can be used to express the polarization angle.

[0032] Therefore, a 2-bit binary code is placed at the beginning of the code to distinguish the polarization mode: linear polarization, left-hand circular polarization, right-hand circular polarization, 7-bit binary code determines the horizontal direction angle, 5-bit binary code determines the vertical direction angle, and they are spliced ​​in sequence, a total of 14-bit binary codes, which can uniquely determine the polarization mode. To facilitate computer storage, 2 bits are added to the 14-bit code and set to 00, a total of 16 bits constitute 2 bytes.

[0033] Preferably, the time domain association method is as follows:

[0034] Given a spatial position, for a specific frequency, the correlation of electromagnetic energy over a period of time is essentially a problem of calculating the mean value of electromagnetic energy during the observation period, which can be expressed as:

[0035]

[0036] Where T is the observation period; T Δ is the quantized time, N is the quantized value of T, is the average power during the observation period, p(t) is the instantaneous power distribution; Q (nT Δ ) is p(nT Δ )’s quantized value.

[0037] Preferably, the frequency domain association method is as follows:

[0038] Given a spatial position, at a specific time, the electromagnetic energy correlation within a certain frequency range is essentially a problem of calculating the mean electromagnetic energy within the observation frequency bandwidth, which can be expressed as:

[0039]

[0040] Where F is the observation frequency bandwidth; F Δ is the quantization frequency; M is the quantization value of F; is the average power within the observation frequency bandwidth F; p(f) is the power density distribution; p Q (mF Δ ) is p(mF Δ )’s quantized value.

[0041] Preferably, the airspace association method is as follows:

[0042] Given a frequency, at a specific time, the electromagnetic energy correlation within a certain spatial range is essentially a problem of calculating the mean value of the electromagnetic energy in the observation space, which can be expressed as:

[0043]

[0044] Where V is the observation space; V Δ is the quantization space; I, J, K are the quantization values ​​of V; is the average power in the observation space V; p(v) is the power spatial distribution; p Q (iV Δ , jV Δ , kV Δ ) is p(iV Δ , jV Δ , kV Δ )’s quantized value.

[0045] Preferably, in step 3, the radar coverage calculation process is as follows:

[0046] By using the three-dimensional data model, the radar detection range and the coverage range of the terrain data can be evenly divided and differentiated, and the complex calculation can be transformed into the intersection operation of the segmented volume element code set. The calculation process is as follows:

[0047] 1) Calculate the maximum radar detection range R without terrain influence max ;

[0048] In free space, when no environmental influence is considered, the radar is at a certain elevation angle θ and azimuth angle The detection range on is determined by the radar equation:

[0049]

[0050] Where P t is the transmission power, P r is the received power; F t is the pattern propagation factor from the transmitting antenna to the target; F r is the pattern propagation factor between the receiving antenna and the target; G t is the transmitting antenna power gain; G r is the receiving antenna power gain; σ is the cross-sectional area of ​​the radar target; λ is the radar wavelength; according to the basic radar parameters and radar equation, the radar is calculated at the pitch angle θ i (1≤i≤m, m is the number of pitch angle samples), azimuth The detection distance on (1≤j≤n, n is the azimuth sampling number), thereby obtaining the radar detection range;

[0051] 2) According to the principle of stereoscopic segmentation, select the appropriate segmentation level according to the required accuracy and convert the detection range into a code set of the same level.

[0052] 3) Load the terrain data near the detection range and convert the terrain data into a coded set

[0053] 4) Perform intersection calculation on the two code sets, where the intersection is the range blocked by the terrain

[0054] Beneficial effects of the present invention:

[0055] Based on the gridded electromagnetic environment data field, the present invention adopts a coded algebraic operation system to carry out two types of electromagnetic environment application research: electromagnetic radiation source propagation path calculation and electromagnetic information data three-dimensional presentation technology, thereby constructing an electromagnetic environment information gridded space system, greatly improving the calculation efficiency of the electromagnetic environment and enriching the expression of the electromagnetic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 To characterize the electromagnetic information space;

[0058] Figure 2 A schematic diagram of the GeoSOT time grid coding;

[0059] Figure 3 Segment and encode the frequency range in frequency segmentation encoding;

[0060] Figure 4 Segment and encode the power range in power segmentation encoding;

[0061] Figure 5 This is a schematic diagram of polarization angle division;

[0062] Figure 6 It is a logical schematic diagram for storing electromagnetic multi-domain information tensor;

[0063] Figure 7 It is a schematic diagram of multi-domain grid tensor storage of electromagnetic information;

[0064] Figure 8 Provides data visualization process for electromagnetic environment data field;

[0065] Fig. 9 Draw a framework for electromagnetic environment data fields. DETAILED DESCRIPTION

[0066] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0067] Research on the spatial representation characteristics and grid organization mechanism of electromagnetic environment information

[0068] The electromagnetic environment is the sum of the signal characteristics and signal density data of all frequency-using devices in the scope. It is one of the main media on which spatial information depends, and most information acquisition and transmission are completed in this field. With the continuous development and breakthrough of electronic technology, it has been widely used in electronic equipment. The data density of the electromagnetic environment is getting higher and higher, and the style is becoming more and more complex and changeable. The information space formed by it contains extremely complex data information.

[0069] For such information space, on the one hand, it is necessary to summarize and analyze the spectrum information existing in the space based on the research of electromagnetic wave distribution characteristics, and form a complete spectrum representation characteristic based on application needs, so that the continuous, silent and invisible electromagnetic environment data can be turned into a discrete and digital information space. On the other hand, it is necessary to study the multi-level organization of data in the complex electromagnetic environment data space based on the geographic information environment to form a data structure that is conducive to real-time calculation and analysis.

[0070] This embodiment specifically provides an electromagnetic environment information representation and organization method of an electromagnetic multi-domain grid model. Step 1: First, the GeoSOT-3D spatial grid partitioning framework is used to perform multi-scale division of the spatial domain information in the electromagnetic environment six-domain representation model. Then, the six-domain representation is discretized using the geographic space grid partitioning concept, and a corresponding encoding method is designed to form an electromagnetic multi-domain grid together with the spatial domain grid. A single multi-domain grid is used as a basic unit for organizing electromagnetic environment data.

[0071] Step 2: Based on the constructed electromagnetic multi-domain grid, design the corresponding electromagnetic information multi-domain grid storage structure as the physical layer implementation of data organization and storage;

[0072] Step 3: Based on the constructed electromagnetic multi-domain grid, a coded algebraic operation system is used to calculate the propagation path of the electromagnetic radiation source and the radar coverage range, and to express the calculated or measured electromagnetic data in true three-dimensional form with selectable scale.

[0073] In step 1, in the information society, since electronic information equipment is not only huge in number, complex in structure, diverse in type, but also widely used in frequency, the electromagnetic signals in space are very dense, forming an extremely complex electromagnetic environment. In a certain space, the complex electromagnetic environment is composed of a large number of electromagnetic signals with complex patterns, dense overlap, and dynamic overlap distributed in the time domain, frequency domain, space domain, energy domain, and modulation domain, thus presenting a variety of manifestations in different domains. Figure 1 shown.

[0074] The performance in the time domain is unpredictable and dynamic. There are only a small number of natural electromagnetic signals in the social environment, and a large number of electromagnetic signals are generated under human control. Therefore, at different times and locations, the number, type, and density of electromagnetic signals generated will change over time, and the way they change is difficult to effectively predict.

[0075] In the frequency domain, the spectrum is infinitely wide and dense. The spectrum is the manifestation of electromagnetic signals in the frequency domain. On the one hand, due to the rapid development of information technology and the extensive use of various frequency-using equipment, the spectrum occupied by electromagnetic signals is getting wider and wider, covering almost all electromagnetic signal frequency bands. On the other hand, due to the influence of factors such as atmospheric attenuation, ionospheric reflection and absorption, in the actual application process, the applicable spectrum range is limited, resulting in the phenomenon of dense overlap of electromagnetic signals in the local frequency range.

[0076] In the airspace, it is ubiquitous and widespread. Electromagnetic waves themselves have the characteristics of being widely distributed. In space, they are invisible and intangible, but they actually exist in every position in space, including land, sea, air, space and even cyberspace.

[0077] In the energy domain, the signal energy density is uneven. Affected by the electromagnetic wave propagation factors, the electromagnetic signal power will continue to decrease due to the increase in propagation distance, absorption and attenuation of air and moisture, and other unfavorable conditions. The terminal receiving antenna must have a sufficiently large directional gain and sensitivity to receive communication signals and perform subsequent signal processing. The entire earth is covered by electromagnetic signals. Communication signals, navigation signals, measurement and control signals and other signals of various styles and systems are intertwined, and the main lobe and side lobe beams of a pair of antennas make the energy density of the entire electromagnetic environment signal uneven and fluctuating.

[0078] In the modulation domain, the modulation system is complex. Traditional communication modulation mainly includes spread spectrum modulation technology and narrowband modulation technology, such as direct sequence spread spectrum (DSSS), frequency hopping (FHSS), QPSK, MSK and QAM. With the rapid development of science and technology, high-speed digital processing technology has brought new vitality to traditional voice coding, data coding and data transmission, and various new modulation technologies and multiple access technologies have been widely used in communications.

[0079] The time, frequency, space, energy and modulation domains reflect different characteristics of the electromagnetic environment respectively, and together constitute a complete electromagnetic environment. The time domain characteristics can reflect the number of radiation sources and the time of power on and off; the frequency domain characteristics can show the spectrum occupancy of the electromagnetic environment; the space domain characteristics reflect the spatial distribution of the radiation source; the energy domain characteristics reflect the change in the relative position of the radiation source and the receiver; the modulation domain reflects the modulation method of the electromagnetic signal.

[0080] Electromagnetic environment information can be described separately through multiple domains such as time domain, frequency domain, space domain and energy domain, but the information in these domains is continuous information, which is not conducive to computer storage and processing. Therefore, it is necessary to discretize and digitize the electromagnetic environment information represented by time domain, frequency domain, space domain and energy domain. This discretization and digitization can be achieved by segmenting and encoding the information.

[0081] The time domain, frequency domain, space domain, and energy domain data of the electromagnetic environment in each grid are discretized by encoding, and the multi-domain grid is used as the basic unit. The encoding process is as follows:

[0082] (1) The time domain segmentation and decoding process is as follows:

[0083] GeoSOT time grid coding is used to discretize time into time intervals of different lengths and assign unique binary codes, so that time is discretized into time periods with lengths, forming a time discrete framework in the organization of electromagnetic environment information; Figure 2 shown.

[0084] (2) The frequency domain is split and decoded, that is, the frequency split and decoding process is as follows:

[0085] Frequency decoding is based on segmented expression. The frequency range from 0.1Hz to 1PHz is divided into 16 segments in units of 10 octaves, and each frequency segment is encoded in order from low to high, such as Figure 3 As shown, the encoding includes the following steps (taking f=1.234567MHz as an example):

[0086] Step 1: According to the frequency value f=1.234567MHz to be expressed, determine the segment number FS=7, and get the quantization frequency QF FS , QF FS =SF 7 ×10 -7 =10 6 ×10 -7 =0.1, where SF 7 is the starting frequency of the frequency band;

[0087] Step 2: Quantize the frequency value, express the quantization result in hexadecimal and retain 7 valid digits. If it is less than 7 digits, add 0 in front to get the frequency quantization value

[0088]

[0089] Step 3: Combine the frequency band code and the frequency quantization value, add the frequency signal flag word 0x46, and finally get the frequency code of 1.234567MHz as 0x4670BC6146.

[0090] The above frequency encoding scheme has significant advantages:

[0091] 1) Regardless of the frequency, each frequency band has the same 10-fold bandwidth; 2) In the entire frequency domain, it has the same frequency expression accuracy (as shown in Table 1), that is, 10 -7 3) Ten-digit one-dimensional hexadecimal frequency encoding, simple conversion, high retrieval and association efficiency.

[0092] Table 1 Frequency division and segment coding

[0093]

[0094] (3) The spatial domain is divided and decoded, that is, the spatial grid coding process is as follows: the longitude and latitude of the earth are expanded three times, from 360°×180° to 512°×512°, 1° to 64', and 1' to 64″, and on this basis, 2n recursive division is implemented to obtain a 32-level longitude and latitude integer grid series with centimeter-level accuracy; each GeoSOT grid is assigned a unique code, and the grid code maintains the integer "degree, minute, second" dimension, and has four coding forms: quaternary 1D code, binary 1D code, binary 2D code, or decimal 2D code; a specific level in the GeoSOT grid is selected as the basic tuple, which can be aggregated to form any specified geographic grid based on longitude and latitude division, and the aggregated grid surface is automatically assigned a globally unique code.

[0095] (4) The energy domain is divided and decoded, that is, the power grid encoding process is:

[0096] Power coding is also based on segmented expression, that is, the power range from 1aW to 100MW is divided into 13 segments in units of 100 octaves, and each power segment is encoded in order from low to high (such as Figure 4 As shown), the encoding includes the following steps (taking p = 345nW as an example):

[0097] Step 1: According to the power value p to be expressed, determine the frequency segment number PS=5, and obtain the quantized power unit QP PS , QP Ps =SP 2 ×10 -2 =10 -8 ×10 -2 =10 -10 , where SP BP is the starting power of the power range;

[0098] Step 2: Quantify the power value, express the quantization result in hexadecimal and retain 4 significant digits. If there are less than 4 digits, add 0 in front to get the power quantization value

[0099] Step 3: Insert the power segment code 5 before the first digit of the power quantization value, and then insert the power flag word 0x50 before the power segment code. The final frequency code of 345nW is 0x5050D80.

[0100] The above power coding scheme has significant advantages:

[0101] 1) Regardless of the power size, each power range has the same 100 times power range width (as shown in Table 2);

[0102] 2) The power expression accuracy is better than 10-2 (better than 0.05dB) in the entire power domain;

[0103] 3) Seven-bit one-dimensional hexadecimal power encoding, simple conversion, high retrieval and association efficiency.

[0104] Table 2 Power segment division and segment coding

[0105]

[0106] (5) Split and encode the modulation domain

[0107] Communication signal modulation method:

[0108] Signal modulation methods include: AM, FM, ASK, FSK, PSK, BPSK, MSK, QAM, QPSK, QQPSK, CPFSK, SSB, USB, Mark X II, ModeS

[0109] Common modulation methods

[0110]

[0111] Radar signal pattern

[0112] Continuous wave signal, conventional pulse signal, pulse Doppler signal, pulse repetition frequency staggered signal, pulse repetition frequency jump signal, pulse repetition frequency sliding signal, pulse repetition frequency jitter signal, frequency agile signal, frequency diversity signal, pulse compression signal, pulse coding signal, etc.

[0113]

[0114] The main electromagnetic signals in space are radar or communication signals. The signals are continuous waves or impulse sequences. The modulation methods are divided into analog modulation, 0 and digital modulation, and modulation usually transforms the three parameters of amplitude, phase and frequency. Therefore, the following modulation information coding scheme can be designed: the first bit of the code distinguishes between radar and communication signals, the second bit distinguishes between continuous signals and pulse signals, the third bit distinguishes between analog modulation and digital modulation, the fourth and fifth bits distinguish between frequency modulation, amplitude modulation, phase modulation and other types of modulation; the sixth to the sixteenth bits determine the specific modulation methods under different signal style categories.

[0115]

[0116] (6) Split and encode the polarization domain

[0117] The law of how the electric field vector changes with time at any fixed point in space is called the polarization of the electromagnetic wave.

[0118] Polarization modes are divided into linear polarization, circular polarization and elliptical polarization, and circular polarization and elliptical polarization are further divided into left-hand circular polarization and right-hand circular polarization, left-hand elliptical polarization and right-hand elliptical polarization.

[0119] For linear polarization, the polarization angle needs to be determined in order to uniquely determine the polarization mode; for circular polarization and elliptical polarization, it is necessary not only to distinguish between left-hand and right-hand polarization, but also to quantify the polarization angle, so a combination of classification and quantitative expression can be used.

[0120] like Figure 5 As shown in the figure, the horizontal angle θ∈[0°~360°] and the vertical angle α∈[0°~90°] are defined to represent the polarization angle. Since the polarization angle affects the efficiency of the antenna energy reception, it is generally believed that the energy reception loss within 10% is acceptable, so the accuracy of the polarization angle Δ=3° (better than 3°) can be determined accordingly. That is, θ∈[0~120Δ], α∈[0~30Δ], which is convenient for binary division. Here, θ and α are expanded to 384° and 96° respectively, that is, 128Δ and 32Δ.

[0121] Therefore, a 2-bit binary code is placed at the beginning of the code to distinguish the polarization mode: linear polarization, left-hand circular polarization, right-hand circular polarization, 7-bit binary code determines the horizontal direction angle, 5-bit binary code determines the vertical direction angle, and they are spliced ​​in sequence, a total of 14-bit binary codes, which can uniquely determine the polarization mode. However, in order to facilitate computer storage, 2 bits are added to the 14-bit code (set to 00), a total of 16 bits to form 2 bytes.

[0122]

[0123] The basic characteristic of electromagnetic environment data is that it is constantly changing in the time domain, frequency domain and space domain. The three spaces are relatively independent. By associating the time domain, frequency domain and space domain, data association can be achieved in any time period, frequency band and space within the electromagnetic environment.

[0124] (1) Time domain correlation of electromagnetic information

[0125] Given a spatial position, for a specific frequency, the correlation of electromagnetic energy over a period of time is essentially a problem of calculating the mean value of electromagnetic energy during the observation period, which can be expressed as:

[0126]

[0127] Where T is the observation period; T Δ is the quantized time, N is the quantized value of T, is the average power during the observation period, p(t) is the instantaneous power distribution; Q (nT Δ ) is p(nT Δ )’s quantized value.

[0128] (2) Frequency correlation of electromagnetic information

[0129] Given a spatial position, at a specific time, the electromagnetic energy correlation within a certain frequency range is essentially a problem of calculating the mean electromagnetic energy within the observation frequency bandwidth, which can be expressed as:

[0130]

[0131] Where F is the observation frequency bandwidth; F Δ is the quantization frequency; M is the quantization value of F; is the average power within the observation frequency bandwidth F; p(f) is the power density distribution; p Q (mF Δ ) is p(mF Δ )’s quantized value.

[0132] (3) Spatial Correlation of Electromagnetic Information

[0133] Given a frequency, at a specific time, the electromagnetic energy correlation within a certain spatial range is essentially a problem of calculating the mean value of the electromagnetic energy in the observation space, which can be expressed as:

[0134]

[0135] Where V is the observation space; V Δ is the quantization space; I, J, K are the quantization values ​​of V; is the average power in the observation space V; p(v) is the power spatial distribution; p Q(iV Δ , jV Δ , kV Δ ) is p(iV Δ , jV Δ , kV Δ )’s quantized value.

[0136] The electromagnetic environment can be characterized by the characteristics of domains such as time domain, frequency domain, space domain, and energy domain. The N attribute information or features of these domains constitute an N-dimensional space. The electromagnetic environment data in each multi-domain grid becomes a point in the feature space. The electromagnetic environment data of thousands of multi-domain grids constitute an electromagnetic environment data field in the feature space.

[0137] Each data object in the electromagnetic environment data field contributes to the potential of any point in the field, and the size of the contribution is proportional to the mass of the object and inversely proportional to the square of the distance between the two. Assume that there are n attribute objects {x 1 , X 2 , ..., X n}, object x i The mass is m i , let x i represents the position of the object, then x in the data field i The potential function at x is:

[0138]

[0139] Among them, ||xx i || is x to x i σ is the impact factor, usually σ=2. x j In x i The potential energy in the data field for:

[0140]

[0141] The space Ω contains multiple objects, and all the objects work together to form the potential field of Ω. The potential at y in Ω is expressed as:

[0142]

[0143] According to the grid division idea, the electromagnetic space is divided into discrete volume data structures, and then the time domain, frequency domain, spatial domain, and energy domain data of the electromagnetic environment in each grid are discretized by encoding, and the multi-domain grid is used as the basic unit to solve the problem of the correlation between spatial information and electromagnetic attribute information, and then the three-dimensional presentation of multi-dimensional electromagnetic information data is realized based on the electromagnetic environment data field. The relationship between multi-domain data of the electromagnetic environment can also be studied with the help of data field theory.

[0144] The electromagnetic data field is a representation of electromagnetic data in different domains and dimensions. The electromagnetic environment data field data organization is based on the geographic data system, and studies the data model of how to effectively organize the electromagnetic environment data field data, so as to store, apply and analyze it scientifically and efficiently. Data models generally include raster data models and vector data models. The raster data model discretizes the electromagnetic space and covers the entire continuous space with multiple dimensions. The raster data model is the simplest and most intuitive data model, which divides the electromagnetic space into uniform grids, and each grid is a pixel.

[0145] The vector data model uses basic elements such as points, lines, and surfaces to accurately represent various geographic elements as much as possible by recording coordinate points. The coordinate space represented by vector data is continuous, so any position, length, area, etc. in the geographic space can be accurately defined. The accuracy of vector data is mainly limited by the accuracy of digital settings and the word length of numerical records. The data structure is based on geometric space coordinates and records the coordinates of sampling points, which can accurately identify the target. This data model can store complex data with minimal data redundancy. Compared with the raster structure, it has the characteristics of high data accuracy and small storage space. It is an efficient graphic data structure.

[0146] In the electromagnetic environment data field, electromagnetic waves themselves have vector characteristics, and the geographic environment needs to be processed in a rasterized manner, so its location data and attribute data are not consistent, so data organization needs to take both into account. There are file-relational database hybrid management, full-relational management, object-relational database management and other methods for such data.

[0147] In step 2, the multi-domain grid is used as the basic unit for organizing electromagnetic data, that is, the electromagnetic space is divided into discrete tensor data structures according to the multi-domain grid. Specifically, each type of electromagnetic data encoding addressing unit needs to correspond to the globally unique GeoSOT3D+ time code. The tensor encoding basic model structure designed by the present invention is shown in the table, that is, it consists of three parts: GeoSOT geographic code, time code, and electromagnetic feature code.

[0148] GeoSOT Geocoding Time Code Electromagnetic signature coding

[0149] Tensor Data Encoding Model

[0150] The electromagnetic tensor storage structure can be seen as a high-level extension of the GeoSOT partitioning framework. It is also a data model for local spatiotemporal ranges and specific attribute intervals. The tensor model has the characteristics of continuous indexing and high-level extension, so the data model is more suitable for the association and management of electromagnetic high-dimensional data, and also supports electromagnetic computing and information mining in the region, laying a good organizational foundation.

[0151] like Figure 6-7 As shown in the figure, if the data model is visualized as a system, it can be divided into the user layer, model layer, and application layer as shown in the figure. The user layer needs to determine the representation dimension, range, and level of the electromagnetic data, and disassemble and input the original data. At the same time, it can input operation instructions such as dimension indexing, attribute query, and block update; the model layer contains the GeoSOT tensor data model and the tensor encoding addressing auxiliary unit, which constructs the data model and associates the grid for the user's original data input. Each grid is associated with the corresponding encoding value and the corresponding electromagnetic attribute information; the application layer performs data operations according to user needs and instructions. Due to the regular organization of the GeoSOT tensor data model, the characteristics and principles of tensor analysis can be used to provide tensor analysis and operations of various data, thereby improving the calculation and use efficiency of data.

[0152] In step 3, the propagation path grid calculation: Combine the three-dimensional geographic information and building model of the spatial area to deduce various paths of electromagnetic propagation under the environmental conditions of the monitored area, and calculate the multipath and obstruction caused by terrain and buildings. In order to ensure the efficiency and real-time performance of the calculation, GPU parallel calculation is used to calculate the propagation paths in all directions of the radiation source at the same time. In the process of path deduction, the model is organized according to the three-dimensional grid coding data, and any entity in the electromagnetic space is customized according to actual needs. Then the path is directly derived according to the continuous coding calculation method.

[0153] The radar coverage calculation process is as follows:

[0154] By using the three-dimensional data model, the radar detection range and the coverage range of the terrain data can be evenly divided and differentiated, and the complex calculation can be transformed into the intersection operation of the segmented volume element code set. The calculation process is as follows:

[0155] 1) Calculate the maximum radar detection range R without terrain influence max ;

[0156] In free space, when no environmental influence is considered, the radar is at a certain elevation angle θ and azimuth angle The detection range on is determined by the radar equation:

[0157]

[0158] Where P t is the transmission power, P r is the received power; F t is the pattern propagation factor from the transmitting antenna to the target; F r is the pattern propagation factor between the receiving antenna and the target; G t is the transmitting antenna power gain; G ris the receiving antenna power gain; σ is the cross-sectional area of ​​the radar target; λ is the radar wavelength; according to the basic radar parameters and radar equation, the radar is calculated at the pitch angle θ i (1≤i≤m, m is the number of pitch angle samples), azimuth The detection distance on (1≤j≤n, n is the azimuth sampling number), thereby obtaining the radar detection range;

[0159] 2) According to the principle of stereoscopic segmentation, select the appropriate segmentation level according to the required accuracy and convert the detection range into a code set of the same level.

[0160] 3) Load the terrain data near the detection range and convert the terrain data into a coded set

[0161] 4) Perform intersection calculation on the two code sets, where the intersection is the range blocked by the terrain

[0162] The above processing algorithm does not need to use the ray method to calculate the intersection of rays and surfaces in three-dimensional space, that is, it omits the step of simultaneous vector equations. The algorithm itself is only related to the actual radar parameters in the first step of calculating the detection range. In the subsequent process, all calculations of the subdivided volume element coding are used. The coding calculation is a bit operation in the computer, and the algorithm complexity is o(NlogN). When the basic parameters of the radar change dynamically, only the R in the first step needs to be changed. max That’s it, the calculation is convenient.

[0163] Three-dimensional presentation technology of multi-dimensional electromagnetic information data based on electromagnetic environment data field

[0164] The three-dimensional visualization technology of multi-dimensional electromagnetic information data is to map the multi-domain information of the electromagnetic environment based on visualization, form a visualized data field, and draw it on the screen in a scientific and intuitive way, so as to present the spatial characteristics of the electromagnetic environment. Its core lies in the drawing of electromagnetic environment data field data. The electromagnetic environment data field drawing converts redundant data into intuitive graphics or images by establishing a mapping relationship (transfer function) between discrete point data in the electromagnetic environment data field and three-dimensional geometric figures. The characteristics of electromagnetic environment data field data are that there are many types of signals, fuzzy distribution boundaries, and complex internal details. It is necessary to describe the specific attribute information of the electromagnetic environment and to show the overall characteristics and internal details of the electromagnetic environment space. The electromagnetic environment data field drawing technology can just show the overall picture of the electromagnetic environment from multiple dimensions to meet different visualization needs.

[0165] This technology is based on the construction of the electromagnetic environment data field, and uses drawing algorithms and combines three-dimensional geographic information to smoothly draw and render electromagnetic data in real time. It provides electromagnetic situation presentation forms with multi-resolution levels, multi-mode expressions, multi-perspective integration and multi-data fusion for dynamic and static radiation source entities, equipment distribution, electromagnetic situation and other information.

[0166] Multidimensional electromagnetic data visualization technology mainly solves three problems: the construction of electromagnetic environment data field, three-dimensional visualization mapping, and the drawing and interactive display of the mapping process. The entire electromagnetic environment data field data visualization process is as follows: Figure 8 shown.

[0167] The visualization of electromagnetic environment data field is the core of the whole process. It solves how to convert the discrete point data in the electromagnetic environment data field into three-dimensional geometric figures that can be drawn. The meaning of "mapping" includes the selection and design of visualization schemes and visualization areas, that is, deciding what should be seen in the final image, how to use shape, brightness, color and other attributes to express the properties and characteristics of interest in the original data, and using the most effective graphical representation to explain the various phenomena and laws hidden in the data.

[0168] Visual mapping includes two aspects: one is the design of the visualization scheme, which determines what should be seen in the final presentation interface, as well as the choice of shape, brightness, color and transparency; the other is graphic plotting, which converts data and attributes into images that can be displayed, including observation transformation, scan conversion, geometric transformation of images, color quantization, dynamic plotting of images, etc.

[0169] The electromagnetic environment data field rendering technology is used to convert discrete point data in the electromagnetic environment data field into visual graphics. The visualization scheme architecture is as follows: Fig. 9 shown.

[0170] Optical visualization mapping maps electromagnetic environment data field values ​​into different optical property parameters, and direct volume rendering and surface rendering algorithms convert geometric figures and optical properties into graphic images that can be displayed by hardware devices. Hybrid rendering refers to the use of surface rendering and volume rendering in a virtual geographic environment according to visualization requirements to obtain the desired display effect.

[0171] The above description is only used to illustrate the technical solution of the present invention rather than to limit it. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in this field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for representing and organizing electromagnetic environment information of an electromagnetic multi-domain grid model, characterized in that: The following steps are involved: Step 1: First, use the GeoSOT-3D spatial grid partitioning framework to perform multi-scale division of the airspace information in the six-domain representation model of the electromagnetic environment. Then, use the geographic space grid partitioning concept to discretize the six-domain representation and design the corresponding encoding method. Together with the airspace grid, it constitutes an electromagnetic multi-domain grid. A single multi-domain grid is used as the basic unit for organizing electromagnetic environment data. Step 2: Based on the constructed electromagnetic multi-domain grid, design the corresponding electromagnetic information multi-domain grid storage structure as the physical layer implementation of data organization and storage; Step 3: Based on the constructed electromagnetic multi-domain grid, a coded algebraic operation system is used to calculate the propagation path of the electromagnetic radiation source and the radar coverage range, and to express the calculated or measured electromagnetic data in true three-dimensional form with selectable scale.

2. The electromagnetic environment information characterization and organization method of the electromagnetic multi-domain grid model according to claim 1 is characterized in that: In step 1, the six-domain representation includes time domain, frequency domain, space domain, energy domain, polarization domain and modulation domain representation, and the basic feature of the electromagnetic environment data is that it is constantly changing in the time domain, frequency domain and space domain. The three spaces of time domain, frequency domain and space domain are relatively independent. By associating the time domain, frequency domain and space domain, data association of any time period, any frequency band and any space in the electromagnetic environment can be achieved.

3. The electromagnetic environment information characterization and organization method of the electromagnetic multi-domain grid model according to claim 2 is characterized in that: In step 1, the time domain, frequency domain, spatial domain, energy domain, polarization domain, and modulation domain data of the electromagnetic environment in each grid are discretized by encoding, and the multi-domain grid is used as the basic unit. The encoding process is as follows: (1) The time domain segmentation and encoding process is as follows: GeoSOT time grid coding is used to discretize time into time intervals of different lengths and assign unique binary codes, so that time is discretized into time periods with lengths, forming a time discrete framework in the organization of electromagnetic environment information; (2) The frequency domain is split and encoded, that is, the frequency split encoding process is as follows: Frequency coding is based on segmented expression. The frequency range from 0.1Hz to 1PHz is divided into 16 segments in units of 10 octaves, and each frequency segment is encoded in order from low to high. The encoding includes the following steps: Step 1: According to the frequency value f to be expressed, determine the segment number FS where it is located and obtain the quantization frequency QF FS , QF FS =SF7×10 -7 , where SF7 is the starting frequency of the frequency band; Step 2: Quantize the frequency value, express the quantization result in hexadecimal and retain 7 valid digits. If it is less than 7 digits, add 0 in front to get the frequency quantization value f Q ; Step 3: Combine the frequency band code and the frequency quantization value, add the frequency signal flag word 0x46, and get the final frequency code; (3) The spatial domain is divided and encoded, that is, the spatial grid encoding process is as follows: the longitude and latitude of the earth are expanded three times, from 360°×180° to 512°×512°, 1° to 64', and 1' to 64″. On this basis, 2n recursive division is implemented to obtain a 32-level longitude and latitude integer grid series with centimeter-level accuracy; (4) The energy domain is divided and encoded, that is, the power grid encoding process is: Power coding is also based on segmented expression, that is, the power range of 1aW to 100MW is divided into 13 segments in units of 100 times the frequency band, and each power segment is encoded in order from low to high. The encoding includes the following steps: Step 1: According to the power value p to be expressed, determine the frequency segment number PS where it is located and obtain the quantized power unit QP PS , QP Ps =SP2×10 -2 , where SP BP is the starting power of the power range; Step 2: Quantize the power value, express the quantization result in hexadecimal and retain 4 significant digits. If it is less than 4 digits, add 0 in front to get the power quantization value p Q ; Step 3: Combine the power segment code and the power quantization value, add the power information flag word 0X50, and get the final power code; (5) Split and encode the modulation domain The electromagnetic signal in space is a radar or communication signal. The signal is a continuous wave or a pulse sequence. The modulation methods are divided into analog modulation, 0 and digital modulation. Modulation usually transforms the three parameters of amplitude, phase and frequency. The modulation information coding is designed as follows: the first bit of the code distinguishes between radar and communication signals, the second bit distinguishes between continuous signals and pulse signals, the third bit distinguishes between analog modulation and digital modulation, and the fourth and fifth bits distinguish between frequency modulation, amplitude modulation, phase modulation and other types of modulation; bits 6 to 16 determine the specific modulation methods under different signal style categories; (6) Split and encode the polarization domain Polarization modes are divided into linear polarization, circular polarization and elliptical polarization, and circular polarization and elliptical polarization are further divided into left-hand circular polarization and right-hand circular polarization, left-hand elliptical polarization and right-hand elliptical polarization; For linear polarization, the polarization angle must be determined to uniquely determine the polarization mode; for circular polarization and elliptical polarization, it is necessary not only to distinguish between left-hand and right-hand polarization, but also to quantify the polarization angle, which can be expressed by combining classification and quantification. Therefore, a 2-bit binary code is placed at the beginning of the code to distinguish the polarization mode: linear polarization, left-hand circular polarization, right-hand circular polarization, 7-bit binary code determines the horizontal direction angle, 5-bit binary code determines the vertical direction angle, and they are spliced ​​in sequence, a total of 14-bit binary codes, which can uniquely determine the polarization mode. To facilitate computer storage, 2 bits are added to the 14-bit code and set to 00, a total of 16 bits constitute 2 bytes.

4. The method for representing and organizing electromagnetic environment information of an electromagnetic multi-domain grid model according to claim 3, characterized in that: The time domain correlation method is as follows: Given a spatial position, for a specific frequency, the correlation of electromagnetic energy over a period of time is essentially a problem of calculating the mean value of electromagnetic energy during the observation period, which can be expressed as: Where T is the observation period; T Δ is the quantized time, N is the quantized value of T, is the average power during the observation period, p(t) is the instantaneous power distribution; Q (nT Δ ) is p(nT Δ )’s quantized value.

5. The electromagnetic environment information characterization and organization method of the electromagnetic multi-domain grid model according to claim 3 is characterized in that: The frequency domain correlation method is as follows: Given a spatial position, at a specific time, the electromagnetic energy correlation within a certain frequency range is essentially a problem of calculating the mean electromagnetic energy within the observation frequency bandwidth, which can be expressed as: Where F is the observation frequency bandwidth; F Δ is the quantization frequency; M is the quantization value of F; is the average power within the observation frequency bandwidth F; p(f) is the power density distribution; p Q (mF Δ ) is p(mF Δ )’s quantized value.

6. The electromagnetic environment information characterization and organization method of the electromagnetic multi-domain grid model according to claim 3 is characterized in that: The airspace association is as follows: Given a frequency, at a specific time, the electromagnetic energy correlation within a certain spatial range is essentially a problem of calculating the mean value of the electromagnetic energy in the observation space, which can be expressed as: Where V is the observation space; V Δ is the quantization space; I, J, K are the quantization values ​​of V; is the average power in the observation space V; p(v) is the power spatial distribution; p Q (iV Δ , jV Δ , kV Δ ) is p(iV Δ , jV Δ , kV Δ )’s quantized value.

7. The electromagnetic environment information characterization and organization method of the electromagnetic multi-domain grid model according to claim 1 is characterized in that: In step 3, the radar coverage calculation process is as follows: By using the three-dimensional data model, the radar detection range and the coverage range of the terrain data can be evenly divided and differentiated, and the complex calculation can be transformed into the intersection operation of the segmented volume element code set. The calculation process is as follows: 1) Calculate the maximum radar detection range R without terrain influence max ; In free space, when no environmental influence is considered, the radar is at a certain elevation angle θ and azimuth angle The detection range is determined by the radar equation: Where P t is the transmitting power; P r is the received power; F t is the pattern propagation factor from the transmitting antenna to the target; F r is the pattern propagation factor between the receiving antenna and the target; G t is the transmitting antenna power gain; G r is the receiving antenna power gain; σ is the cross-sectional area of ​​the radar target; λ is the radar wavelength; according to the basic radar parameters and radar equation, the radar is calculated at the pitch angle θ i , 1≤i≤m, m is the number of pitch angle samples, azimuth angle n is the detection distance based on the azimuth sampling number, from which the radar detection range is obtained; 2) According to the principle of stereoscopic segmentation, select the appropriate segmentation level according to the required accuracy and convert the detection range into a code set of the same level. 3) Load the terrain data near the detection range and convert the terrain data into a coded set 4) Perform intersection calculation on the two code sets, where the intersection is the range blocked by the terrain

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