CN117914434A - Channel attenuation compensation method and device for marine rescue communication channel attenuation model - Google Patents
Channel attenuation compensation method and device for marine rescue communication channel attenuation model Download PDFInfo
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Abstract
The invention provides a channel attenuation compensation method and device of an offshore rescue communication channel attenuation model, and relates to the technical field of offshore wireless communication, wherein the method comprises the following steps: acquiring a plurality of marine weather environment attenuation characteristics according to a marine weather environment data set acquired by a command center, and constructing a weather environment attenuation model corresponding to the marine weather environment attenuation characteristics; constructing an initial double-ray channel model based on a rough sea surface reflection function, a sea surface shadow effect function, a complex baseband received signal function and a multipath fading signal-to-noise ratio function; solving a plurality of initial double-ray channel models based on an artificial bee colony algorithm to obtain a double-ray channel model; and simulating the communication process of the rescue ship in real time according to the marine comprehensive meteorological environment attenuation model and the double-ray channel model so as to compensate the attenuation channel of the rescue ship in real time. The invention is beneficial to improving the transmission precision of the sea area wireless communication channel network.
Description
Technical Field
The invention relates to the technical field of offshore wireless communication, in particular to a channel attenuation compensation method and device for an offshore rescue communication channel attenuation model.
Background
With the continuous development and development of ocean space and resources, the quantity of maritime trade traffic and offshore operation platforms, facilities and the like is continuously increased, however, the ocean water area is wide, the environment is complex, the moving range of a ship during maritime navigation is wide, the natural conditions clearly increase the difficulty of maritime communication, and particularly when the ship or the facility encounters an emergency condition, the difficulty of communication for transmitting communication demands to a ground satellite base station by a ship satellite terminal is increased, and the stability and the high efficiency of communication are difficult to ensure.
The Chinese patent with publication number CN108540248B discloses a dynamic multipath channel model for marine wireless communication, a method and a wireless communication system, and provides a sea wave height prediction model; calculating the effective antenna height; calculating the field intensity at a receiving end on the direct-view path; calculating the field intensity at a receiving end on a specular reflection path; calculating the field intensity at a receiving end on the diffuse reflection path; calculating the time delay on the specular reflection path; calculating the time delay on the diffuse reflection path; a dynamic channel model is added. However, the sea area channel model constructed by the scheme does not fully consider the sea surface propagation environment, so that the transmission precision of the sea area wireless communication channel network is poor. Therefore, it is very necessary to provide a channel attenuation compensation method and device for an offshore rescue communication channel attenuation model to improve the transmission accuracy of a wireless communication channel network in the sea.
Disclosure of Invention
In view of the above, the invention provides a channel attenuation compensation method and device for an offshore rescue communication channel attenuation model. The weather factor on the sea is corrected in real time by constructing a meteorological environment attenuation model, meanwhile, sea reflection characteristics, shadow effect and multipath channel signal-to-noise ratio attenuation are introduced into a double-ray channel model, and the double-ray channel model is optimized by an artificial bee colony algorithm so as to realize real-time compensation of an attenuation channel, thereby improving the transmission precision of a sea area wireless communication channel network.
The invention provides a channel attenuation compensation method of an offshore rescue communication channel attenuation model, which comprises the following steps:
Acquiring a plurality of marine weather environment attenuation characteristics according to a marine weather environment data set acquired by a command center, and constructing a weather environment attenuation model corresponding to the marine weather environment attenuation characteristics;
Constructing an initial double-ray channel model based on a rough sea surface reflection function, a sea surface shadow effect function, a complex baseband received signal function and a multipath fading signal-to-noise ratio function;
Solving a plurality of initial double-ray channel models based on an artificial bee colony algorithm to obtain a double-ray channel model;
And simulating the communication process of the rescue ship in real time according to the meteorological environment attenuation model and the double-ray channel model so as to compensate the attenuation channel of the rescue ship in real time.
On the basis of the above technical solution, it is preferable to construct an initial dual-ray channel model based on a rough sea surface reflection function, a sea surface shadow effect function, a complex baseband received signal function, and a multipath fading signal-to-noise ratio function, which specifically includes:
Constructing an offshore rescue communication channel attenuation function based on the rough sea surface reflection function and the sea surface shadow effect function;
constructing a multipath channel error rate progressive function based on the complex baseband received signal function and the multipath fading signal-to-noise ratio function;
And constructing the initial double-ray channel model according to the attenuation function of the marine rescue communication channel and the error rate progressive function of the multipath channel.
On the basis of the technical scheme, preferably, the rough sea surface reflection function and the sea surface shadow effect function specifically comprise:
Wherein epsilon r represents the effective reflection coefficient of rough sea surface, epsilon represents the effective reflection coefficient of the sea surface, sigma h represents the standard deviation value of rough sea surface height distribution, theta i represents the incident angle of electromagnetic waves emitted by a command center antenna, lambda represents the wavelength of the electromagnetic waves emitted by the command center, S LOS represents the sea shadow effect function, lambda represents the constant function, erfc () represents the complementary error function, and gamma represents the root mean square surface slope of the electromagnetic waves emitted by the command center.
Still further preferably, the constructing an attenuation function of the rescue communication channel at sea specifically includes:
η＝|1+SLOS·G·εr·exp(jkDf)|
Wherein D L represents a line-of-sight propagation distance of the rescue vessel from the command center, D f represents a path length difference between D L and sea surface reflection, R represents an earth radius, h 1 represents a height difference between the rescue vessel and sea surface, h 2 represents a height difference between the command center and sea surface, j represents an imaginary number, k represents a propagation constant, ψ represents a model loss factor, G represents an influence factor, α represents a first reference angle, β represents a second reference angle, η represents a circular earth loss function, P de represents an offshore rescue communication channel attenuation function, and L f represents a total diffraction loss of a direct path and a reflection path.
Still further preferably, the complex baseband received signal function specifically includes:
Where BD denotes a complex baseband received signal function, ρ denotes a unit mean gamma distribution random variable, V nexp(jΦn) denotes an nth specular marine reflection component, V n denotes an amplitude of the nth specular marine reflection component, Φ n denotes a random phase of the nth specular marine reflection component, and x+ jY denotes a complex gaussian random variable.
Still further preferably, the multipath channel error rate progressive function specifically includes:
Wherein ω represents the ratio of the average power of the main component of the electromagnetic wave transmitted by the command center antenna to the diffuse reflection multipath power of the remaining components, σ 2 represents the variance in the complex gaussian random variable, Δ represents the degree of similarity of the average received powers of the multiple specular reflection components to each other, |m χ(s) | represents the absolute value of the moment generating function of the signal-to-noise ratio χ received by the rescue vessel, M represents the decay function of the fluctuating dual-path model, P μ () represents the legendre function of order μ, Representing the average signal-to-noise ratio received by the rescue vessel, s representing a polynomial composed of the decay function of the wave-motion dual-path model, o () representing the peano remainder, andRepresenting the multipath channel error rate progression function, α r and β r represent the modulation constants, and N represents the maximum number of modulation constants.
Still further preferably, the artificial bee colony algorithm is used for solving a plurality of initial dual-ray channel models to obtain dual-ray channel models, and the method comprises the following steps:
setting related parameters of an artificial bee colony algorithm and randomly generating an initial population, wherein the related parameters comprise population scale, maximum iteration times and search precision, and an initial honey source is randomly generated in a search space;
Leading the bee to expand a random search with the neighborhood of the corresponding initial honey source, calculating the fitness value of the initial honey source after searching, and determining whether to reserve the initial honey source through a greedy selection mechanism;
If the initial honey source is not reserved, acquiring a first honey source corresponding to the leading bee based on the position of the initial honey source and a honey source updating algorithm;
After all the leading bees and the following bees finish the search task, judging whether the iteration times of the artificial bee colony algorithm are larger than the maximum iteration times;
if the iteration number of the artificial bee colony algorithm is larger than the maximum iteration number, converting the leading bee into a detection bee, randomly searching a second honey source in the search space by the detection bee through a chaos search strategy, and taking the second honey source as a double-ray channel model.
Still further preferably, the method solves a plurality of initial dual-ray channel models based on an artificial bee colony algorithm to obtain a dual-ray channel model, and specifically includes:
wherein, Representing the initial position of honey source i,Represents the position near the honey source i when honey is picked for the t time, D max represents the upper limit of D-dimensional search space, D min represents a lower bound for the D-dimensional search space, and d= (1, 2, (S.) D)Represents the position close to the honey source i in d-dimensional space when honey is collected for the t time, rand (0, 1) represents selecting one random number in [0,1 ]Represents a new honey source generated near honey source i in d-dimensional space at the time of honey collection at the t-th time,Representing the position of leading bees near honey source i in d-dimensional space when honey is collected for the t time,Representing the position of leading bees near honey source j in d-dimensional space when honey is collected for the t time,Representing a random number in 0, 1.
Still further preferably, the detecting bees randomly search the second honey source in the search space through a chaos search strategy, and specifically includes:
wherein, Representing new honey sources generated after adding chaotic search,Representing the position of leading bees near honey source i in d-dimensional space when honey is collected for the t time,Representing the position of the leading bee near the honey source j in d-dimensional space at the time of honey collection at the t-th time, ceil () represents an upward rounding function, rand (0, 1) represents a random number in the choice [0,1], cir represents a Circle mapping factor, mod () represents a solution Yu Hanshu, and random numbers a and b represent a random number in the choice [0,1 ].
In a second aspect of the application, a channel attenuation compensation device for an offshore rescue communication channel attenuation model is provided, the channel attenuation compensation device comprising an attenuation model construction module, a channel model construction module, a swarm algorithm optimization module and an attenuation channel compensation module, wherein,
The attenuation model construction module is used for acquiring a plurality of attenuation characteristics of the marine meteorological environment according to the marine meteorological environment data set acquired by the command center and constructing a meteorological environment attenuation model corresponding to the attenuation characteristics of the marine meteorological environment;
The channel model construction module is used for constructing an initial double-ray channel model based on a rough sea surface reflection function, a sea surface shadow effect function, a complex baseband received signal function and a multipath fading signal-to-noise ratio function;
The bee colony algorithm optimization module is used for solving a plurality of initial double-ray channel models based on an artificial bee colony algorithm so as to obtain a double-ray channel model;
the attenuation channel compensation module is used for simulating the communication process of the rescue ship in real time according to the meteorological environment attenuation model and the double-ray channel model so as to compensate the attenuation channel of the rescue ship in real time.
Compared with the prior art, the channel attenuation compensation method and device of the marine rescue communication channel attenuation model have the following beneficial effects:
(1) The method comprises the steps of constructing a meteorological environment attenuation model to correct marine weather factors in real time, introducing the acquired sea surface reflection characteristics, shadow effect and multipath channel signal-to-noise ratio attenuation into a double-ray channel model, performing real-time communication modulation through a complex baseband receiving signal, and finally optimizing the double-ray channel model through a manual bee colony algorithm to realize real-time compensation of an attenuation channel, thereby improving the transmission precision of a sea area wireless communication channel network;
(2) The initialization strategy combining homogenization and randomization is used for generating various initial honey sources, a foundation is laid for subsequent searching, the collaborative searching strategy regulated by the leading bee guidance and greedy selection mechanism is used for searching the optimal solution, global and local searching capability is effectively balanced, searching time is reduced, searching precision is improved, meanwhile, the leading bee is converted into the chaotic searching strategy after the detection bee to effectively help the algorithm to escape the local optimal value, and the chaotic searching strategy has higher tracking precision and faster tracking speed and less power fluctuation under static and dynamic environments.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of steps of a channel attenuation compensation method for an offshore rescue communication channel attenuation model provided by the invention;
FIG. 2 is a circular earth geometry model based on the dual ray method provided by the invention;
FIG. 3 is a schematic diagram of a over the surface LOS radio link on a smooth earth surface provided by the present invention;
FIG. 4 is a schematic diagram of a channel attenuation compensation device according to the present invention;
Fig. 5 is a schematic structural diagram of an electronic device provided by the present invention.
Reference numerals illustrate: 1. channel attenuation compensation means; 11. an attenuation model construction module; 12. a channel model construction module; 13. a bee colony algorithm optimization module; 14. an attenuation channel compensation module; 2. an electronic device; 21. a processor; 22. a communication bus; 23. a user interface; 24. a network interface; 25. a memory.
Detailed Description
The following description of the embodiments of the present invention will clearly and fully describe the technical aspects of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
The embodiment of the application discloses a channel attenuation compensation method of an offshore rescue communication channel attenuation model, which comprises the steps of S1-S4 as shown in figure 1.
Step S1, acquiring a plurality of marine weather environment attenuation characteristics according to a marine weather environment data set acquired by a command center, and constructing a weather environment attenuation model corresponding to the marine weather environment attenuation characteristics.
In this step, the marine meteorological environment data set typically includes marine meteorological observation data, marine meteorological model output data, and other relevant marine environment data. The marine meteorological observation data comprise observation data of marine meteorological elements such as sea surface wind speed and direction, sea wave height and direction, ocean surface temperature, ocean tides, ocean air pressure and the like; the output data of the marine meteorological model is output data of marine meteorological elements which are obtained by simulation of the marine meteorological model (such as a WRF model, a SWAN model and the like) generally, and the output data comprise a sea surface wind field, a sea wave field, a marine surface temperature field and the like; other marine environmental data include marine hydrologic data, marine biological data, marine pollution data, and the like, related to marine environment.
The above mentioned marine meteorological environment data set can be used for inquiring marine environment monitoring reports such as sea waves, tides, water temperatures and the like through official websites and databases of various countries or various regions; the system can also collect by utilizing a satellite remote sensing technology, can receive and transmit information of various natural phenomena on the earth in real time through a sensor carried by a satellite, such as ice thickness and the like of a water color water temperature meter buoy drifting thermometer ice layer coverage area calculation method, and reflects obtained data in an image form; or acquire data resources by participating in international collaborative projects, such as Ocean Data Exchange (ODEx) to acquire experimental results and data analysis in the field of marine research, or World OceanAtlas (WOA) to acquire a large amount of marine observations; and can also be searched through an online database, such as ESG-Net search or query of a large amount of various observation data closely related to marine ecological protection.
The attenuation characteristic of the marine meteorological environment refers to attenuation conditions of signal transmission such as electromagnetic waves or sound waves and the like of complex weather conditions corresponding to a sea area in the marine meteorological environment data set. For electromagnetic waves, the attenuation characteristics of the marine weather environment generally relate to the effects of attenuation, scattering, refraction, etc., of electromagnetic waves as they propagate in the atmosphere and in the seawater medium. For example, rain, fog, sea waves, etc. in an oceanographic environment attenuate the propagation of electromagnetic waves, which may vary with frequency, i.e., the dispersion characteristics. In addition, ionospheric effects in the ocean, reflection and diffraction from the ocean surface, and the like are also factors that affect the attenuation of electromagnetic wave propagation. For sound waves, the attenuation characteristics of the marine weather environment refer to the attenuation, scattering, absorption and other influences of the sound waves when the sound waves propagate in the sea water. The factors such as the temperature, the salinity and the pressure of the sea can influence the propagation speed and the attenuation condition of sound waves in the sea, and the submarine topography, the marine organisms and the like in the sea can also influence the propagation of the sound waves.
In one example, comprehensive weather, fog, rainfall, snowfall and mixed marine weather environment attenuation characteristics are combined, attenuation models of the weather, the weather is respectively built, the comprehensive weather environment attenuation models are substituted, a marine weather data simulator is built, different marine weather is simulated based on time triggering, weather environment attenuation changes of different sea condition weather are simulated, the probability of weather, rain, snow and fog and different weather in the sea are counted according to historical weather data of a target sea area, a random weather environment simulator is built, simulated marine weather is generated and enters a weather queue, when simulation test is executed, a time trigger is set, the weather is read from the weather queue at intervals of a set time threshold value, the weather mark i is substituted into the attenuation model according to weather mark i of the weather, and the weather environment attenuation model is generated;
Based on meteorological environment data of a certain sea area and a certain year disclosed by the national meteorological bureau, constructing a meteorological environment attenuation model simulation experiment environment, and analyzing the fitting precision of the model in two aspects of large-scale attenuation and small-scale attenuation; studying the adaptability of the model with Rician, nakagami, rayleigh, weibull different attenuation distributions; and setting an offshore meteorological data simulator, and generating weather, rain, snow and fog to simulate the performance of different meteorological environment attenuation models.
The higher the radio wave propagation frequency is, the higher the radio wave transmission energy is, and the signal fading is more easily caused by the absorption of offshore atmosphere, rain, snow, fog and the like, and the weather environment fading is in direct proportion to the frequency factor; the communication frequency band is interfered by solar electromagnetic radiation and shows a day-night period change; the sunlight irradiates to make the temperature higher, so that more seawater is evaporated to cause radio wave refraction loss, and the fading strength in the daytime is higher than that in the night. The marine meteorological environment builds a fading coefficient model thereof as follows:
Wherein C represents a sunny day attenuation coefficient, f w represents the working frequency of the antenna of the command center, The solar cycle period is represented by θ, the phase shift value is represented by U, the mist density is represented by F, the mist attenuation coefficient is represented by I, the rainfall intensity is represented by δ, the rainfall attenuation coefficient is represented by Γ, the shake amplitude is represented by V, the snowfall intensity is represented by S, and the snowfall attenuation coefficient is represented by S. And X (t) is the fading coefficient of different marine weather environments, i is a weather type identifier, and i=0, 1,2,3 and 4 respectively represent weather environments mixed with weather, fog, rain, snow and rain and snow at present, so that a corresponding weather fading coefficient model is constructed.
When a rain and snow mixed environment exists, the influence of rain and snow attenuation is not average, but the main meteorological environment attenuation influence is taken as the dominant influence, and the coefficient mean square value is adopted for estimation.
And S2, constructing an initial dual-ray channel model based on the rough sea surface reflection function, the sea surface shadow effect function, the complex baseband received signal function and the multipath fading signal-to-noise ratio function.
In the step, a command center antenna is used as a transmitter, and a wireless communication terminal on the rescue ship is used as a receiving end for wireless communication, namely, the command center and the rescue ship are used as sight propagation.
In this step, step S2 further includes steps S21 to S23.
And S21, constructing an offshore rescue communication channel attenuation function based on the rough sea surface reflection function and the sea surface shadow effect function.
Referring to fig. 2, the earth cannot be considered as a "plane", and therefore a circular geometric model based on the dual-ray method is proposed, the earth radius (6371 km) is denoted by R, and D 1+D2 is defined as the projection of the TX-RX distance onto the earth surface. Parameters h 1 and h 2 represent the heights of the command center transmit antenna and rescue vessel receive antenna, respectively.
Wherein D L represents the line-of-sight propagation distance of the rescue vessel from the command center, D f represents the path length difference between D L and sea surface reflection, R represents the earth radius, h 1 represents the height difference between the rescue vessel and sea level, h 2 represents the height difference between the command center and sea level, and ψ represents the model loss factor.
In this step, the rough sea surface reflection function and the sea surface shadow effect function specifically include:
Wherein epsilon r represents the effective reflection coefficient of rough sea surface, epsilon represents the effective reflection coefficient of the sea surface, sigma h represents the standard deviation value of the height distribution of rough sea surface, theta i represents the incident angle of electromagnetic waves emitted by a command center antenna, lambda represents the wavelength of electromagnetic waves emitted by the command center, S LOS represents the sea shadow effect function, lambda represents the constant function, erfc () represents the complementary error function, and gamma represents the root mean square surface slope of electromagnetic waves emitted by the command center.
Due to the effects of earth curvature, the power density of the reflected light varies with the TX-RX distance (beyond the usual "tapering" phenomenon in free space). In long-range communications, including marine communications, the divergence effect should be considered when constructing the initial dual-ray channel model.
Namely, the following formula:
As shown in fig. 3, by integrating the three parts d 1,d2 and d 3 of the radio link, and d=d 1+d2+d3, a corresponding loss L n, n=1, 2,3 is caused on each path of the radio link. And losses L1 and L2 in dB are always positive.
L2＝20log10 N2
Where L 1、L2 and L 3 correspond to propagation losses of the three parts d 1,d2 and d 3 of the radio link, respectively, τ n represents a propagation loss factor, k e represents a ratio of an effective refractive index to an atmospheric refractive index, F s represents an atmospheric loss factor, F represents a path length of the first fresnel zone, and F represents a frequency of an electromagnetic wave emitted by the command center.
Constructing an attenuation function of an offshore rescue communication channel, which specifically comprises the following steps:
η＝|1+SLOS·G·εr·exp(jkDf)|
Where j represents an imaginary number, k represents a propagation constant, α represents a first reference angle, β represents a second reference angle, G represents an influence factor, η represents a circular earth loss function, P de represents an offshore rescue communication channel attenuation function, and L f represents a total diffraction loss of the direct path and the reflected path.
Step S22, constructing a multipath channel error rate progressive function based on the complex baseband received signal function and the multipath fading signal-to-noise ratio function.
In this step, the complex baseband received signal function specifically includes:
Where BD denotes a complex baseband received signal function, ρ denotes a unit mean gamma distribution random variable, V nexp(jΦn) denotes an nth specular marine reflection component, V n denotes an amplitude of the nth specular marine reflection component, Φ n denotes a random phase of the nth specular marine reflection component, and x+ jY denotes a complex gaussian random variable.
Where the complex gaussian random variable x+ jY, which represents the scattered received signal component due to the combined reception of many weak and independently phased scattered waves, can be shown as N-X, Y (0, σ 2), the gaussian model is based on a combination of the central limit theorem for the sum of these numerous waves.
The multipath channel error rate progressive function specifically comprises:
Wherein ω represents the ratio of the average power of the main component of the electromagnetic wave transmitted by the command center antenna to the diffuse reflection multipath power of the remaining components, σ 2 represents the variance in the complex gaussian random variable, Δ represents the degree of similarity of the average received powers of the multiple specular reflection components to each other, |m χ(s) | represents the absolute value of the moment generating function of the signal-to-noise ratio χ received by the rescue vessel, M represents the decay function of the fluctuating double path model, P μ () represents the legendre function of order μ, Representing the average signal-to-noise ratio received by the rescue vessel, s representing a polynomial composed of the decay functions of the wave-motion dual-path model, o () representing the peano remainder,Representing the multipath channel error rate progression function, α r and β r represent the modulation constants, and N represents the maximum number of modulation constants.
And S23, constructing an initial double-ray channel model according to the attenuation function of the marine rescue communication channel and the error rate progressive function of the multipath channel.
And step S3, solving a plurality of initial double-ray channel models based on an artificial bee colony algorithm to obtain the double-ray channel model.
In the step, the leading bees are used for constructing and migrating the honeycomb, are the cores of the bee colony, and represent the local optimal solution; the following bees are used for searching food sources around the honeycomb, and represent feasible solutions found by a searching algorithm; the detection bees are used for marking food sources with better quality around the honeycomb, helping the leading bees to make nesting and migration decisions, and represent the best solutions found by the search algorithm.
In this step, step S3 further includes steps S31 to S35.
Step S31, setting related parameters of the artificial bee colony algorithm and randomly generating an initial population, wherein the related parameters comprise population scale, maximum iteration number and search precision, and the initial honey source is randomly generated in a search space.
Step S32, leading the neighborhood of the bee and the corresponding initial honey source to be expanded for one time for random search, calculating the fitness value of the initial honey source after searching, and determining whether to reserve the initial honey source through a greedy selection mechanism.
In this step, the fitness value of the initial honey source is calculated by the following formula,
Wherein prob (v) represents the probability of the solution of the upsilon, fit (v) represents the fitness of the solution of the upsilon, c υ represents the probability of selecting the solution of the upsilon, abs () represents the absolute function, and NS represents the number of honey sources.
Step S33, if the initial honey source is not reserved, acquiring a first honey source corresponding to the leading bee based on the position of the initial honey source and a honey source updating algorithm.
In this step, based on the position of the initial honey source and the honey source updating algorithm, the method for obtaining the first honey source corresponding to the leading bee specifically includes:
wherein, Representing the initial position of honey source i,Represents the position near the honey source i when honey is picked for the t time, D max represents the upper limit of D-dimensional search space, D min represents a lower bound for the D-dimensional search space, and d= (1, 2, (S.) D)Represents the position close to the honey source i in d-dimensional space when honey is collected for the t time, rand (0, 1) represents selecting one random number in [0,1 ]Represents a new honey source generated near honey source i in d-dimensional space at the time of honey collection at the t-th time,Representing the position of leading bees near honey source i in d-dimensional space when honey is collected for the t time,Representing the position of leading bees near honey source j in d-dimensional space when honey is collected for the t time,Representing a random number in 0, 1.
In one example, a conjugate gradient method can be introduced in the following bee stage to strengthen the local searching capability of the artificial bee colony algorithm, the probability of each honey source being selected is calculated through a roulette method, the following bee selects the honey source to be updated according to the roulette method, and the conjugate gradient method is used for carrying out local fine searching on the honey source Ai to be updated by the following bee.
The specific updating rule is as follows: the current honey source N i is marked as A 0, meanwhile, the negative gradient direction P 0 generated at A 0 is used as the searching direction, the searching step length is t, the position where a new honey source is searched is marked as N 1,N1＝N0+tP0, and then the fitness value of N 1 is calculated by the following formula:
g(N1)＝g(N0+tP0)
g(N0+tP0)＝ming(N0+tP0)
if the new honey source position N 1 meets the set precision requirement, the conjugate gradient method is jumped out for circulation, otherwise, the new position is continuously searched by using the following calculation formula:
Nk+1＝Nk+tkPk
Pk+1＝-gradu(Nk)
Where N k represents the kth facility point, P k represents the negative gradient direction generated at N k, N k+1 represents the kth+1th facility point, P k+1 represents the negative gradient direction generated at N k+1, t k represents the search step size at N k+1; gradu () represents a gradient computation function; until the set precision requirement is met or the set cycle times are reached, and replacing the initial honey source with the final searched position; the conjugate gradient method is set to have an accuracy of eps=le -10. From the consideration of the integral optimization effect of the algorithm, the precision requirement of the conjugate gradient method can be properly lowered, for example, the set precision is eps=le -10, and the cycle times of the conjugate gradient method can be reduced.
And step S34, judging whether the iteration times of the artificial bee colony algorithm are larger than the maximum iteration times after all leading bees and following bees finish the search task.
And S35, if the iteration number of the artificial bee colony algorithm is greater than the maximum iteration number, converting the leading bee into a detection bee, randomly searching a second honey source in a search space by the detection bee through a chaos search strategy, and taking the second honey source as a double-ray channel model.
In the step, the chaos search strategy randomly searches a second honey source in a search space, and specifically comprises the following steps:
wherein, Representing new honey sources generated after adding chaotic search,Representing the position of leading bees near honey source i in d-dimensional space when honey is collected for the t time,Representing the position of the leading bee near the honey source j in d-dimensional space at the time of honey collection at the t-th time, ceil () represents an upward rounding function, rand (0, 1) represents a random number in the choice [0,1], cir represents a Circle mapping factor, mod () represents a solution Yu Hanshu, and random numbers a and b represent a random number in the choice [0,1 ].
The initialization strategy combining homogenization and randomization is used for generating various initial honey sources, a foundation is laid for subsequent searching, the collaborative searching strategy regulated by the leading bee guidance and greedy selection mechanism is used for searching the optimal solution, global and local searching capability is effectively balanced, searching time is reduced, searching precision is improved, meanwhile, the leading bee is converted into the chaotic searching strategy after the detection bee to effectively help the algorithm to escape the local optimal value, and the chaotic searching strategy has higher tracking precision and faster tracking speed and less power fluctuation under static and dynamic environments.
And S4, simulating the communication process of the rescue ship in real time according to the meteorological environment attenuation model and the double-ray channel model so as to compensate the attenuation channel of the rescue ship in real time.
The method comprises the steps of constructing a meteorological environment attenuation model to correct marine weather factors in real time, introducing the acquired sea surface reflection characteristics, shadow effect and multipath channel signal-to-noise ratio attenuation into a double-ray channel model, carrying out real-time communication modulation through a complex baseband receiving signal, and finally optimizing the double-ray channel model through a manual bee colony algorithm to realize real-time compensation of an attenuation channel, thereby improving the transmission precision of a sea area wireless communication channel network.
Based on the above method, the embodiment of the application discloses a channel attenuation compensation device of an offshore rescue communication channel attenuation model, referring to fig. 4, the channel attenuation compensation device 1 comprises an attenuation model construction module 11, a channel model construction module 12, a swarm algorithm optimization module 13 and an attenuation channel compensation module 14, wherein,
The attenuation model construction module 11 is used for acquiring a plurality of attenuation characteristics of the marine meteorological environment according to the marine meteorological environment data set acquired by the command center and constructing a meteorological environment attenuation model corresponding to the attenuation characteristics of the marine meteorological environment;
The channel model construction module 12 is configured to construct an initial dual-ray channel model based on the rough sea surface reflection function, the sea surface shadow effect function, the complex baseband received signal function, and the multipath fading signal-to-noise ratio function;
the bee colony algorithm optimization module 13 is used for solving a plurality of initial double-ray channel models based on an artificial bee colony algorithm to obtain a double-ray channel model;
The attenuation channel compensation module 14 is used for real-time simulation of the communication process of the rescue ship according to the meteorological environment attenuation model and the dual-ray channel model so as to compensate the attenuation channel of the rescue ship in real time.
In one example, the channel model construction module 12 is configured to construct an offshore rescue communication channel attenuation function based on the rough sea surface reflection function and the sea surface shadow effect function; constructing a multipath channel error rate progressive function based on the complex baseband received signal function and the multipath fading signal-to-noise ratio function; and constructing an initial double-ray channel model according to the attenuation function of the marine rescue communication channel and the error rate progressive function of the multipath channel. .
In one example, the rough sea surface reflection function and the sea surface shadow effect function specifically include:
Wherein epsilon r represents the effective reflection coefficient of rough sea surface, epsilon represents the effective reflection coefficient of the sea surface, sigma h represents the standard deviation value of the height distribution of rough sea surface, theta i represents the incident angle of electromagnetic waves emitted by a command center antenna, lambda represents the wavelength of electromagnetic waves emitted by the command center, S LOS represents the sea shadow effect function, lambda represents the constant function, erfc () represents the complementary error function, and gamma represents the root mean square surface slope of electromagnetic waves emitted by the command center.
In one example, constructing the marine rescue communication channel attenuation function specifically includes:
η＝|1+SLOS·G·εr·exp(jkDf)|
Wherein D L represents the line-of-sight propagation distance of the rescue vessel from the command center, D f represents the path length difference between D L and sea surface reflection, R represents the earth radius, h 1 represents the altitude difference between the rescue vessel and sea surface, h 2 represents the altitude difference between the command center and sea surface, j represents an imaginary number, k represents the propagation constant, ψ represents the model loss factor, G represents the impact factor, α represents the first reference angle, β represents the second reference angle, η represents the circular earth loss function, P de represents the marine rescue communication channel attenuation function, and L f represents the total diffraction loss of the direct and reflected paths.
In one example, the complex baseband receive signal function specifically includes:
Where BD denotes a complex baseband received signal function, ρ denotes a unit mean gamma distribution random variable, V nexp(jΦn) denotes an nth specular marine reflection component, V n denotes an amplitude of the nth specular marine reflection component, Φ n denotes a random phase of the nth specular marine reflection component, and x+ jY denotes a complex gaussian random variable.
In one example, the multipath channel error rate progression function specifically includes:
Wherein ω represents the ratio of the average power of the main component of the electromagnetic wave transmitted by the command center antenna to the diffuse reflection multipath power of the remaining components, σ 2 represents the variance in the complex gaussian random variable, Δ represents the degree of similarity of the average received powers of the multiple specular reflection components to each other, |m χ(s) | represents the absolute value of the moment generating function of the signal-to-noise ratio χ received by the rescue vessel, M represents the decay function of the fluctuating double path model, P μ () represents the legendre function of order μ, Representing the average signal-to-noise ratio received by the rescue vessel, s representing a polynomial composed of the decay functions of the wave-motion dual-path model, o () representing the peano remainder,Representing the multipath channel error rate progression function, α r and β r represent the modulation constants, and N represents the maximum number of modulation constants.
In one example, the swarm algorithm optimization module 13 is configured to set parameters related to the artificial swarm algorithm and randomly generate an initial population, where the parameters related include population size, maximum iteration number, and search accuracy, and the initial honey source is randomly generated in the search space; leading the neighborhood of the bee and the corresponding initial honey source to be expanded for one time for random search, calculating the fitness value of the initial honey source after searching, and determining whether to reserve the initial honey source through a greedy selection mechanism; if the initial honey source is not reserved, acquiring a first honey source corresponding to the leading bee based on the position of the initial honey source and a honey source updating algorithm; after all leading bees and following bees finish the search task, judging whether the iteration times of the artificial bee colony algorithm are larger than the maximum iteration times; if the iteration number of the artificial bee colony algorithm is larger than the maximum iteration number, converting the leading bee into a detection bee, randomly searching a second honey source in a search space by the detection bee through a chaos search strategy, and taking the second honey source as a double-ray channel model.
In one example, solving a plurality of initial dual-ray channel models based on an artificial bee colony algorithm to obtain a dual-ray channel model specifically includes:
wherein, Representing the initial position of honey source i,Represents the position near the honey source i when honey is picked for the t time, D max represents the upper limit of D-dimensional search space, D min represents a lower bound for the D-dimensional search space, and d= (1, 2, (S.) D)Represents the position close to the honey source i in d-dimensional space when honey is collected for the t time, rand (0, 1) represents selecting one random number in [0,1 ]Represents a new honey source generated near honey source i in d-dimensional space at the time of honey collection at the t-th time,Representing the position of leading bees near honey source i in d-dimensional space when honey is collected for the t time,Representing the position of leading bees near honey source j in d-dimensional space when honey is collected for the t time,Representing a random number in 0, 1.
In one example, the detection bees randomly search the second honey source in the search space through the chaotic search strategy, and specifically comprises:
wherein, Representing new honey sources generated after adding chaotic search,Representing the position of leading bees near honey source i in d-dimensional space when honey is collected for the t time,Representing the position of the leading bee near the honey source j in d-dimensional space at the time of honey collection at the t-th time, ceil () represents an upward rounding function, rand (0, 1) represents a random number in the choice [0,1], cir represents a Circle mapping factor, mod () represents a solution Yu Hanshu, and random numbers a and b represent a random number in the choice [0,1 ].
Referring to fig. 5, a schematic structural diagram of an electronic device is provided in an embodiment of the present application. As shown in fig. 5, the electronic device 2 may include: at least one processor 21, at least one network interface 24, a user interface 23, a memory 25, at least one communication bus 22.
Wherein the communication bus 22 is used to enable connected communication between these components.
The user interface 23 may include a Display screen (Display), a Camera (Camera), and the optional user interface 23 may further include a standard wired interface, a wireless interface.
The network interface 24 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 21 may comprise one or more processing cores. The processor 21 connects various parts within the overall server using various interfaces and lines, performs various functions of the server and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 25, and invoking data stored in the memory 25. Alternatively, the processor 21 may be implemented in at least one hardware form of digital signal processing (DigitalSignalProcessing, DSP), field programmable gate arrays (field-ProgrammableGateArray, FPGA), programmable logic arrays (ProgrammableLogicArray, PLA). The processor 21 may integrate one or a combination of several of a central processor (CentralProcessingUnit, CPU), an image processor (GraphicsProcessingUnit, GPU), a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 21 and may be implemented by a single chip.
The memory 25 may include a random access memory (RandomAccessMemory, RAM) or a read-only memory (rom). Optionally, the memory 25 includes a non-transitory computer readable medium (non-transitorycomputer-readablestoragemedium). Memory 25 may be used to store instructions, programs, code sets, or instruction sets. The memory 25 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like involved in the above respective method embodiments. The memory 25 may alternatively be at least one memory device located remotely from the aforementioned processor 21. As shown in fig. 5, an operating system, a network communication module, a user interface module, and an application program of a channel attenuation compensation method of the marine rescue communication channel attenuation model may be included in the memory 25 as one type of computer storage medium.
In the electronic device 2 shown in fig. 3, the user interface 23 is mainly used for providing an input interface for a user, and acquiring data input by the user; and processor 21 may be configured to invoke an application of the channel attenuation compensation method of the rescue communication channel attenuation model at sea in memory 25, which when executed by one or more processors, causes the electronic device to perform one or more of the methods as in the embodiments described above.
A computer readable storage medium having instructions stored thereon. When executed by one or more processors, cause a computer to perform a method such as one or more of the embodiments described above.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all of the preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. And the aforementioned memory includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a magnetic disk or an optical disk.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. A channel attenuation compensation method of an offshore rescue communication channel attenuation model, which is applied to a cloud computing platform, and is characterized by comprising the following steps:
Acquiring a plurality of marine weather environment attenuation characteristics according to a marine weather environment data set acquired by a command center, and constructing a weather environment attenuation model corresponding to the marine weather environment attenuation characteristics;
Constructing an initial double-ray channel model based on a rough sea surface reflection function, a sea surface shadow effect function, a complex baseband received signal function and a multipath fading signal-to-noise ratio function;
Solving a plurality of initial double-ray channel models based on an artificial bee colony algorithm to obtain a double-ray channel model;
And simulating the communication process of the rescue ship in real time according to the meteorological environment attenuation model and the double-ray channel model so as to compensate the attenuation channel of the rescue ship in real time.
2. The method of claim 1, wherein constructing the initial dual-ray channel model based on the rough sea surface reflection function, the sea surface shadow effect function, the complex baseband received signal function, and the multipath fading signal-to-noise ratio function, comprises:
Constructing an offshore rescue communication channel attenuation function based on the rough sea surface reflection function and the sea surface shadow effect function;
constructing a multipath channel error rate progressive function based on the complex baseband received signal function and the multipath fading signal-to-noise ratio function;
And constructing the initial double-ray channel model according to the attenuation function of the marine rescue communication channel and the error rate progressive function of the multipath channel.
3. The method of claim 2, wherein the rough sea surface reflection function and the sea surface shadow effect function comprise:
Wherein epsilon r represents the effective reflection coefficient of rough sea surface, epsilon represents the effective reflection coefficient of the sea surface, sigma h represents the standard deviation value of rough sea surface height distribution, theta i represents the incident angle of electromagnetic waves emitted by a command center antenna, lambda represents the wavelength of the electromagnetic waves emitted by the command center, S LOS represents the sea shadow effect function, lambda represents the constant function, erfc () represents the complementary error function, and gamma represents the root mean square surface slope of the electromagnetic waves emitted by the command center.
4. A method according to claim 3, wherein said constructing an offshore rescue communication channel attenuation function comprises:
η＝|1+SLOS·G·εr·exp(jkDf)|
Wherein D L represents a line-of-sight propagation distance of the rescue vessel from the command center, D f represents a path length difference between D L and sea surface reflection, R represents an earth radius, h 1 represents a height difference between the rescue vessel and sea surface, h 2 represents a height difference between the command center and sea surface, j represents an imaginary number, k represents a propagation constant, ψ represents a model loss factor, G represents an influence factor, α represents a first reference angle, β represents a second reference angle, η represents a circular earth loss function, P de represents an offshore rescue communication channel attenuation function, and L f represents a total diffraction loss of a direct path and a reflection path.
5. The method of claim 4, wherein the complex baseband received signal function specifically comprises:
Where BD denotes a complex baseband received signal function, ρ denotes a unit mean gamma distribution random variable, V nexp(jΦn) denotes an nth specular marine reflection component, V n denotes an amplitude of the nth specular marine reflection component, Φ n denotes a random phase of the nth specular marine reflection component, and x+ jY denotes a complex gaussian random variable.
6. The method of claim 5, wherein the multipath channel error rate progression function specifically comprises:
Wherein ω represents the ratio of the average power of the main component of the electromagnetic wave transmitted by the command center antenna to the diffuse reflection multipath power of the remaining components, σ 2 represents the variance in the complex gaussian random variable, Δ represents the degree of similarity of the average received powers of the multiple specular reflection components to each other, |m χ(s) | represents the absolute value of the moment generating function of the signal-to-noise ratio χ received by the rescue vessel, M represents the decay function of the fluctuating dual-path model, P μ () represents the legendre function of order μ, Representing the average signal-to-noise ratio received by the rescue vessel, s representing a polynomial composed of the decay function of the wave-motion dual-path model, o () representing the peano remainder,Representing the multipath channel error rate progression function, α r and β r represent the modulation constants, and N represents the maximum number of modulation constants.
7. The method of claim 1, wherein said artificial bee colony algorithm-based solving a number of said initial dual-ray channel models to obtain a dual-ray channel model comprises the steps of:
setting related parameters of an artificial bee colony algorithm and randomly generating an initial population, wherein the related parameters comprise population scale, maximum iteration times and search precision, and an initial honey source is randomly generated in a search space;
Leading the bee to expand a random search with the neighborhood of the corresponding initial honey source, calculating the fitness value of the initial honey source after searching, and determining whether to reserve the initial honey source through a greedy selection mechanism;
If the initial honey source is not reserved, acquiring a first honey source corresponding to the leading bee based on the position of the initial honey source and a honey source updating algorithm;
After all the leading bees and the following bees finish the search task, judging whether the iteration times of the artificial bee colony algorithm are larger than the maximum iteration times;
if the iteration number of the artificial bee colony algorithm is larger than the maximum iteration number, converting the leading bee into a detection bee, randomly searching a second honey source in the search space by the detection bee through a chaos search strategy, and taking the second honey source as a double-ray channel model.
8. The method of claim 7, wherein solving a plurality of the initial dual-ray channel models based on an artificial bee colony algorithm to obtain a dual-ray channel model, in particular comprises:
wherein, Representing the initial position of honey source i,Represents the position near the honey source i when honey is picked for the t time, D max represents the upper limit of D-dimensional search space, D min represents a lower bound for the D-dimensional search space, and d= (1, 2, (S.) D)Represents the position close to the honey source i in d-dimensional space when honey is collected for the t time, rand (0, 1) represents selecting one random number in [0,1 ]Represents a new honey source generated near honey source i in d-dimensional space at the time of honey collection at the t-th time,Representing the position of leading bees near honey source i in d-dimensional space when honey is collected for the t time,Representing the position of leading bees near honey source j in d-dimensional space when honey is collected for the t time,Representing a random number in 0, 1.
9. The method of claim 7, wherein the forensic bees randomly search the search space for a second honey source by a chaotic search strategy, comprising:
wherein, Representing new honey sources generated after adding chaotic search,Representing the position of leading bees near honey source i in d-dimensional space when honey is collected for the t time,Representing the position of the leading bee near the honey source j in d-dimensional space at the time of honey collection at the t-th time, ceil () represents an upward rounding function, rand (0, 1) represents a random number in the choice [0,1], cir represents a Circle mapping factor, mod () represents a solution Yu Hanshu, and random numbers a and b represent a random number in the choice [0,1 ].
10. Channel attenuation compensation device of an offshore rescue communication channel attenuation model, characterized in that the channel attenuation compensation device (1) comprises an attenuation model construction module (11), a channel model construction module (12), a bee colony algorithm optimization module (13) and an attenuation channel compensation module (14), wherein,
The attenuation model construction module (11) is used for acquiring a plurality of attenuation characteristics of the marine meteorological environment according to the marine meteorological environment data set acquired by the command center and constructing a meteorological environment attenuation model corresponding to the attenuation characteristics of the marine meteorological environment;
The channel model construction module (12) is used for constructing an initial double-ray channel model based on a rough sea surface reflection function, a sea surface shadow effect function, a complex baseband received signal function and a multipath fading signal-to-noise ratio function;
the bee colony algorithm optimization module (13) is used for solving a plurality of initial double-ray channel models based on an artificial bee colony algorithm so as to obtain a double-ray channel model;
The attenuation channel compensation module (14) is used for simulating the communication process of the rescue ship in real time according to the meteorological environment attenuation model and the double-ray channel model so as to compensate the attenuation channel of the rescue ship in real time.
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| CN113630202A (en) * | 2021-08-18 | 2021-11-09 | 嘉兴学院 | Method for estimating marine wireless communication channel fading in complex meteorological environment |
| CN116625376A (en) * | 2023-06-05 | 2023-08-22 | 西安电子科技大学 | Unmanned aerial vehicle maritime search and rescue path planning method based on tabu bee colony algorithm |
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| US20100130151A1 (en) * | 2006-11-14 | 2010-05-27 | Yozo Shoji | Channel characteristic analyzing apparatus and method |
| CN113630202A (en) * | 2021-08-18 | 2021-11-09 | 嘉兴学院 | Method for estimating marine wireless communication channel fading in complex meteorological environment |
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