CN111538059B - Self-adaptive rapid dynamic positioning system and method based on improved Boltzmann machine - Google Patents
Self-adaptive rapid dynamic positioning system and method based on improved Boltzmann machine Download PDFInfo
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- CN111538059B CN111538059B CN202010390792.3A CN202010390792A CN111538059B CN 111538059 B CN111538059 B CN 111538059B CN 202010390792 A CN202010390792 A CN 202010390792A CN 111538059 B CN111538059 B CN 111538059B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/46—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention relates to an adaptive fast dynamic positioning system and method based on an improved Boltzmann machine, and belongs to the technical field of marine rescue. The life jacket comprises a signal generator, a Beidou satellite positioning system, an unmanned aerial vehicle base station and an unmanned aerial vehicle, wherein the signal generator is arranged on the life jacket; the Beidou satellite positioning system is connected with the unmanned aerial vehicle through the unmanned aerial vehicle base station; the signal generator that is equipped with on the life vest is connected with big dipper satellite positioning system and unmanned aerial vehicle. According to the invention, the area of the accident is rapidly obtained through the Beidou positioning system, the drift model of the personnel falling into the water on the sea is constructed through the analysis of the real-time data of the terrain and the hydrological environment of the sea area in China, and the rapid locking of the search and rescue area is realized through the LORA three-point positioning method and the improved trajectory planning algorithm of the Boltzmann machine. The invention has good practicability, can quickly position the person falling into the water, can capture the position of the person falling into the water to the maximum extent, realizes the putting of the positioner and is convenient for quick rescue in the later period.
Description
Technical Field
The invention relates to an adaptive rapid dynamic positioning system and method based on an improved Boltzmann machine, and belongs to the technical field of rescue at sea.
Background
With the development of science and technology and the enhancement of economic development level, the proposal of marine economic planning in China and the expansion of military sea area activity range are increased, marine activities such as marine fishing, marine transportation, military patrol and the like are increased, various accidents can happen in the activities inevitably, effective rescue or control needs to be achieved within the shortest time for emergency treatment such as search and rescue, positioning and the like of accident personnel and ships, and a rapid dynamic positioning system needs to be established for accurate positioning and rescue positioning of personnel falling into water. The conventional marine positioning system does not consider the influence of factors such as marine climate, environment and the like, does not use a track planning algorithm, and has the problems of unreliable prediction model and slow target approaching speed.
Disclosure of Invention
The invention aims to solve the technical problems that when a marine disaster occurs, the search range of the position area of a person to be rescued on the sea is narrowed, the position can be accurately determined, and the rescue efficiency is improved.
In order to solve the problems, the technical scheme adopted by the invention is to provide an adaptive rapid dynamic positioning system based on an improved Boltzmann machine, which comprises a signal generator, a Beidou satellite positioning system, an unmanned aerial vehicle base station and an unmanned aerial vehicle, wherein the signal generator, the Beidou satellite positioning system, the unmanned aerial vehicle base station and the unmanned aerial vehicle are arranged on a life jacket; the Beidou satellite positioning system is connected with the unmanned aerial vehicle through the unmanned aerial vehicle base station; and a signal generator arranged on the life jacket is connected with the Beidou satellite positioning system and the unmanned aerial vehicle.
Preferably, be equipped with LORA signal generator on the life vest, be equipped with LORA's location communication module on the unmanned aerial vehicle.
The invention also provides a working method of the self-adaptive rapid dynamic positioning system based on the improved Boltzmann machine, which comprises the following steps:
step 1: the offshore drowning person sends a self position signal to a Beidou satellite positioning system, the Beidou satellite positioning system establishes a drifting model of the offshore drowning person by using the received position signal, predicts a drifting track of the drowning person according to the drifting model of the offshore drowning person, and then transmits the drifting track signal to an unmanned aerial vehicle base station;
step 2: establishing an unmanned aerial vehicle positioning system based on LORA; the unmanned aerial vehicle base station establishes an LORA three-point positioning model by adopting an RSSI (received signal strength indicator) propagation model of the LORA and the communication capability of the unmanned aerial vehicle base station, and transmits signals to the unmanned aerial vehicle;
and step 3: applying a trajectory planning algorithm based on an improved Boltzmann machine; carrying out the in-process that the search and rescue scope was reduced to the self-adaptation of LORA positioning system's unmanned aerial vehicle, considering the influence of the wind pressure that unmanned aerial vehicle received at sea level, wind direction, adopting the trajectory planning algorithm based on modified boltzmann machine to carry out optimal planning to the route to go to the personnel of falling into the water department rapidly and carry out the input of locator and appropriate amount of fresh water.
Preferably, the establishing of the model of the drifting of the overboard personnel in the step 1 needs to consider the influence of a plurality of ocean factors on the overboard personnel when drifting on the sea surface, including wind pressure, wind current, waves and self submergence ratio factors.
Preferably, the positioning system of the unmanned aerial vehicle based on LORA established in step 2 performs positioning by calculating three-point signal strength values, and establishes a "distance-loss" model by using a gradual change model in wireless signal transmission.
Preferably, the trajectory planning algorithm based on the improved boltzmann machine in the step 3 aims to enable the unmanned aerial vehicle to approach the target faster in the process of adaptively and quickly reducing the search and rescue range, and to optimally plan the path by using the improved boltzmann machine in consideration of the influence of the wind pressure and the wind direction on the unmanned aerial vehicle at the sea level.
Preferably, the improved boltzmann machine combines an MTM algorithm and the boltzmann machine to accelerate the path planning of the unmanned aerial vehicle.
Compared with the prior art, the invention has the following beneficial effects:
when a shipwreck occurs, the invention reduces a search range of the position area of the personnel to be rescued on the sea, and can utilize LORA to carry out accurate position determination, thereby improving the rescue efficiency.
Drawings
FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is a diagram of a drift model architecture;
FIG. 3 is a schematic view of a LORA three-point positioning system;
FIG. 4 is a diagram of a location update of a person to be saved;
the change in position is indicated by arrows in the figure;
reference numerals: 1. offshore personnel 2 that fall into water, unmanned aerial vehicle 3, big dipper satellite positioning system 4, unmanned aerial vehicle basic station
Detailed Description
In order to make the invention more comprehensible, preferred embodiments are described in detail below with reference to the accompanying drawings:
as shown in fig. 1-4, the invention provides an adaptive fast dynamic positioning system based on an improved boltzmann machine, which comprises a signal generator arranged on a life jacket, a Beidou satellite positioning system 3, an unmanned aerial vehicle base station 4 and an unmanned aerial vehicle 2; the Beidou satellite positioning system 3 is connected with the unmanned aerial vehicle 2 through an unmanned aerial vehicle base station 4; the signal generator that is equipped with on the life vest that personnel 1 dressed in the water at sea is connected with big dipper satellite positioning system 3 and unmanned aerial vehicle 2. Be equipped with LORA signal generator on the life vest, be equipped with LORA's location communication module on unmanned aerial vehicle 2.
The invention provides a working method of an adaptive rapid dynamic positioning system based on an improved Boltzmann machine, which comprises the following steps:
step 1: the offshore drowning person 1 sends a self position signal to the Beidou satellite positioning system 3, the Beidou satellite positioning system 3 establishes a drifting model of the offshore drowning person by using the received position signal, predicts a drifting track of the drowning person according to the drifting model of the offshore drowning person, and then transmits the drifting track signal to the unmanned aerial vehicle base station 4;
and 2, step: establishing an unmanned aerial vehicle 2 positioning system based on LORA; the unmanned aerial vehicle base station 4 establishes an LORA three-point positioning model by adopting an RSSI (received signal strength indicator) propagation model of the LORA and the communication capability of the unmanned aerial vehicle base station, and transmits signals to the unmanned aerial vehicle 2;
and 3, step 3: applying a trajectory planning algorithm based on an improved Boltzmann machine; during the process that the unmanned aerial vehicle 2 carrying the LORA positioning system carries out self-adaptive reduction of the search and rescue range, the influence of wind pressure and wind direction on the unmanned aerial vehicle 2 at sea level is considered, and the optimal planning is carried out on the path by adopting a trajectory planning algorithm based on an improved Boltzmann machine, so that the unmanned aerial vehicle can rapidly go to the position of the person 1 falling into the water to carry out the release of a positioner and a proper amount of fresh water.
The establishing of the offshore drowning person drifting model in the step 1 needs to consider the influence of a plurality of ocean factors including wind pressure, wind current, waves and self submergence ratio factors on the drowning person when the drowning person drifts on the sea surface.
The drift velocity of the person falling into water influenced by the wind pressure in the actual drifting process is expressed by
Wherein A represents the area of the person falling into water facing the wind on the water surface, B represents the area of the person falling into water facing the sea current under the water surface, v i The wind speed is expressed as the ith time period, and k is a constant and is related to the target to be rescued.
The drift velocity of the person falling into water influenced by the ocean current in the actual drifting process is represented by W i ＝λV i + b, where λ and b are both constants, and in practice it has been found that λ is typically around 4, b is between 0 and 1, and V i Is the drift velocity affected by wind pressure.
The drift velocity of the person falling into the water influenced by the waves in the actual drifting process is expressed by
In the formula: b is the projection surface of the floater under the water surface, C d (C D ) Is a coefficient of resistance, C d (C D ) The value of (b) is different depending on the shape of the floating body under the water surface, and is generally measured by a water tank test. d is the submergence ratio of the drifting object, H 1/3 The effective wave height, K is the wave number, and λ is the wavelength.
The two factors of wind pressure and wind induced flow in the drowning person drift model are the leading factors for determining the drift trajectory of the drowning person, the wind direction and the sea current flow direction on the sea change every moment, and the time needs to be considered in a segmented manner. Since the rescue time is limited, the recording is performed for a period of 20 minutes.
In the model of the drift of the person falling into water, the submergence ratio of the person falling into water and the waves also have an influence on the drift trajectory, and the submergence ratio refers to the ratio of the volume of the person falling into water above the water surface to the volume of the person falling into water below the water surface. Wave's main parametersWith wave number, wave height and wave length, in actual drifting, the waves generate a continuous propelling force to people falling into the water, the general direction is the same as the direction of ocean current, and the waves are always acting on drifting objects. The total drift velocity can be expressed as: v General assembly ＝λ 1 V Wind power +λ 2 V Water (W) +λ 3 V Wave (wave) Wherein λ is 1 ，λ 2 And λ 3 The weight values of the three are respectively, the weight values are generally fixed and unchangeable, and the weight values need to be measured again only in extreme weather.
And 2, an unmanned aerial vehicle positioning system based on LORA is established to perform positioning by calculating three-point signal strength values, and a distance-loss model is established by using a gradient model in wireless signal transmission.
The unmanned aerial vehicle positioning system based on the LORA is characterized in that an LORA (an ultra-long distance wireless transmission scheme based on a spread spectrum technology) base station is arranged on the unmanned aerial vehicle, and is in data transmission with an LORA signal generator set on a life jacket, and positioning is carried out by calculating a three-point signal strength value (RSSI).
Among the unmanned aerial vehicle positioning system based on LORA, FIG. 3 is LORA three point location system schematic diagram, has a plurality of unmanned aerial vehicles to fly around waiting to rescue the target scope, and unmanned aerial vehicle is last to carry LORA's location communication module, and after the location node scanned all anchor base station's ID, the signal strength value (RSSI) to every anchor base station was sequenced, obtains the best three anchor base stations of RSSI, carries out the measurement of distance through the power of signal value.
In an unmanned aerial vehicle positioning system based on LORA, an RSSI estimation method generally performs multiple measurement experiments on a positioning environment to obtain a relationship between a signal propagation distance and a path loss in the environment, and a gradual change Model (Shadowing Model) in wireless signal transmission establishes a distance-loss Model, which generally has the following form:
wherein, d 0 Is the distance of the reference point from the signal source, P r0 Is a distance d 0 The RSSI of the signal source is received, d is the true distance,z～N(0,d 2 ) Is a shading factor, P is the RSSI of the signal source received by the point to be detected, n is the path loss coefficient, and the position of the person to be saved is updated as shown in fig. 4.
The trajectory planning algorithm based on the improved boltzmann machine in the step 3 aims to enable the unmanned aerial vehicle to approach a target faster in the process of self-adapting and fast reducing the search and rescue range, consider the influence of wind pressure and wind direction on the unmanned aerial vehicle at sea level and optimally plan a path by using the improved boltzmann machine. The improved Boltzmann machine combines an MTM algorithm with the Boltzmann machine, and accelerates the path planning speed of the unmanned aerial vehicle.
The improved boltzmann machine-based trajectory planning algorithm considers the influences of wind pressure and wind direction on the sea level in the unmanned aerial vehicle trajectory planning process, so that the search and rescue range is adaptively and quickly reduced, and the unmanned aerial vehicle is enabled to approach the target more quickly.
In the trajectory planning algorithm based on the modified boltzmann machine, because the MTM (Multiple-try Metropolis) algorithm extracts a plurality of test points instead of an original single test point through each iteration in the calculation process of simulated annealing, each test point is endowed with a weight, one test point from the k test points is randomly selected as an proposing point, and the probability of each point being selected is proportional to the weight of each point. The proposal point is then accepted or rejected according to the Metropolis-Hastings (MH) principle. After the method is combined with the unmanned aerial vehicle path planning problem, the parameter space can be locally searched, the receiving probability of the proposal point is improved by acquiring posterior distribution information, and the operation convergence rate is effectively improved.
The trajectory planning algorithm based on the improved Boltzmann machine adopts a model based on the improved Boltzmann Machine (BM) to perform optimal planning on a path, and an energy function E (X) of the Boltzmann machine can be defined as follows:
wherein ω is ij Is the weight of the connection between two variables, x i E {0,1} represents the state, b i Represents the variable x i Of the sensor. Based on the original Boltzmann machine, is added withAfter the MTM (Multiple-try Metropolis) algorithm is combined with the unmanned aerial vehicle path planning problem, the parameter space can be locally searched, the receiving probability of the proposal point is improved by acquiring posterior distribution information, and the operation convergence rate is effectively improved.
When the path planning is carried out based on the improved trajectory planning algorithm of the Boltzmann machine, the current position of the unmanned aerial vehicle is selected as the starting point of the path planning, the central point in the target area is selected as the end point, and the wind pressure and the wind direction are taken as influence factors and considered in the model. By measuring the weight of the two factors, the route which enables the unmanned aerial vehicle to search at the fastest speed and has lower energy loss is found.
While the invention has been described with respect to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention. Those skilled in the art can make various changes, modifications and equivalent arrangements, which are equivalent to the embodiments of the present invention, without departing from the spirit and scope of the present invention, and which may be made by utilizing the techniques disclosed above; meanwhile, any equivalent changes, modifications and evolutions of the above embodiments according to the essential technology of the present invention are still within the scope of the technical solution of the present invention.
Claims (3)
1. A working method of an adaptive fast dynamic positioning system based on an improved Boltzmann machine is characterized in that: the self-adaptive rapid dynamic positioning system based on the improved Boltzmann machine comprises a signal generator, a Beidou satellite positioning system, an unmanned aerial vehicle base station and an unmanned aerial vehicle, wherein the signal generator, the Beidou satellite positioning system, the unmanned aerial vehicle base station and the unmanned aerial vehicle are arranged on a life jacket; the Beidou satellite positioning system is connected with the unmanned aerial vehicle through the unmanned aerial vehicle base station; a signal generator arranged on the life jacket is connected with the Beidou satellite positioning system and the unmanned aerial vehicle; an LORA signal generator is arranged on the life jacket, and a positioning communication module of the LORA is arranged on the unmanned aerial vehicle; the working method comprises the following steps:
step 1: the offshore drowning person sends a self position signal to a Beidou satellite positioning system, the Beidou satellite positioning system establishes a drifting model of the offshore drowning person by using the received position signal, predicts a drifting track of the drowning person according to the drifting model of the offshore drowning person, and then transmits the drifting track signal to an unmanned aerial vehicle base station;
and 2, step: establishing an unmanned aerial vehicle positioning system based on LORA; the unmanned aerial vehicle base station establishes an LORA three-point positioning model by adopting an RSSI (received signal strength indicator) propagation model of the LORA and the communication capability of the unmanned aerial vehicle base station, and transmits signals to the unmanned aerial vehicle;
and step 3: applying a trajectory planning algorithm based on an improved Boltzmann machine; in the process of carrying out self-adaptive search and rescue range reduction by an unmanned aerial vehicle loaded with a LORA positioning system, considering the influence of wind pressure and wind direction on the unmanned aerial vehicle on the sea level, and optimally planning a path by adopting a track planning algorithm based on an improved Boltzmann machine, so that the unmanned aerial vehicle can rapidly go to a person falling into the water to carry out a positioner and put in proper amount of fresh water; the improved boltzmann machine-based trajectory planning algorithm aims to enable an unmanned aerial vehicle to approach a target faster in the process of self-adapting and fast reducing the search and rescue range, consider the influence of wind pressure and wind direction on the unmanned aerial vehicle at sea level and optimally plan a path by using the improved boltzmann machine; the improved Boltzmann machine combines an MTM algorithm with the Boltzmann machine, and accelerates the path planning speed of the unmanned aerial vehicle.
2. The method of claim 1, wherein the improved boltzmann machine based adaptive fast dynamic positioning system is characterized in that: the establishing of the drifting model of the offshore drowning person in the sea in the step 1 needs to consider the influence of a plurality of sea factors including wind pressure, wind current, waves and self submergence ratio factors on the drowning person in the sea when the drowning person drifts on the sea surface.
3. The method of claim 1, wherein the improved boltzmann machine based adaptive fast dynamic positioning system is characterized in that: and 2, an unmanned aerial vehicle positioning system based on LORA is established to perform positioning by calculating three-point signal strength values, and a distance-loss model is established by using a gradient model in wireless signal transmission.
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| CN113867337A (en) * | 2021-09-14 | 2021-12-31 | 武汉理工大学 | Formation path planning method and system for maritime search and rescue |
| CN115071915A (en) * | 2022-06-23 | 2022-09-20 | 平安银行股份有限公司 | Life jacket, communication host and water rescue system |
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