CN111586633A - Unmanned ship cooperative transmission method facing marine environment perception - Google Patents

Unmanned ship cooperative transmission method facing marine environment perception Download PDF

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CN111586633A
CN111586633A CN202010419492.3A CN202010419492A CN111586633A CN 111586633 A CN111586633 A CN 111586633A CN 202010419492 A CN202010419492 A CN 202010419492A CN 111586633 A CN111586633 A CN 111586633A
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unmanned ship
perception
buoy
base station
sensing
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CN111586633B (en
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吕玲
戴燕鹏
林彬
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Dalian Maritime University
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Abstract

The invention provides a marine environment perception-oriented unmanned ship cooperative transmission method, which belongs to the technical field of wireless communication and comprises the following steps: based on wireless channel conditions, uniformly expressing the influence of data packet loss and transmission delay in wireless transmission on sensing performance as sensing information age, and describing an analytic relation between the sensing information age and state sensing mean square error; based on the transmission rate of the unmanned ship cooperation buoy-land base station perception information, the association relation between the unmanned ship position and the buoy-unmanned ship is optimized in a combined mode, and the perception mean square error is minimized; and determining the association relation of the buoy and the unmanned ship in polynomial time based on a heuristic algorithm of a matching theory, and determining the position of the unmanned ship based on a convex optimization theory. The method can reduce the influence of limited resources and path loss on the perception performance and effectively improve the perception performance of the marine environment.

Description

Unmanned ship cooperative transmission method facing marine environment perception
Technical Field
The invention relates to the technical field of wireless communication, in particular to an unmanned ship cooperative transmission method for marine environment perception.
Background
With the rapid development of wireless communication technology, emerging internet of things systems are widely applied to intelligent seas, such as marine environment monitoring, offshore exploration, maritime search and rescue and the like. In recent years, unmanned ships have attracted extensive attention in the industry and academia by virtue of their advantages such as on-demand deployment and flexible spatial network structure.
Taking marine environment monitoring activities as an example, unmanned ships in spatial distribution cooperate to monitor environmental conditions, and transmit sensing data to a land base station through a wireless channel, and the land base station estimates the marine environment according to the received sensing data. However, the marine environment sensing system based on unmanned ship cooperation has practical limitations such as limited transmission power, limited sensing range, and scarce spectrum resources, which will result in increased packet loss rate and transmission delay. If the terrestrial base station fails to successfully receive the sensing data within a specified time, the current state must be inferred from the historical sensing data, which results in larger sensing errors and reduced monitoring performance. It can be seen that the marine environment perception performance is heavily dependent on the transmission performance of the perception data transmitted back through the marine wireless network. The information age can measure the timeliness and freshness of the available state information of the land base station, so that the state perception based on the information age is convenient for analyzing the influence of the transmission performance of the perception information returned through the offshore wireless network on the state perception performance. In order to improve the perception capability of the marine environment in a complex marine wireless communication environment, the problem of how to passively tolerate the adverse effect of data packet loss and transmission delay on the perception performance of the marine environment is mainly concerned in the aspect of state perception; the wireless transmission aspect mainly focuses on actively alleviating adverse effects of wireless transmission, and improving transmission reliability and real-time performance of the sensing information returned via the wireless network. However, the state-sensing aspect mainly reduces adverse effects of the harsh wireless environment and limited network resources on transmission reliability and real-time performance by designing and optimizing a state-sensing algorithm, which can be regarded as tolerating packet loss and transmission delay in a passive manner. In a complex maritime wireless communication environment, it is difficult for such a passive approach to significantly improve the perceptual performance of the marine environment. In the aspect of wireless communication, cooperative transmission can improve the strength of a received signal and the spectrum utilization rate by using space diversity gain, and is favorable for providing high-reliability low-delay perception information transmission for state perception in a severe marine environment. The cooperative transmission aspect mainly focuses on how to reliably and timely transmit a data packet to a destination, but does not consider how a receiving end utilizes information carried by the data packet. Therefore, the traditional cooperative transmission is directly applied to the transmission of the state perception information, the potential gain of the cooperative transmission technology is difficult to be fully exerted, and the improvement of the marine environment perception capability is restricted.
In summary, the problems of the prior art are as follows: the existing state perception algorithm passively tolerates the adverse effect of data packet loss and transmission delay on the perception performance of the marine environment by designing and optimizing the perception algorithm, and the perception performance of the marine environment is difficult to be obviously improved in a complex marine wireless communication environment; the current cooperative transmission method does not consider the effect of information carried by a transmitted data packet on improving the perception performance, is difficult to give full play to the potential gain of the cooperative transmission technology, and restricts the improvement of the perception capability of the marine environment.
Disclosure of Invention
According to the technical problems provided by the prior art, an unmanned ship cooperative transmission method facing marine environment perception is provided. The method describes the influence of data packet loss and transmission delay on the state perception performance punished based on the information age by constructing a function between the perception information age and the wireless transmission performance; designing a perception information transmission method based on unmanned ship cooperation according to the diversity gain of cooperative transmission and the mobility of the unmanned ship; and constructing a constraint optimization problem of minimizing the state perception mean square error, and performing combined optimization on the position of the unmanned ship and the association relation between the buoy and the unmanned ship.
The technical means adopted by the invention are as follows:
a marine environment perception oriented unmanned ship cooperative transmission method comprises the following steps:
s1, uniformly expressing the influence of data packet loss and transmission delay in wireless transmission on sensing performance as sensing information age, and describing the analytic relation between the sensing information age and state sensing mean square error;
s2, jointly optimizing the incidence relation between the position of the unmanned ship and the buoy-unmanned ship based on the transmission rate of the buoy-land base station perception information of unmanned ship cooperation, and minimizing the perception mean square error;
s3, determining the association relation of the buoy and the unmanned ship in polynomial time based on a heuristic algorithm of a matching theory, and determining the position of the unmanned ship based on a convex optimization theory.
Further, the wireless channel conditions are mainly distance dependent, i.e. the path loss of the link depends on the position of the drone; the horizontal position of the nth float is denoted as zn(t)＝[an(t)，bn(t)]The horizontal position of the s-th unmanned ship is denoted as zs(t)＝[as(t)，bs(t)](ii) a The distance between the nth float and the s unmanned ship is
Figure BDA0002496392860000031
The horizontal position of the terrestrial base station is denoted as z0(t)＝[a0(t)，b0(t)]The distance from the s-th unmanned ship to the land base station is
Figure BDA0002496392860000032
Further, the step S1 is specifically:
s11, adopting 0-1 integer random variable to represent whether the data packet is lost; if the land base station successfully receives the nth floating sensing data packet, xn(t) ═ 1; otherwise, xn(t) ═ 0; in particular, the amount of the solvent to be used,
Figure BDA0002496392860000033
wherein R isn(t) represents the transmission rate of the nth float to the terrestrial base station, Δ represents the duration of a sensing period, lnIndicating the perception number of the nth floatThe length of the packet;
s12, in the t sensing period, if the sensing data packet of the nth float is successfully received, the data packet is xn(t) 1, the age of the perception information available to the terrestrial base station is
Figure BDA0002496392860000034
If the nth floating sensing data packet is not successfully received, i.e.' χn(t) ═ 0, then the age of the perceptual information available to the terrestrial base stations is τn(t)＝τn(t-1) + 1; thus:
Figure BDA0002496392860000035
s13, estimating the ocean state by the land base station according to the received perception information, wherein the estimated value is as follows:
Figure BDA0002496392860000036
wherein x isn(t-τn(t)) represents the latest sensing information that the terrestrial base station can use during the t-th sensing period; further, the perceptual error is:
Figure BDA0002496392860000037
wherein A represents a system matrix, m represents an information age, vn(t) represents the measured noise of the nth float in the t sensing period; taking into account the age τ of the informationn(t) has randomness, and adopts mean square error to evaluate the perception performance,
Figure BDA0002496392860000041
wherein Q isnCovariance matrix of nth floated system noise, state-aware mean square errorRepresenting information aboutAge τn(t) is an increasing function.
Further, the step S2 is specifically:
s21, determining the wireless channel conditions of the float-unmanned ship and unmanned ship-land base station transmission links:
the power gain of the wireless channel from the nth buoy to the s unmanned ship is as follows:
Figure BDA0002496392860000043
wherein the content of the first and second substances,
Figure BDA0002496392860000044
which represents the gain of the antenna at the transmitting end,
Figure BDA0002496392860000045
which represents the gain of the antenna at the receiving end,
Figure BDA00024963928600000415
denotes the wavelength, d0A reference distance is indicated and is,
Figure BDA0002496392860000046
representing small-scale fading subject to rayleigh distribution;
the power gain of the radio channel from the s-th drone to the terrestrial base station is:
Figure BDA0002496392860000047
wherein the content of the first and second substances,
Figure BDA0002496392860000048
which represents the gain of the antenna at the transmitting end,
Figure BDA0002496392860000049
which represents the gain of the antenna at the receiving end,
Figure BDA00024963928600000410
small scale decay representing obedience to rice distributionDropping;
s22, determining the transmission rate of the perception information based on unmanned ship cooperation:
let 0-1 integer variables，n(t) representing the association of the nth buoy with the s unmanned ship; if the perception information of the nth float is forwarded to the land base station through the s unmanned ships，n(t) ═ 1; if not, then,s，n(t)＝0；
according to the wireless channel condition of the transmission link of the floating-unmanned ship, the data rate of the unmanned ship-land base station can be obtained:
Figure BDA00024963928600000411
where, B denotes a channel bandwidth,
Figure BDA00024963928600000412
representing the signal-to-noise ratio of the received signal, p representing the transmit power of the float, N0Representing the noise power;
according to the wireless channel condition of the transmission link of the floating-unmanned ship, the transmission rate of the unmanned ship-land base station can be obtained as follows:
Figure BDA00024963928600000413
wherein the content of the first and second substances,
Figure BDA00024963928600000414
representing the signal-to-noise ratio of the received signal, q representing the transmitted power of the unmanned ship; therefore, in the unmanned ship cooperative transmission, the transmission rate of the nth buoy perception information is as follows:
Figure BDA0002496392860000051
s23, jointly optimizing the incidence relation between the position of the unmanned ship and the buoy-unmanned ship, and minimizing the perception mean square error:
modeling a constrained optimization problem of perceptual mean square error minimization over T perceptual periods, as follows:
Figure BDA0002496392860000052
Figure BDA0002496392860000053
Figure BDA0002496392860000054
wherein the content of the first and second substances,
Figure BDA0002496392860000055
the constraint condition C1 indicates that one buoy can only be associated with one unmanned ship, and the constraint condition C2 is equivalent to the condition Pr { χ } of state perception convergence under intermittent observationn(t)＝0}≤1/ρ2(A) (ii) a ρ (·) represents the spectral radius of the matrix;
and solving the established problem model to obtain the unmanned ship position and the association relation between the buoy and the unmanned ship, wherein the unmanned ship position enables the perception mean square error to be minimum.
Further, S231, let τn＝Tr(ATAQn) In the objective function
Figure BDA0002496392860000057
Is rewritten as
Figure BDA0002496392860000058
Thus, the objective function can be rewritten as:
Figure BDA0002496392860000059
wherein, the preference factor in the [0,1] interval is represented;
s232, approximating the objective function to
Figure BDA00024963928600000510
The objective function of the problem P1 is approximated as
Figure BDA00024963928600000511
S233, in problem P1, only the objective function contains the time accumulation sum of T sensing periods, and the constraint conditions are all for each sensing period, so the problem P1 is decomposed into T sub-problems, and the objective function of each sub-problem is
Figure BDA00024963928600000512
The sub-problem for the tth sensing period is:
Figure BDA00024963928600000513
Figure BDA00024963928600000514
Figure BDA00024963928600000515
wherein the content of the first and second substances,
Figure BDA00024963928600000516
and is
Figure BDA00024963928600000517
The value is known at the t sensing period; because of the fact that
Figure BDA0002496392860000061
The objective function of the problem P2 depends on the probability Pr { R }n(t)≥lnA/a increases and decreases, the size of this probability being determined by the transmission rate.
Further, the step S3 is specifically:
s31, determining the association relation of the buoy and the unmanned ship in polynomial time based on a heuristic algorithm of a matching theory:
s311, enabling all the floats to float according to tau by each unmanned shipnIs sorted in descending order and the sequence L is sortedsThe position of the Mth element is marked as Is(M)；
S312, each buoy arranges all unmanned ships in a descending order according to the distance;
s313, each unmanned ship selects front M in the sequencesIndividual floats and mark selected floats ass，n(t) 1, with unselected floats marked ass，n(t)＝0；
S314, if only one float is selected by a plurality of unmanned ships, that is to say
Figure BDA0002496392860000062
Then find the unmanned ship nearest to the buoy
Figure BDA0002496392860000063
And order
Figure BDA0002496392860000064
Figure BDA0002496392860000065
Wherein n issRepresents a buoy closest to the unmanned ship s;
s315, if a plurality of floats are selected by a plurality of unmanned ships, selecting from the sequence LsStarting to execute the last step S314 by the element with the most front middle position until the association relation between all the floats and the unmanned ship is determined;
s32, determining the position of the unmanned ship based on the convex optimization theory:
s321, modeling the unmanned ship position optimization problem, and comprising the following steps:
Figure BDA0002496392860000066
Figure BDA0002496392860000067
s322, position zsAnd
Figure BDA0002496392860000068
the mutual relationship between the two is complex, and the target function is deducedTo obtain a worst-case solution to the problem, which in the worst case is:
Figure BDA0002496392860000069
Figure BDA00024963928600000610
s323, approximating the transmission rate of the floating-unmanned ship to
Figure BDA00024963928600000611
Wherein
Figure BDA00024963928600000612
Approximating the unmanned ship-to-terrestrial base station transmission rate as
Figure BDA00024963928600000613
Wherein
Figure BDA00024963928600000614
S324, dividing the problems into two situations for respective discussion according to the relative magnitude relation of the two-stage transmission rates of unmanned ship cooperative transmission;
case 1:
Figure BDA0002496392860000071
in this case
Figure BDA0002496392860000072
Question P4 is rewritten as:
Figure BDA0002496392860000073
wherein
Figure BDA0002496392860000074
Case 2:
Figure BDA0002496392860000075
in this case
Figure BDA0002496392860000076
Question P4 is rewritten as:
Figure BDA0002496392860000077
problem(s)
Figure BDA0002496392860000078
And problems with
Figure BDA0002496392860000079
The target function of the method is a two-norm, so the two problems are unconstrained convex optimization problems, and an optimal solution can be obtained through a Karush-Kuhn-Tucker condition.
S325, because the association relation between the buoy and the unmanned ship is changed due to the adjustment of the position of the unmanned ship, the position optimization problem of the unmanned ship and the association problem between the buoy and the unmanned ship are subjected to iterative solution until the difference between two adjacent objective function values is small enough, namely:
Figure BDA00024963928600000710
where ∈ denotes a sufficiently small number,
Figure BDA00024963928600000711
representing the value of the objective function for the kth iteration,
Figure BDA00024963928600000712
represents the value of the objective function of the (k-1) th iteration, wherein
Figure BDA00024963928600000713
Compared with the prior art, the invention has the following advantages:
the unmanned ship cooperative transmission method facing marine environment perception provided by the invention can reduce the influence of limited resources and path loss on perception performance and effectively improve the marine environment perception performance.
For the above reasons, the present invention can be widely applied to the fields of wireless communication and the like.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a network scenario diagram used in an embodiment of the present invention.
Fig. 3 is a flow chart of a heuristic algorithm based on a matching theory according to an embodiment of the present invention.
Fig. 4 is a flowchart of unmanned ship position optimization according to an embodiment of the present invention.
Fig. 5 is a view of a setting scene according to an embodiment of the present invention.
Fig. 6 is a comparative analysis diagram of the perceptual mean square error provided by the embodiment of the present invention.
Fig. 7 is a comparative analysis diagram of the age of the perception information provided by the embodiment of the present invention.
Fig. 8 is a comparison graph of the perceived mean square error under different numbers of unmanned ships according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1, an embodiment of the present invention provides a marine environment perception oriented unmanned ship cooperative transmission method, including the following steps:
s1, uniformly expressing the influence of data packet loss and transmission delay in wireless transmission on sensing performance as sensing information age, and describing the analytic relation between the sensing information age and state sensing mean square error;
s2, jointly optimizing the incidence relation between the position of the unmanned ship and the buoy-unmanned ship based on the transmission rate of the buoy-land base station perception information of unmanned ship cooperation, and minimizing the perception mean square error;
s3, determining the association relation of the buoy and the unmanned ship in polynomial time based on a heuristic algorithm of a matching theory, and determining the position of the unmanned ship based on a convex optimization theory.
The application of the principles of the present invention will now be described in further detail with reference to the accompanying drawings.
As shown in fig. 2, a network scenario to which the present invention is applicable is an uplink aware information backhaul network. The perception information return network mainly comprises a buoy, unmanned ships and a land base station, and comprises n buoys, s unmanned ships and a land base station. The marine environment state monitored by the buoy is an estimation object of the invention, and the environment state is an estimation object at any timeThe evolution process between is xn(t+1)＝Axn(t)+vn(t) wherein xn(t) State information acquired by the nth float in the t sensing period, vn(t) is the measured noise of the nth float in the t sensing period, the noise is zero mean Gaussian white noise, and the covariance matrix is QnAnd A is a system matrix. Initial state xn(0) Is Gaussian random variable with mean value of
Figure BDA0002496392860000093
The covariance matrix is Wn(0). For floats performing different sensing tasks, there is a certain difference in the types of sensors carried on the float, so let lnIndicating the packet length generated by the nth float.
In order to cope with the influence on the transmission rate caused by the limited transmission capability of the long-distance offshore communication and the buoy, the sensing information of the buoy is efficiently transmitted back to the land-based base station in a mode of unmanned ship cooperation. A plurality of floats deployed on the sea surface acquire the perception information of the marine environment in a coordinated mode, and the unmanned aerial vehicle is used as a relay of cooperative transmission and adopts an out-of-band full-duplex working mode. The buoy transmits the sensing data to an unmanned ship through a wireless channel, the unmanned ship forwards the received data to the land base station, and the land base station carries out state estimation on the marine environment according to the received sensing data to realize monitoring on the marine environment. In this unmanned-vessel cooperative-transmission-based state sensing, the performance of state estimation is limited by the transmission rate from the buoy to the unmanned vessel and the transmission rate from the unmanned vessel to the terrestrial base station, and the transmission rate is determined by the position of the unmanned vessel and the association relationship between the buoy and the unmanned vessel.
Preferably, the wireless channel conditions are primarily distance dependent, i.e. the path loss of the link depends on the position of the drone; the horizontal position of the nth float is denoted as zn(t)＝[an(t)，bn(t)]The horizontal position of the s-th unmanned ship is denoted as zs(t)＝[as(t)，bs(t)](ii) a The distance between the nth float and the s unmanned ship is
Figure BDA0002496392860000091
The horizontal position of the terrestrial base station is denoted as z0(t)＝[a0(t)，b0(t)]The distance from the s-th unmanned ship to the land base station is
Figure BDA0002496392860000092
Preferably, the step S1 is specifically:
s11, adopting 0-1 integer random variable to represent whether the data packet is lost; if the land base station successfully receives the nth floating sensing data packet, xn(t) ═ 1; otherwise, xn(t) ═ 0; in particular, the amount of the solvent to be used,
Figure BDA0002496392860000101
wherein R isn(t) represents the transmission rate of the nth float to the terrestrial base station, Δ represents the duration of a sensing period, lnIndicating the length of the sensing data packet of the nth float;
s12, in the t sensing period, if the sensing data packet of the nth float is successfully received, the data packet is xn(t) 1, the age of the perception information available to the terrestrial base station is
Figure BDA0002496392860000102
If the nth floating sensing data packet is not successfully received, i.e.' χn(t) ═ 0, then the age of the perceptual information available to the terrestrial base stations is τn(t)＝τn(t-1) + 1; thus:
Figure BDA0002496392860000103
s13, estimating the ocean state by the land base station according to the received perception information, wherein the estimated value is as follows:
Figure BDA0002496392860000104
wherein x isn(t-τn(t)) represents the latest sensing information that the terrestrial base station can use during the t-th sensing period; further, the perceptual error is:
Figure BDA0002496392860000105
wherein A represents a system matrix, m represents an information age, vn(t) represents the measured noise of the nth float in the t sensing period; taking into account the age τ of the informationn(t) has randomness, and adopts mean square error to evaluate the perception performance,
Figure BDA0002496392860000106
wherein Q isnCovariance matrix, state-aware mean square error, representing system noise of nth float
Figure BDA0002496392860000107
Indicating age τ on informationn(t), therefore, in step S2, it is necessary to study joint optimization of the position of the unmanned ship and the association relationship between the floating vessel and the unmanned ship to improve the cooperative transmission rate of the unmanned ship, thereby reducing the perceptual mean square error.
Preferably, the step S2 is specifically:
s21, determining the wireless channel conditions of the float-unmanned ship and unmanned ship-land base station transmission links:
the power gain of the wireless channel from the nth buoy to the s unmanned ship is as follows:
Figure BDA0002496392860000111
wherein the content of the first and second substances,
Figure BDA0002496392860000112
which represents the gain of the antenna at the transmitting end,
Figure BDA0002496392860000113
which represents the gain of the antenna at the receiving end,
Figure BDA0002496392860000114
denotes the wavelength, d0A reference distance is indicated and is,
Figure BDA0002496392860000115
representing small-scale fading subject to rayleigh distribution;
the power gain of the radio channel from the s-th drone to the terrestrial base station is:
Figure BDA0002496392860000116
wherein the content of the first and second substances,
Figure BDA0002496392860000117
which represents the gain of the antenna at the transmitting end,
Figure BDA0002496392860000118
which represents the gain of the antenna at the receiving end,
Figure BDA0002496392860000119
represents a small scale fading subject to a rice distribution;
s22, determining the transmission rate of the perception information based on unmanned ship cooperation:
let 0-1 integer variables，n(t) representing the association of the nth buoy with the s unmanned ship; if the perception information of the nth float is forwarded to the land base station through the s unmanned ships，n(t) ═ 1; if not, then,s，n(t)＝0；
according to the wireless channel condition of the transmission link of the floating-unmanned ship, the data rate of the unmanned ship-land base station can be obtained:
Figure BDA00024963928600001110
where, B denotes a channel bandwidth,
Figure BDA00024963928600001111
representing the signal-to-noise ratio of the received signal, p representing the transmit power of the float, N0Representing the noise power;
according to the wireless channel condition of the transmission link of the floating-unmanned ship, the transmission rate of the unmanned ship-land base station can be obtained as follows:
Figure BDA00024963928600001112
wherein the content of the first and second substances,
Figure BDA00024963928600001113
representing the signal-to-noise ratio of the received signal, q representing the transmitted power of the unmanned ship; therefore, in the unmanned ship cooperative transmission, the transmission rate of the nth buoy perception information is as follows:
Figure BDA00024963928600001114
s23, jointly optimizing the incidence relation between the position of the unmanned ship and the buoy-unmanned ship, and minimizing the perception mean square error:
modeling a constrained optimization problem of perceptual mean square error minimization over T perceptual periods, as follows:
Figure BDA0002496392860000121
Figure BDA0002496392860000122
Figure BDA0002496392860000123
wherein the content of the first and second substances,
Figure BDA0002496392860000124
the constraint condition C1 indicates that one buoy can only be associated with one unmanned ship, and the constraint condition C2 is equivalent to the condition Pr { χ } of state perception convergence under intermittent observationn(t)＝0}≤1/ρ2(A) (ii) a ρ (·) represents the spectral radius of the matrix;
and solving the established problem model to obtain the unmanned ship position and the association relation between the buoy and the unmanned ship, wherein the unmanned ship position enables the perception mean square error to be minimum.
S231, order τn＝Tr(ATAQn) In the objective function
Figure BDA0002496392860000126
Is rewritten as
Figure BDA0002496392860000127
Thus, the objective function can be rewritten as:
Figure BDA0002496392860000128
wherein, the preference factor in the [0,1] interval is represented; if approaching 1, it indicates that the application is insensitive to the time delay experienced by the information during transmission.
S232, approximating the objective function to
Figure BDA0002496392860000129
The objective function of the problem P1 is approximated as
Figure BDA00024963928600001210
S233, in problem P1, only the objective function contains the time accumulation sum of T sensing periods, and the constraint conditions are all for each sensing period, so the problem P1 is decomposed into T sub-problems, and the objective function of each sub-problem is
Figure BDA00024963928600001211
The sub-problem for the tth sensing period is:
Figure BDA00024963928600001212
Figure BDA00024963928600001213
Figure BDA00024963928600001214
wherein the content of the first and second substances,
Figure BDA00024963928600001215
and is
Figure BDA00024963928600001216
The value is known at the t sensing period; because of the fact that
Figure BDA00024963928600001217
The objective function of the problem P2 depends on the probability Pr { R }n(t)≥lnA/a increases and decreases, the size of this probability being determined by the transmission rate.
Preferably, the step S3 is specifically:
s31, as shown in figure 3, determining the association relation of the buoy and the unmanned ship in polynomial time based on a heuristic algorithm of a matching theory:
s311, enabling all the floats to float according to tau by each unmanned shipnIs sorted in descending order and the sequence L is sortedsThe position of the Mth element is marked as Is(M)；
S312, each buoy arranges all unmanned ships in a descending order according to the distance;
s313, each unmanned ship selects front M in the sequencesIndividual floats and mark selected floats ass，n(t) 1, with unselected floats marked ass，n(t)＝0；
S314, if only one float is selected by a plurality of unmanned ships, that is to say
Figure BDA0002496392860000131
Then find the unmanned ship nearest to the buoy
Figure BDA0002496392860000132
And order
Figure BDA0002496392860000133
Figure BDA0002496392860000134
Wherein n issRepresents a buoy closest to the unmanned ship s;
s315, if a plurality of floats are selected by a plurality of unmanned ships, selecting from the sequence LsStarting to execute the last step S314 by the element with the most front middle position until the association relation between all the floats and the unmanned ship is determined;
s32, as shown in fig. 4, determining the position of the unmanned ship based on the convex optimization theory:
s321, modeling the unmanned ship position optimization problem, and comprising the following steps:
Figure BDA0002496392860000135
Figure BDA0002496392860000136
s322, position zsAnd
Figure BDA0002496392860000137
the correlation between them is complex, and the upper bound of the objective function is derived to obtain the solution of the worst case of the problem:
Figure BDA0002496392860000138
Figure BDA0002496392860000139
s323, approximating the transmission rate of the floating-unmanned ship to
Figure BDA00024963928600001310
Wherein
Figure BDA00024963928600001311
Approximating the unmanned ship-to-terrestrial base station transmission rate as
Figure BDA00024963928600001312
Wherein
Figure BDA00024963928600001313
S324, dividing the problems into two situations for respective discussion according to the relative magnitude relation of the two-stage transmission rates of unmanned ship cooperative transmission;
case 1:
Figure BDA00024963928600001314
in this case
Figure BDA00024963928600001315
Question P4 is rewritten as:
Figure BDA00024963928600001316
wherein
Figure BDA00024963928600001317
Case 2:
Figure BDA00024963928600001318
in this case
Figure BDA00024963928600001319
Question P4 is rewritten as:
Figure BDA0002496392860000141
problem(s)
Figure BDA0002496392860000142
And problems with
Figure BDA0002496392860000143
Is object ofThe number is two norms, so the two problems are unconstrained convex optimization problems, and an optimal solution can be obtained through a Karush-Kuhn-Tucker condition.
S325, because the association relation between the buoy and the unmanned ship is changed due to the adjustment of the position of the unmanned ship, the position optimization problem of the unmanned ship and the association problem between the buoy and the unmanned ship are subjected to iterative solution until the difference between two adjacent objective function values is small enough, namely:
Figure BDA0002496392860000144
where ∈ denotes a sufficiently small number,
Figure BDA0002496392860000145
representing the value of the objective function for the kth iteration,
Figure BDA0002496392860000146
represents the value of the objective function of the (k-1) th iteration, wherein
Figure BDA0002496392860000147
In order to verify the effectiveness of the method of the present invention, the following describes the application effect of the present invention in detail with simulation.
Simulation conditions
In the simulation scenario, the perception range of the marine environment is [0, 10 ]]km×[0，10]kmAs shown in fig. 5, 20 floats and 4 unmanned vessels were deployed randomly within this perception range. The path loss parameter of the radio channel is 2.31, the mean of the rayleigh distribution is 0 and the variance is 1, and the mean of the rice distribution is 0 and the variance is 1. The reference distance is 1m, the path loss under the reference distance is 56.7dB, the Gaussian white noise power spectrum density is-87 dbm/Hz, the bandwidth is 0.2MHz, the length of a sensing period is 100ms, the maximum transmitting power of the buoy is 10mw, and the maximum transmitting power of the unmanned ship is 50 mw.
Simulation content and result analysis
The effectiveness of the method of the invention was verified by comparison with the following two transmission methods.
Comparative method 1: unmanned ships are deployed randomly, and state perception performance is improved only through optimization of a buoy-unmanned ship association relationship without considering position optimization of the unmanned ships.
Comparative method 2: and the perception information age is minimized through the joint optimization of the unmanned ship position and the association relation between the buoy and the unmanned ship.
Simulation 1: the comparative analysis is based on the state perception performance of different transmission methods.
As can be seen from fig. 6, the mean square error and the information age of the comparison method 1 are larger than those of the other two transmission methods, and this result indicates the necessity of performing joint optimization on the position of the unmanned ship and the association relationship between the buoy and the unmanned ship. In addition, as can be seen from fig. 7, although the information age achieved by the comparison method 2 is relatively small, the state-aware mean square error caused by the comparison method is larger than that of the transmission method of the present invention, and this result shows that although the information age affects the perceived mean square error, it is not appropriate for marine environment monitoring to directly evaluate the meter performance by using the information age, even if the information age is used for evaluating many other applications of the internet of things.
Simulation 2: the influence of the number of unmanned vessels on the state-aware mean square error is analyzed.
As shown in fig. 8, it can be seen from fig. 8 that the state-aware mean square error is decreasing with the number of unmanned ships, but the perceptual performance achieved by the transmission method of the present invention is always better than that achieved by the two transmission methods. The result shows that the transmission method provided by the invention has the advantages of improving the perception performance of the marine environment, and the transmission performance of perception information can be improved by deploying more unmanned ships, so that the state perception performance under the severe marine environment is improved.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (6)

1. A marine environment perception oriented unmanned ship cooperative transmission method is characterized by comprising the following steps:
s1, based on wireless channel conditions, uniformly expressing the influence of data packet loss and transmission delay in wireless transmission on sensing performance as sensing information age, and describing the analytic relationship between the sensing information age and state sensing mean square error;
s2, jointly optimizing the incidence relation between the position of the unmanned ship and the buoy-unmanned ship based on the transmission rate of the buoy-land base station perception information of unmanned ship cooperation, and minimizing the perception mean square error;
s3, determining the association relation of the buoy and the unmanned ship in polynomial time based on a heuristic algorithm of a matching theory, and determining the position of the unmanned ship based on a convex optimization theory.
2. The unmanned ship cooperative transmission method facing marine environment perception according to claim 1, wherein the wireless channel condition is mainly related to distance, namely, path loss of a link depends on unmanned ship position; the horizontal position of the nth float is denoted as zn(t)＝[an(t)，bn(t)]The horizontal position of the s-th unmanned ship is denoted as zs(t)＝[as(t)，bs(t)](ii) a The distance between the nth float and the s unmanned ship is
Figure FDA0002496392850000011
The horizontal position of the terrestrial base station is denoted as z0(t)＝[a0(t)，b0(t)]The distance from the s-th unmanned ship to the land base station is
Figure FDA0002496392850000012
3. The unmanned ship cooperative transmission method facing marine environment perception according to claim 1 or 2, wherein the step S1 is specifically as follows:
s11, adopting 0-1 integer random variable to represent whether the data packet is lost; if the land base station successfully receives the nth floating sensing data packet, xn(t) ═ 1; otherwise, xn(t) ═ 0; in particular, the amount of the solvent to be used,
Figure FDA0002496392850000013
wherein R isn(t) represents the transmission rate of the nth float to the terrestrial base station, Δ represents the duration of a sensing period, lnIndicating the length of the sensing data packet of the nth float;
s12, in the t sensing period, if the sensing data packet of the nth float is successfully received, the data packet is xn(t) 1, the age of the perception information available to the terrestrial base station is
Figure FDA0002496392850000014
If the nth floating sensing data packet is not successfully received, i.e.' χn(t) ═ 0, then the age of the perceptual information available to the terrestrial base stations is τn(t)＝τn(t-1) + 1; thus:
Figure FDA0002496392850000021
s13, estimating the ocean state by the land base station according to the received perception information, wherein the estimated value is as follows:
Figure FDA0002496392850000022
wherein x isn(t-τn(t)) represents the latest sensing information that the terrestrial base station can use during the t-th sensing period; further, the perceptual error is:
Figure FDA0002496392850000023
wherein A represents a system matrix, m represents an information age, vn(t) represents the measured noise of the nth float in the t sensing period; taking into account the age τ of the informationn(t) has randomness, and adopts mean square error to evaluate the perception performance,
Figure FDA0002496392850000024
wherein Q isnCovariance matrix, state-aware mean square error, representing system noise of nth float
Figure FDA0002496392850000025
Indicating age τ on informationn(t) is an increasing function.
4. The unmanned ship cooperative transmission method facing marine environment perception according to claim 1, wherein the step S2 is specifically:
s21, determining the wireless channel conditions of the float-unmanned ship and unmanned ship-land base station transmission links:
the power gain of the wireless channel from the nth buoy to the s unmanned ship is as follows:
Figure FDA0002496392850000026
wherein the content of the first and second substances,
Figure FDA0002496392850000027
which represents the gain of the antenna at the transmitting end,
Figure FDA0002496392850000028
which represents the gain of the antenna at the receiving end,
Figure FDA0002496392850000029
denotes the wavelength, d0A reference distance is indicated and is,
Figure FDA00024963928500000210
representing small-scale fading subject to rayleigh distribution;
the power gain of the radio channel from the s-th drone to the terrestrial base station is:
Figure FDA00024963928500000211
wherein the content of the first and second substances,
Figure FDA00024963928500000212
which represents the gain of the antenna at the transmitting end,
Figure FDA00024963928500000213
which represents the gain of the antenna at the receiving end,
Figure FDA00024963928500000214
represents a small scale fading subject to a rice distribution;
s22, determining the transmission rate of the perception information based on unmanned ship cooperation:
let 0-1 integer variables，n(t) representing the association of the nth buoy with the s unmanned ship; if the perception information of the nth float is forwarded to the land base station through the s unmanned ships，n(t) ═ 1; if not, then,s，n(t)＝0；
according to the wireless channel condition of the transmission link of the floating-unmanned ship, the data rate of the unmanned ship-land base station can be obtained:
Figure FDA0002496392850000031
where, B denotes a channel bandwidth,
Figure FDA0002496392850000032
representing the signal-to-noise ratio of the received signal, p representing the transmit power of the float, N0Representing the noise power;
according to the wireless channel condition of the transmission link of the floating-unmanned ship, the transmission rate of the unmanned ship-land base station can be obtained as follows:
Figure FDA0002496392850000033
wherein the content of the first and second substances,
Figure FDA0002496392850000034
representing the signal-to-noise ratio of the received signal, q representing the transmitted power of the unmanned ship; therefore, in the unmanned ship cooperative transmission, the transmission rate of the nth buoy perception information is as follows:
Figure FDA0002496392850000035
s23, jointly optimizing the incidence relation between the position of the unmanned ship and the buoy-unmanned ship, and minimizing the perception mean square error:
modeling a constrained optimization problem of perceptual mean square error minimization over T perceptual periods, as follows:
Figure FDA0002496392850000036
Figure FDA0002496392850000037
Figure FDA0002496392850000038
wherein the content of the first and second substances,
Figure FDA0002496392850000039
the constraint condition C1 indicates that one buoy can only be associated with one unmanned ship, and the constraint condition C2 is equivalent to the condition Pr { χ } of state perception convergence under intermittent observationn(t)＝0}≤1/ρ2(A) (ii) a ρ (·) represents the spectral radius of the matrix;
and solving the established problem model to obtain the unmanned ship position and the association relation between the buoy and the unmanned ship, wherein the unmanned ship position enables the perception mean square error to be minimum.
5. The unmanned ship cooperative transmission method facing marine environment perception according to claim 4, wherein the problem model solving process in the step S23 is as follows:
s231, order
Figure FDA00024963928500000310
In an objective function
Figure FDA00024963928500000311
Is rewritten as
Figure FDA00024963928500000312
Thus, the objective function can be rewritten as:
Figure FDA00024963928500000313
wherein, the preference factor in the [0,1] interval is represented;
s232, approximating the objective function to
Figure FDA0002496392850000041
The objective function of the problem P1 is approximated as
Figure FDA0002496392850000042
S233, in problem P1, only the objective function contains the time accumulation sum of T sensing periods, and the constraint conditions are all for each sensing period, so the problem P1 is decomposed into T sub-problems, and the objective function of each sub-problem is
Figure FDA0002496392850000043
The sub-problem for the tth sensing period is:
Figure FDA0002496392850000044
Figure FDA0002496392850000045
Figure FDA0002496392850000046
wherein the content of the first and second substances,
Figure FDA0002496392850000047
and is
Figure FDA0002496392850000048
The value is known at the t sensing period; because of the fact that
Figure FDA0002496392850000049
Problem P2 objective function with probability
Figure FDA00024963928500000410
Is increased and decreased, the size of this probability being determined by the transmission rate.
6. The unmanned ship cooperative transmission method facing marine environment perception according to claim 1, wherein the step S3 is specifically:
s31, determining the association relation of the buoy and the unmanned ship in polynomial time based on a heuristic algorithm of a matching theory:
s311, enabling each unmanned ship to float all the floats according to
Figure FDA00024963928500000411
Is sorted in descending order and the sequence is applied
Figure FDA00024963928500000412
Bit of Mth elementIs set as Is(M)；
S312, each buoy arranges all unmanned ships in a descending order according to the distance;
s313, each unmanned ship selects front M in the sequencesIndividual floats and mark selected floats ass，n(t) 1, with unselected floats marked ass，n(t)＝1；
S314, if only one float is selected by a plurality of unmanned ships, that is to say
Figure FDA00024963928500000413
Then find the unmanned ship nearest to the buoy
Figure FDA00024963928500000414
And order
Figure FDA00024963928500000415
Figure FDA00024963928500000416
Wherein n issRepresents a buoy closest to the unmanned ship s;
s315, if a plurality of floats are selected by a plurality of unmanned ships, selecting from the sequence LsStarting to execute the last step S314 by the element with the most front middle position until the association relation between all the floats and the unmanned ship is determined;
s32, determining the position of the unmanned ship based on the convex optimization theory:
s321, modeling the unmanned ship position optimization problem, and comprising the following steps:
Figure FDA0002496392850000051
Figure FDA0002496392850000052
s322, position zsAnd
Figure FDA0002496392850000053
the correlation between them is complex, and the upper bound of the objective function is derived to obtain the solution of the worst case of the problem:
Figure FDA0002496392850000054
Figure FDA0002496392850000055
s323, approximating the transmission rate of the floating-unmanned ship to
Figure FDA0002496392850000056
Wherein
Figure FDA0002496392850000057
Approximating the unmanned ship-to-terrestrial base station transmission rate as
Figure FDA0002496392850000058
Wherein
Figure FDA0002496392850000059
S324, dividing the problems into two situations for respective discussion according to the relative magnitude relation of the two-stage transmission rates of unmanned ship cooperative transmission;
case 1:
Figure FDA00024963928500000510
in this case
Figure FDA00024963928500000511
Question P4 is rewritten as:
Figure FDA00024963928500000512
wherein
Figure FDA00024963928500000513
Case 2:
Figure FDA00024963928500000514
in this case
Figure FDA00024963928500000515
Question P4 is rewritten as:
Figure FDA00024963928500000516
problem(s)
Figure FDA00024963928500000517
And problems with
Figure FDA00024963928500000518
The target function of the method is a two-norm, so the two problems are unconstrained convex optimization problems, and an optimal solution can be obtained through a Karush-Kuhn-Tucker condition.
S325, because the association relation between the buoy and the unmanned ship is changed due to the adjustment of the position of the unmanned ship, the position optimization problem of the unmanned ship and the association problem between the buoy and the unmanned ship are subjected to iterative solution until the difference between two adjacent objective function values is small enough, namely:
Figure FDA00024963928500000519
where ∈ denotes a sufficiently small number,
Figure FDA00024963928500000520
representing the value of the objective function for the kth iteration,
Figure FDA00024963928500000521
represents the value of the objective function of the (k-1) th iteration, wherein
Figure FDA00024963928500000522
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