CN111343023A - Distributed cooperative decision-making method for motion control of self-adaptive mobile network node - Google Patents

Distributed cooperative decision-making method for motion control of self-adaptive mobile network node Download PDF

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CN111343023A
CN111343023A CN202010130516.3A CN202010130516A CN111343023A CN 111343023 A CN111343023 A CN 111343023A CN 202010130516 A CN202010130516 A CN 202010130516A CN 111343023 A CN111343023 A CN 111343023A
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CN111343023B (en
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夏威
周卓阳
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University of Electronic Science and Technology of China
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Abstract

The invention belongs to the field of signal processing, in particular to a distributed cooperative decision method for motion control of a self-adaptive mobile network node; in the case of multiple targets with mobility, the decision of the node changes from moment to moment according to the position of the node and the position of the target. The decision between the node and the neighbor node is fused and exchanged in a diffusion cooperation mode, and is combined with the decision of the node to perform distributed decision, so that more accurate decision is obtained. Compared with the existing self-adaptive mobile network with a plurality of targets, the application scene is more complex and the complex situation of dynamic change of the positions of the targets and the situation that the motion states of the nodes have different choices are considered; meanwhile, a distributed method is used for making motion state decision, so that the survival rate of the nodes is effectively increased while the predation success rate is ensured.

Description

Distributed cooperative decision-making method for motion control of self-adaptive mobile network node
Technical Field
The invention belongs to the field of signal processing, and particularly relates to a distributed cooperative decision in an adaptive mobile network, in particular to a distributed cooperative decision for the adaptive mobile network with various motion states.
Background
In recent years, due to the rapid development of modern digital signal processing and wireless communication technologies and the wide application of wireless sensor devices, data processing of networks becomes a future development direction, wherein a distributed method is widely researched due to the fact that information sharing among nodes is fully utilized, and the distributed method has low calculation complexity, good expandability and good anti-interference performance. Compared with a centralized method, the distributed method can more accurately transmit and process data, and can be widely applied to actual scenes such as parameter estimation, target tracking and the like.
In recent years, some researchers have studied Distributed Estimation methods, for example, in "dominant F S, SayedA H.Di dispersion LMS Strategies for Distributed Estimation [ J. IEEE Transactionson Signal processing g,2010,58(3): 1035-; the distributed method is widely applied to network signal processing due to simple structure and low calculation complexity; but only for the case where the topology of the network is static and does not change, parameter estimation is performed for one target.
Furthermore, the scholars optimize the combination coefficient of the distributed dispersive LMS method aiming at the condition that the network has a plurality of targets, and can achieve better estimation performance under the condition that the network has a plurality of targets. For example, a real-time updated method for calculating distributed combination coefficients applied to multi-target parameter estimation is proposed in "Chen J, Richard C, S eye A H. Difsion LMS Over Multi task Networks [ J ]. IEEETransactions on Signal processing 2015,63(11): 2733-; however, the method does not consider the situation of the change of the topological structure of the network and the situation of tracking the moving target, does not control the moving state of the network node, and does not relate to distributed decision making.
Still further, some researchers apply the distributed method to the Adaptive mobile network, for example, in "Tu S, SayedA h. mobile Adaptive Networks [ J ]. IEEE Journal of Selected topocs in signaling processing,2011,5(4):649 + 664" the distributed diffusible LMS method is applied to the Adaptive mobile network with real-time change in the topology of the network to track a single target; the application scene of the proposed method is single, only a single static target (predator) exists in the space, the maneuvering performance of the target is not considered, only a single target exists in the space, and the situation of multiple targets is not considered; the motion control method is simple and only considers that the node only has a tracked motion state, and does not relate to distributed decision making.
Still further, there are trainees who have expanded the motion situation of the adaptive mobile network, for example, in "TuS, Sa ed a h. tracking is latent of mobile adaptive networks [ C ].2010 Conference record of h e form Fourth adaptive communication on Signals, Systems and computers, performance group, CA, USA: ieee.2010: 698-one 702", two forms of targets including predators and predators, and network nodes are in a motion state of tracking or escaping according to the position of the target of motion; however, the application scenario is that only a single predator target and a predator target are in space, the situation of a plurality of predator targets and predator targets is not considered, and distributed decision making is not involved.
Still further, some scholars have proposed a distributed Decision method in the adaptive mobile network, for example, a Decision method applied to the distributed network is proposed in "Kh awatmi S, Zoubir a M, safe a h. decentralized Decision-Making Over Multi-Task Netwo rks [ J ]. Signal Processing,2019,160: 229-; the node carries out decision making by utilizing information sharing of the node and surrounding neighbor nodes, but the decision making process is independent and does not influence each other at each node, decision making results of other nodes are not considered, and the decision making method can cause decision making errors due to insufficient information; and a mechanism of nodes in the self-adaptive mobile network, which can not realize the target avoidance. In practice, the motion state of the node of the adaptive mobile network for tracking or avoiding the target may change, for example, when the predator is too close to the node, the node changes from the motion state of target tracking to the motion state of avoiding; therefore, it is necessary and reasonable to adopt a distributed collaborative decision-making based method to realize the adaptive and continuous motion control of the network nodes.
Disclosure of Invention
The invention aims to provide a distributed cooperative decision method for controlling the motion of nodes of an adaptive mobile network, which is used for solving the problem of performing distributed cooperative decision under the condition that the nodes in the adaptive mobile network have different motion states.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the distributed cooperative decision method for the motion control of the self-adaptive maneuvering network node is characterized by comprising the following steps of:
step 1: setting N nodes in the network, and automatically clustering according to different estimation targets;
for node k at the current time i, defining a matrix E:
Figure BDA0002395655510000021
wherein [ ·]l,kThe elements of the ith row and kth column of the matrix in brackets are indicated,
Figure BDA0002395655510000022
is the domain of node k;
all of (E)]l,k Node 1 is in the same cluster as node k, labeled as
Figure BDA0002395655510000023
And all in the neighborhood of node k do not belong to
Figure BDA0002395655510000031
The nodes of (a) form a set, label
Figure BDA0002395655510000032
The symbol | represents the number of elements in the set;
step 2: will be provided with
Figure BDA0002395655510000033
Grouping according to the estimated values of the nodes;
is defined as the size
Figure BDA0002395655510000034
The matrix F of (2):
Figure BDA0002395655510000035
wherein, wm(i)、wn(i) Respectively representing the estimation values of a certain target at the time i in the node m and the node n, wherein zeta > 0 is a preset threshold value;
if [ F ]]m,nIf 1, the node m and the node n are determined to be the same, belong to the same group, and will be taken according to the value of the matrix F
Figure BDA0002395655510000036
Grouping to obtain G groups:
Figure BDA0002395655510000037
and step 3: calculating estimates for common targets within each group
Figure BDA0002395655510000038
And type of estimation
Figure BDA0002395655510000039
Figure BDA00023956555100000310
Figure BDA00023956555100000311
Wherein the content of the first and second substances,
Figure BDA00023956555100000312
meaning rounding to the nearest integer, sn(i) Representing the estimation type of the estimation target of the node n at the time i;
and 4, step 4: carrying out information sharing of estimation values between adjacent nodes, arranging the estimation values of all groups together with the estimation values of the nodes to obtain a matrix Yk(i)、Zk(i)：
Figure BDA00023956555100000313
Figure BDA00023956555100000314
Wherein, the matrix Yk(i) A position estimate vector containing all predators within node k, having a dimension of
Figure BDA00023956555100000315
Zk(i) A position estimate vector comprising all predators within node k, having a dimension of
Figure BDA00023956555100000316
Representing the number of different estimates of predators and predators, respectively, that are present at node k; t is t1,...,t n1 st to nth groups indicating that the estimated type is predator among the G groups, e1,...,e m1 st to m-th groups representing estimated types as predators among the G groups;
and 5: determining a motion state decision vector g based on the position of the node and the included estimatek(i)：
If there is no estimated value of predators in node k, node k is in an avoidance state:
Figure BDA0002395655510000041
wherein the content of the first and second substances,
Figure BDA0002395655510000042
distance between node k and nearest predator, symbol [. ]]:,lVector, x, representing the l-th column of the matrixk(i) Denotes the position of node k, ReIs the threat radius of the predator;
if there is no estimated predator in node k, node k is in a tracking state:
gk(i)＝[0，1]T；
if two types of estimates are contained in node k:
Figure BDA0002395655510000043
step 6: carrying out information interaction on the decision vector of the node and the decision of the neighbor node to obtain a decision intermediate value iota:
Figure BDA0002395655510000044
wherein, [ g ]l(i)]1Deciding vector g for node ll(i) Epsilon is a preset threshold;
and 7: and carrying out distributed cooperative decision updating on the decision vector of the node containing the two types of estimation values, wherein if the node k contains the two types of estimation values:
Figure BDA0002395655510000045
and 8: and performing motion control according to the decision vector:
Figure BDA0002395655510000046
wherein the content of the first and second substances,
Figure BDA0002395655510000047
to track the tracking speed of the target:
Figure BDA0002395655510000048
Figure BDA0002395655510000049
is a constant, representing the speed,
Figure BDA00023956555100000410
Figure BDA0002395655510000051
to hide inEscape speed outside the threat radius of the avoidance objective:
Figure BDA0002395655510000052
Figure BDA0002395655510000053
is a constant, representing velocity;
Figure BDA0002395655510000054
Figure BDA0002395655510000055
to avoid escape speed within the threat radius of the target:
Figure BDA0002395655510000056
the invention has the beneficial effects that:
the distributed cooperative decision method for the motion control of the self-adaptive mobile network node provided by the invention has the following advantages:
1. the method provided by the invention can perform distributed, simultaneous, self-adaptive and continuous motion control on the self-adaptive mobile network node under the condition of tracking a single or a plurality of targets;
2. the method provided by the invention can implement distributed, simultaneous, adaptive and continuous cooperative decision in the motion process on the adaptive mobile network node under the condition of tracking a single or a plurality of targets;
3. the distributed cooperative decision method provided by the invention can effectively reduce decision errors possibly caused by insufficient information of a single node;
4. according to the invention, the decision result is shared among the neighbor nodes through a diffusion mechanism, and the local decision result and the decision results of the neighbor nodes around are combined to obtain a cooperative decision result.
Drawings
Fig. 1 is a flowchart of a distributed cooperative decision method when an adaptive mobile network node moves.
FIG. 2 is a diagram of the location of predators, and the network at an initial time in an embodiment.
FIG. 3 is a diagram of the location of predators and predators during movement at a network node in an embodiment.
FIG. 4 is a diagram showing simulation results in the example.
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
the embodiment provides a distributed cooperative decision method for motion control of a self-adaptive mobile network node, and the flow of the method is shown in fig. 1; the method comprises the following specific steps:
step 1: setting N nodes in the network, wherein each node selects one target from a plurality of targets to estimate the position of the target, and automatically clustering according to different estimated targets;
a matrix E of dimension N × is defined as:
Figure BDA0002395655510000061
wherein [ ·]l,kThe elements of the ith row and kth column of the matrix in brackets are indicated,
Figure BDA0002395655510000062
the method is a set composed of all neighbor nodes which directly communicate with the node k in the field of the node k;
and, define all of the groups [ E ]]l,kAll nodes l 1 belong to the same cluster as node k
Figure BDA0002395655510000063
Meaning that node l is a neighbor node of node k and node l and node k have the same estimation target at the current moment;
step 2: within node k, will
Figure BDA0002395655510000064
Grouping according to the estimated values of the nodes;
define one
Figure BDA0002395655510000065
The matrix F of (1) is:
Figure BDA0002395655510000066
wherein [ ·]m,nThe elements of the m-th row and n-th column of the matrix in brackets are indicated,
Figure BDA0002395655510000067
all of the neighbors in the neighborhood of node k do not belong to
Figure BDA0002395655510000068
Represents the number of elements in the set, |, a column vector w of dimension M × 1 (M represents the spatial dimension)m(i) A column vector w representing an estimated value of a target at time i and node mn(i) Showing the estimated value of a certain target in the time i and the node n, wherein zeta > 0 is a preset threshold value and a smaller threshold value used for judging whether the targets of the node m and the node n are the same or not;
if [ F ]]m,nIf 1, the node m and the node n are regarded as the same target, i.e. belong to the same group, and the value of the matrix F will be taken as the basis
Figure BDA0002395655510000069
Grouping is carried out, and the grouping is divided into G groups:
Figure BDA00023956555100000610
the targets estimated within each group are different from each other;
and step 3: within node k, estimates and estimate types for common targets within each group are computed:
Figure BDA00023956555100000611
Figure BDA00023956555100000612
wherein the content of the first and second substances,
Figure BDA00023956555100000613
denotes taking the nearest integer, sn(i) Indicating the estimation type of the estimation target of node n at time i,
Figure BDA00023956555100000614
Figure BDA00023956555100000615
respectively represent
Figure BDA00023956555100000616
The estimation type and corresponding estimation value of the group;
the invention assumes that the node knows the estimation type of the estimation target: predators(s) that the node wishes to avoid pursuingk(i) 0) or predators(s) whose node wishes to prey onk(i) 1), a uniform weighted combination method is applied in each group to obtain the estimation type and estimation value of the common target in each group;
and 4, step 4: carrying out information sharing of estimation values among the adjacent nodes, and arranging the estimation values of all groups together with the estimation values of the nodes in the node k;
Figure BDA0002395655510000071
Figure BDA0002395655510000072
wherein the dimensions are respectively
Figure BDA0002395655510000073
Matrix Y ofk(i)、Zk(i) Respectively storing all predators and predators in the node kIs determined by the position estimation vector of (a),
Figure BDA0002395655510000074
representing the number of different estimates of predators and predators, respectively, that are present at node k; t is t1,...,t n1 st to nth groups indicating that the estimated type is predator among the G groups, e1,...,em1 st to m-th groups representing estimated types as predators among the G groups;
all will be
Figure BDA0002395655510000075
Is estimated value of
Figure BDA0002395655510000076
Together with if sk(i) 0 matrix Y of estimates of node k itselfk(i) All will be similar
Figure BDA0002395655510000077
Is estimated value of
Figure BDA0002395655510000078
Together with if sk(i) The matrix Z is formed by the estimated values of 1 node k itselfk(i) (ii) a A plurality of estimates of predator and predator targets are obtained within node k;
and 5: within each node, a preliminary motion state decision vector g is determined based on the node's location and the included estimatesk(i)；
For node k:
if there is no estimated value of predators in node k, node k is in an avoidance state:
Figure BDA0002395655510000079
wherein the content of the first and second substances,
Figure BDA00023956555100000710
distance between node k and nearest predator, symbol [. ]]:,lTo representVector of the l-th column of the matrix, xk(i) Denotes the position of node k, ReIs the threat radius of the predator;
if there is no estimated predator in node k, node k is in a tracking state:
gk(i)＝[0，1]T；
if two types of estimates are contained in node k:
Figure BDA0002395655510000081
preliminary decision vector gk(i) Represents the preliminary motion state of the node k, 0 represents the tracking state, and 1 represents the avoidance state;
step 6: sharing the information of the decision of the node and the decision of the neighbor node to obtain a decision intermediate value iota:
Figure BDA0002395655510000082
wherein, [ g ]l(i)]1Deciding vector g for node ll(i) Epsilon is a preset threshold value and is used for judging whether the device is in an escape state or not;
and 7: and carrying out distributed cooperative decision making to determine a final decision in the node containing two forms of target estimation values:
Figure BDA0002395655510000083
when the node determines that the motion state is escape according to the state of the neighbor node and the initial decision of the node is inconsistent, the node does not adopt decision determination of the node in order to ensure the safety of the node, and adopts a result obtained by using distributed cooperative decision;
and 8: and performing motion control according to the final decision vector:
Figure BDA0002395655510000084
wherein the content of the first and second substances,
Figure BDA0002395655510000085
to track the tracking speed of the target:
Figure BDA0002395655510000086
Figure BDA0002395655510000087
is a constant, representing the speed,
Figure BDA0002395655510000088
Figure BDA0002395655510000089
to escape speed outside the threat radius of the evasive target:
Figure BDA00023956555100000810
Figure BDA00023956555100000811
is a constant, representing velocity;
Figure BDA00023956555100000812
Figure BDA00023956555100000813
to avoid escape speed within the threat radius of the target:
Figure BDA0002395655510000091
the closer the distance the greater the speed.
The feasibility and the superiority of the method of the invention are illustrated by applying the method of the invention to a basic model of the Adaptive mobile network proposed in the document "TuS, Sayed A H. Mobile Adaptive Networks [ J ]. IEEE Journal of Selected Topicsin Signal processing,2011,5(4): 649-.
Simulation conditions are as follows:
an adaptive mobile network containing 80 nodes is used, the topological structure of the adaptive mobile network is changed along with the time, and each node selects 7 nodes closest to the node as neighbor nodes within the range of 5 radiuses of the node at each moment. There are 2 targets in the space, including 3 predators who wish to move to the nodal target, and 10 predators who are in constant linear motion. At each moment the predator estimates the node in space using the LMS method and chooses the node that is tracked closest to it.
Predators apply a state space model:
xi+1＝Φxi+Γξi,
Figure BDA0002395655510000092
wherein the content of the first and second substances,
Figure BDA0002395655510000093
in 2-dimensional space, the position and the velocity of the x axis and the position and the velocity of the y axis respectively, T is the time interval of sampling between each iteration and is taken as 0.1, and the noise ξiIs white gaussian noise with a variance of 0.02.
Setting parameters: threshold ζ of grouping is 0.1, threat radius R of predator e5, threshold epsilon of decision 0.2, tracking speed
Figure BDA0002395655510000094
Escape velocity of state of motion 2 is
Figure BDA0002395655510000095
The node is considered to be predated when the distance between the node and the nearest predator is less than 1, and the predator is considered to be successfully predated by the node when the distance between the node and the nearest predator is greater than 3 nodes within the range of the safe distance of 2 of the predator. Average survival rate of nodes RsAverage predation success rate R of sum nodepIs defined as
Figure BDA0002395655510000096
Figure BDA0002395655510000097
Wherein L is the number of independent experiments,
Figure BDA0002395655510000098
the number of nodes still surviving in the l independent experiment,
Figure BDA0002395655510000099
the number of predators who had been successfully ingested in the i independent experiments.
The movement of the network and the object over time is shown in fig. 3. When a predator approaches to the node, part of the nodes begin to escape and share the decision of the predator to the neighbor nodes around the predator, and the nearby nodes are determined to be in the escaping state through distributed cooperative decision making. Some nodes in this process are successfully ingested because of their proximity to the predator. Some nodes are in the state of tracking the predators, and after one predator is successfully predated, the nodes go to track the next predator, and after a period of time, the predators are successfully predated by the nodes.
The mean of the node survival rates among 100 simulation experiments is shown in fig. 4. Three algorithms of different forms of distributed decision making, information sharing without estimation values between adjacent nodes and information sharing without decision making between adjacent nodes are respectively carried out. The graph and the following table show that the survival rate of the nodes is effectively increased by applying the distributed cooperative decision method for network node motion control provided by the invention under the condition of ensuring that the predation success rate is not changed.
Distributed decision making Decision-free information sharing Estimate-free information sharing
Average predation rate 99.8％ 99.7％ 99.8％
While the invention has been described with reference to specific embodiments, any feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise; all of the disclosed features, or all of the method or process steps, may be combined in any combination, except mutually exclusive features and/or steps.

Claims (1)

1. The distributed cooperative decision method for the motion control of the self-adaptive maneuvering network node is characterized by comprising the following steps of:
step 1: setting N nodes in the network, and automatically clustering according to different estimation targets;
for node k at the current time i, defining a matrix E:
Figure FDA0002395655500000011
wherein [ ·]l,kThe elements of the ith row and kth column of the matrix in brackets are indicated,
Figure FDA0002395655500000012
is the domain of node k;
all of (E)]l,kNode 1 is in the same cluster as node k, labeled as
Figure FDA0002395655500000013
And all in the neighborhood of node k do not belong to
Figure FDA0002395655500000014
The nodes of (a) form a set, label
Figure FDA0002395655500000015
The symbol | represents the number of elements in the set;
step 2: will be provided with
Figure FDA0002395655500000016
Grouping according to the estimated values of the nodes;
is defined as the size
Figure FDA0002395655500000017
The matrix F of (2):
Figure FDA0002395655500000018
wherein, wm(i)、wn(i) Respectively representing the estimation values of a certain target at the time i in the node m and the node n, wherein zeta > 0 is a preset threshold value;
if [ F ]]m,nIf 1, the node m and the node n are determined to be the same, belong to the same group, and will be taken according to the value of the matrix F
Figure FDA0002395655500000019
Grouping to obtain G groups:
Figure FDA00023956555000000110
and step 3: calculating estimates for common targets within each group
Figure FDA00023956555000000111
And type of estimation
Figure FDA00023956555000000112
Figure FDA00023956555000000113
Figure FDA00023956555000000114
Wherein the content of the first and second substances,
Figure FDA00023956555000000115
meaning rounding to the nearest integer, sn(i) Representing the estimation type of the estimation target of the node n at the time i;
and 4, step 4: carrying out information sharing of estimation values between adjacent nodes, arranging the estimation values of all groups together with the estimation values of the nodes to obtain a matrix Yk(i)、Zk(i)：
Figure FDA0002395655500000021
Figure FDA0002395655500000022
Wherein, the matrix Yk(i) A position estimate vector containing all predators within node k, having a dimension of
Figure FDA0002395655500000023
Zk(i) A position estimate vector comprising all predators within node k, having a dimension of
Figure FDA0002395655500000024
Figure FDA0002395655500000025
Representing the number of different estimates of predators and predators, respectively, that are present at node k; t is t1,...,tn1 st to nth groups indicating that the estimated type is predator among the G groups, e1,...,emRepresenting groups 1 to m indicating that the estimated type is predator among the G groups;
and 5: determining a motion state decision vector g based on the position of the node and the included estimatek(i)：
If there is no estimated value of predators in node k, node k is in an avoidance state:
Figure FDA0002395655500000026
wherein the content of the first and second substances,
Figure FDA0002395655500000027
Figure FDA0002395655500000028
distance between node k and nearest predator, symbol [. ]]:,lVector, x, representing the l-th column of the matrixk(i) Denotes the position of node k, ReIs the threat radius of the predator;
if there is no estimated predator in node k, node k is in a tracking state:
gk(i)＝[0，1]T；
if two types of estimation values are contained in node k
Figure FDA0002395655500000029
Determines whether in tracking or evasive state:
Figure FDA00023956555000000210
step 6: sharing the information of the decision of the node and the decision of the neighbor node to obtain a decision intermediate value iota:
Figure FDA00023956555000000211
wherein, [ g ]l(i)]1Deciding vector g for node ll(i) Epsilon is a preset threshold;
and 7: and carrying out distributed cooperative decision updating on the decision vector of the node containing the two types of estimation values, wherein if the node k contains the two types of estimation values:
Figure FDA0002395655500000031
and 8: and performing motion control according to the decision vector:
Figure FDA0002395655500000032
wherein the content of the first and second substances,
Figure FDA0002395655500000033
to track the tracking speed of the target:
Figure FDA0002395655500000034
Figure FDA0002395655500000035
is a constant and represents the speed,
Figure FDA0002395655500000036
Figure FDA0002395655500000037
to escape speed outside the threat radius of the evasive target:
Figure FDA0002395655500000038
Figure FDA0002395655500000039
is constant, represents velocity;
Figure FDA00023956555000000310
Figure FDA00023956555000000311
to avoid escape speed within the threat radius of the target:
Figure FDA00023956555000000312
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