CN101242619B - A method for simulating motion process of mobile station based on particle motion - Google Patents
A method for simulating motion process of mobile station based on particle motion Download PDFInfo
- Publication number
- CN101242619B CN101242619B CN2008100469786A CN200810046978A CN101242619B CN 101242619 B CN101242619 B CN 101242619B CN 2008100469786 A CN2008100469786 A CN 2008100469786A CN 200810046978 A CN200810046978 A CN 200810046978A CN 101242619 B CN101242619 B CN 101242619B
- Authority
- CN
- China
- Prior art keywords
- particle
- motion
- characteristic
- state
- described general
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Abstract
The invention discloses a method for simulating movement of mobile station by movement of particles, which pertains to mobile communication technology. The method includes: initializing simulation area, setting movement rules for particular particle and common particle, starting simulation procedure; calculating a new location of particular particle after a simulation period according to current state of moving and corresponding moving rule; after moving to a new location, common particle and particular particle determine whether to update their moving state according to their new location. The invention simulates movement of mobile station by movement of particles, is characterized by simplicity in realization, flexibility in configuration and authenticity, and exhibits stochastic characteristic of mobile unit's movement, individualized area characteristic and gregariousness tendency characteristic.
Description
Technical field
The present invention relates to the mobile communication technology field, particularly a kind of method with simulating motion process of mobile station based on particle motion.
Background technology
For complete analysis and new mobile communication system of emulation and corresponding protocol signaling system, set up the travelling carriage motion model and come the motion feature of travelling carriage under the accurate descriptive system real running environment to be absolutely necessary.Valuable new system, scheme or a design, even a small improvement is arranged, also all must be through theory analysis with near real emulation double verification, only under this prerequisite, just can confirm the mobile communication system of brand-new design and the feasibility and the credibility of protocol signaling system, so the foundation of motion model seems of crucial importance.
The method of research travelling carriage motion mainly comprises two classes in the mobile communication system at present: real trace is reappeared and the abstract motion model definition.So-called real trace replay method, promptly by certain approach, the movement locus of travelling carriage in the true environment is obtained in observation, it is carried out motion features such as temporal information, positional information and velocity information and restore reproduction, thereby mobile communication system is analyzed, and the advantage of this kind method is the motion feature that it can the most real reflection travelling carriage.So-called abstract motion model definition method is that the real motion track of travelling carriage is observed equally, it can realize the irrealizable function of many real trace replay methods, different with the real trace replay method is, abstract motion model definition method is not to be that the data that observe are carried out intact reproduction, but motion feature information such as temporal information, positional information and velocity information by extracting, use mathematic(al) representation to describe the motion process of travelling carriage, the motion of artificial definition travelling carriage, thus mobile communication system is analyzed and emulation.At present, the abstract motion model that often uses in to the mobile communication system analysis mainly comprises two classes: individual movement model and group movement model.The individual movement model is mainly used in the description to individual travelling carriage motion feature, and it mainly comprises road point motion model, random walk motion model, Gauss-Markovian motion model and block, city motion model or the like at random.The group movement model is mainly used in the description to the common motion feature of a group or a large amount of travelling carriage, and it mainly comprises correlation of indices motion model, reference point group motion model, column's model and chases model or the like.Abstract motion model definition method is that sizable contribution has been made in the research of mobile communications network, in a large number, new departure of mobile communications network and new algorithm carry out simulating, verifying or theory analysis about all being based on motion model, mostly be to draw owing to motion model in addition, so motion model can be described out the internal characteristics of individual motion characteristics and group movement well according to the real motion trace reduction in the actual mobile communication network.
But, all there are weak point in real trace replay method and abstract motion model definition method: at first the real trace replay method is relatively more difficult on concrete enforcement, the real trace replay method needs a large amount of time to carry out observed samples, also need the mass data that sampling obtains is handled, even obtained the real motion track of some travelling carriage, be difficult to also guarantee that the data that these samplings obtain possess generality, can not reappear all travelling carriages real motion track at any time after all; Secondly, though abstract motion model definition method can be described out the motion feature of travelling carriage, and the motion feature of describing out has generality, the scope of application is extensive, utilization flexibly, but because abstract motion model definition method all is discussion individual movement and the group movement that isolates, and more real motion model need be studied individual movement and when can show sociable characteristic, it is the contact of individual movement and group movement, this just requires motion model can show enough universal significances and statistical property, rather than simple case model, aforementioned abstract motion model for the research of these aspects then show not enough to some extent.
Summary of the invention
In order to solve existing motion of mobile terminals simulation model mobile subscriber's sociable characteristic description is truly reached the too shortcoming of machinery inadequately, the invention provides a kind of method with simulating motion process of mobile station based on particle motion, described method comprises:
Steps A: the initialization simulating area is provided with the sports rule of characteristic particle and general particle, and begins simulation process;
Step B: described characteristic particle calculates the reposition of all after dates of a simulation time step-length according to the sports rule of oneself;
Step C: described general particle calculates the reposition of all after dates of a simulation time step-length according to own current motion state and corresponding sports rule thereof;
Step D: after described characteristic particle and general particle moved to reposition respectively, described general particle was judged the motion state of whether upgrading oneself according to the reposition of oneself and the reposition of described characteristic particle, and upgraded simulation time;
Described general particle is judged the motion state of whether upgrading oneself and is comprised:
A) described general particle judges whether oneself change the free state time over to surpasses the threshold value that sets in advance, if, the attraction of then described general calculating particles oneself and all characteristic particles is if the attraction that has a characteristic particle greater than the threshold value that sets in advance, and satisfies v
Max〉=v
QSin β, then the motion state of described general particle is converted to the state of chasing by free state, and with speed v
p=v
QThe vertical direction that tan β moves along the pursuing of goal characteristic particle with its attraction maximum is to the motion of pursuing of goal characteristic particle, wherein v
QBe the speed of described general particle pursuing of goal characteristic particle, β is the angle of described general particle and the pursuing of goal characteristic particle line and the pursuing of goal characteristic particle direction of motion; Otherwise described general particle still keeps free state;
B) whether the distance between described general particle judgement oneself and pursuing of goal characteristic particle is smaller or equal to the threshold value that sets in advance, if then the motion state of described general particle is the retinue state by chasing state exchange; Otherwise described general particle judges that whether the time that oneself enters the state of chasing is greater than the time-out time of chasing that sets in advance, if, then the motion state of described general particle is a free state by chasing state exchange, otherwise described general particle still keeps chasing state;
C) described general particle generates a random number, if described random number is smaller or equal to i/N, wherein i is the capable intact number of path of the current retinue characteristic particle of described general particle, N is the path number in the set of paths of its accompanying characteristic particle, and then the motion state of described general particle is a free state by the retinue state exchange; Otherwise described general particle still keeps the retinue state.
The content of initialization simulating area specifically comprises in the described steps A: be provided with characteristic particle and general particle number, be provided with the characteristic particle motion path subclass, electric charge vector dimension is set, be provided with characteristic particle and general particle with the electric charge vector, be provided with characteristic particle and general particle's velocity distribution and dead time distribution, simulation time step-length cycle and constant and threshold value variable are set.
Described characteristic particle with the electric charge vector be specially one-component be+1 and other component all be 0 finite dimension number vector; Described general particle with the electric charge vector to be specially at least one component be that negative and other component all are 0 finite dimension number vector; Described characteristic particle with electric charge vector and described general particle with the electric charge vector between attractive.
Described simulating area is a full-mesh zone on the two dimensional surface.
The sports rule of described characteristic particle is specially the model of route at random in the described simulating area; The sports rule of described general particle is specially the point model of road at random in the described simulating area, chases motion and retinue motion.
The content of the described model of route at random is specially: described characteristic particle is chosen a motion path in the motion path set of himself, at the uniform velocity gone and stagnated behind the described motion path, and after reaching the dead time that sets in advance in dead time, again in the motion path set of himself, choose a new motion path motion, up to go himself motion path all motion paths in gathering.
The content of described road at random point model is specially: described general particle is chosen a moving target, and uniform motion is stagnated behind described moving target, and after reaching the dead time that sets in advance in dead time, reselects new moving target and to its motion.
Described content of chasing motion is specially: described general particle is attracted by certain characteristic particle, and to described characteristic particle motion.
The content of described retinue campaign is specially: the displacement that the displacement of described general particle reposition equals its accompanying characteristic particle last update position adds a random perturbation vector.
The beneficial effect of technical scheme provided by the invention is: the present invention is by the motion process with particle movement emulation travelling carriage, have characteristics such as realizing simple, flexible configuration, authenticity and randomness, but also can show stochastic behaviour, personalized zone of action characteristic and the mobile affiliation characteristic of mobile individual movement.
Description of drawings
Fig. 1 is the method flow diagram with simulating motion process of mobile station based on particle motion that the embodiment of the invention provides;
Fig. 2 is the schematic diagram of the general particle retinue of embodiment of the invention characteristic particle motion;
Fig. 3 is the general particle movement state exchange of an embodiment of the invention schematic diagram;
Fig. 4 is the schematic diagram that the general particle of the embodiment of the invention is chased particle;
Fig. 5 is 6 * 6 simulating scenes schematic diagrames that the embodiment of the invention provides.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, embodiment of the present invention is described further in detail below in conjunction with accompanying drawing.
The embodiment of the invention adopts the motion process of particle to come the motion process of emulation travelling carriage, and the particle that it adopted is characteristic particle and the general particle that has the electric charge vector, and so-called electric charge vector is the vector that is made of multiple electric charge.The electric charge vector that characteristic particle had be have one-component be+1 and other component all be 0 finite dimension number vector, be used to describe the common trick motion track of a kind of user group, promptly represent a kind of sociable characteristic; General particle is corresponding to the travelling carriage (mobile node) in the emulation, and the electric charge vector that it had is that at least one component is arranged is that negative and other component all are 0 finite dimension number vector, is used to describe the movement locus of a mobile node.Characteristic particle with electric charge vector and general particle with the electric charge vector between attractive.Characteristic particle is according to route model sport at random, and the general particle that has the identical charges vector with characteristic particle will be attracted, thereby move with characteristic particle.
Referring to Fig. 1, the embodiment of the invention provides a kind of method with simulating motion process of mobile station based on particle motion, specifically may further comprise the steps:
Step 101: select a full-mesh zone on the two dimensional surface as simulating area, and this simulating area is carried out initialization;
Simulating area is carried out initialized content specifically to be comprised: in simulating area, be provided with characteristic particle and general particle number, the subclass P of characteristic particle motion path is set
k, electric charge vector dimension n is set, be provided with characteristic particle and general particle with the electric charge vector, the VELOCITY DISTRIBUTION scope [V of characteristic particle is set
Min, V
Max] and dead time distribution [T
Pmin, T
Pmax], general particle's velocity distribution [v is set
Min, v
Max] and dead time distribution [t
Pmin, t
Pmax], the simulation time step-length cycle is set is T second and constant and threshold value variable; Wherein, electric charge vector dimension n equals the number of syndrome classification in the simulating scenes, and the dimension of the electric charge vector that had of characteristic particle and general particle equates that k the electric charge vector that characteristic particle had can be expressed as
Constant and threshold value variable specifically comprise k, α, d
Min, d
Max, T
Max, d
Thr, T
Freeze, F
Thr
Step 102: the initial position and the sports rule of characteristic particle are set, initial position, sports rule and the motion state of general particle is set;
The initial position of characteristic particle is randomly distributed in its motion path subclass P separately
kIn the starting point of a paths, the initial position of general particle is distributed in the simulating area uniformly;
(Random Trip Model, RTM), characteristic particle is at every turn at its motion path subclass P for the model of route at random in the sports rule obedience simulating area of characteristic particle
kIn choose a motion path, at the uniform velocity gone full journey with speed V, and stagnated T
PauseAfter second, again from its motion path set P
kIn choose a new motion path motion, gather P up to go motion path
kIn all motion paths; The movement rate V of characteristic particle obeys [V
Min, V
Max] interior even distribution, wherein V
Min＞0; T dead time of characteristic particle
PauseObey [T
Pmin, T
Pmax] interior even distribution, wherein T
Pmin〉=0;
The motion state of general particle specifically comprises:
A) free state: general particle self-movement separately under this state, the point model of road at random in the sports rule obedience simulating area (Random Waypoint, RWP), the general particle of free state is chosen a moving target at every turn, after target, stagnate t with speed v uniform motion
PauseSecond, reselect a new target travel again; The speed v of the general particle of free state obeys [v
Min, v
Max] interior even distribution, wherein v
Min＞0; T dead time of the general particle of free state
PauseObey [t
Pmin, t
Pmax] interior even distribution, wherein t
Pmin〉=0;
B) chase state: general particle is attracted by certain characteristic particle under this state, and to this characteristic particle motion, sports rule is obeyed the tracing movement rule;
C) retinue state: general particle is attached to characteristic particle under this state, moves with characteristic particle; The displacement that the general particle of retinue state upgrades the position at every turn equal its displacement of accompanying characteristic particle last update position add a random perturbation vector
Size obey [0, d
Thr] interior even distribution,
Direction obey even distribution in [0,2 π], as shown in Figure 2;
Step 103: it is 0 that the emulation t zero hour is set, and the current motion state that all general particles are set is a free state, and begins to carry out emulation;
The general particle of free state in simulating area according to the motion of road point model at random; Characteristic particle in simulating area according to route model sport at random;
Step 104: general particle is according to own current motion state and corresponding sports rule thereof, calculates T own reposition after second, and characteristic particle basis route Model Calculation at random goes out T oneself reposition after second;
The general particle of free state calculates T oneself reposition after second according to road point model at random; The general particle of chasing state calculates T oneself reposition after second according to the tracing movement rule; The general particle of retinue state goes out T oneself reposition after second according to route Model Calculation at random;
Step 105: general particle and characteristic particle move to the reposition of T after second that calculates separately respectively;
Step 106: general particle is judged the motion state of whether upgrading oneself according to T oneself reposition and reposition of characteristic particle after second, and the renewal simulation time;
Fig. 3 shows general particle movement state exchange schematic diagram, and its concrete transfer process is as follows:
After the general particle of free state moves to reposition, can go out the motion state of whether upgrading oneself according to following condition judgment:
1) general particle changes the time of free state over to above the threshold value T that sets in advance
Freeze
2) attraction between general particle and pursuing of goal characteristic particle is greater than the threshold value F that sets in advance
Thr
Attraction between general particle and pursuing of goal characteristic particle can calculate by following formula:
Wherein, F represents the size of attraction between general particle and the pursuing of goal characteristic particle, Q represents the electric charge vector of target signature particle, q represents the electric charge vector of general particle,＜Q, q〉inner product of expression electric charge vector Q and q, d represents the distance between general particle and the pursuing of goal characteristic particle, k and α are two positive constants that are used to regulate attraction;
3) the maximum movement speed v under the general particle free state
MaxSatisfy v
Max〉=v
QSin β, wherein v
QBe the speed of pursuing of goal characteristic particle, β is the angle of general particle and the pursuing of goal characteristic particle line and the characteristic particle direction of motion;
If the general particle of free state satisfies condition 1, promptly the free state time surpasses T
Freeze, and exist when satisfying condition 2 and 3 characteristic particle, the motion state of so general particle is converted to the state of chasing by free state, with speed v
p=v
QTan β is along the vertical direction of moving with the pursuing of goal characteristic particle of its attraction maximum, to the target signature particle movement of its attraction maximum, as shown in Figure 4, v wherein
QBe the speed of pursuing of goal characteristic particle, β is the angle of general particle and the pursuing of goal characteristic particle line and the pursuing of goal characteristic particle direction of motion; Otherwise the motion state of general particle still keeps free state;
The general particle of chasing state moves to reposition, judge own and the target signature of chasing interparticle apart from d whether smaller or equal to d
Thr, i.e. d≤d
Thr, if then general particle is chased successfully, its motion state is the retinue state by chasing state exchange; Otherwise judge that whether the time that oneself enters the state of chasing chase time-out time T greater than what set in advance
Max, if then general particle is chased failure, its motion state is a free state by chasing state exchange, otherwise its motion state still keeps chasing state;
The general particle of retinue state moves to reposition, behind the capable intact paths of the characteristic particle of promptly accompanying, generate a random number r, r obeys the even distribution in [0,1], if r satisfies r≤i/N, wherein i is the capable intact number of path of the current retinue characteristic particle of general particle, N is the path number in the set of paths of the characteristic particle that attracted, and so general particle breaks away from accompanying characteristic particle, and its motion state is a free state by the retinue state exchange; Otherwise general particle continues to keep the retinue state;
After general particle moved to reposition, simulation time was updated to t+T by t, i.e. t ← t+T; Simulation time can pass through formula t=t
0+ nT, wherein t
0Be the emulation initial value of the zero hour, n is a natural number, t in the present embodiment
0Be 0, i.e. t=nT;
Step 107: repeated execution of steps 104 finishes up to simulation process to step 106.
In order more clearly to describe the technical scheme that present embodiment provides, be illustrated below by a concrete example: as shown in Figure 5, suppose to select one 6 * 6 full-mesh zone on the two dimensional surface as simulating area, comprise horizontal vertical each 7 motion path in this zone, have 6 characteristic particles and 100 general particles, characteristic particle and general particle the dimension of electrically charged vector all be 3, the subclass of characteristic particle motion path is combined into the set of minimal paths between the moving target separately; 6 characteristic particles with the electric charge vector be respectively (1,0,0), (1,0,0), (0,1,0), (0,1,0), (0,0,1), (0,0,1); 100 general particles with the electric charge vector be (q
1, q
2, q
3), q wherein
1, q
2, q
3Equal picked at random between [1,0]; The initial position of 6 characteristic particles is in the moving target; The initial position of 100 general particles places 6 * 6 simulating area at random; Characteristic particle and general particle movement speed range are set; Constant, the emulation t=0 zero hour, simulation step length period T and emulation termination condition (concluding time T are set
Finish), the beginning simulation process:
1. all general particles are judged own motion state, calculate T own speed and displacement after second: if free state is then pressed road point model calculating at random; If chase state, chase speed v
p=v
QTan β, the vertical direction of direction for moving, wherein v along the pursuing of goal characteristic particle
QBe the speed of pursuing of goal characteristic particle, β is the angle of general particle and the pursuing of goal characteristic particle line and the pursuing of goal characteristic particle direction of motion, and displacement equals v
pT; If the retinue state, the displacement of general particle equals
Wherein
Be the displacement in its accompanying last cycle of characteristic particle,
Be the random perturbation vector,
Size obey [0, d
Thr] between even distribution,
Direction obey even distribution between [0,2 π];
2. all characteristic particles move on its motion path, every reach a moving target after, stagnate a period of time, select a new moving target and movement velocity then, along shortest path, to target travel;
3. after the each completing place of all general particles is upgraded, judge whether oneself upgrades motion state: the general particle of free state surpasses T if change the time of free state over to
Freeze, then calculate the attractions of own and all characteristic particles, if the attraction that has a characteristic particle is greater than setting threshold F
Thr, and satisfy v
Max〉=v
QSin β, then this general particle is converted to the state of chasing, and pursuing of goal is that satisfy condition and characteristic particle its attraction maximum; Chase the general particle of state, judge the distance between own and pursuing of goal characteristic particle, if distance is less than setting threshold d
Thr, then this general particle is converted to the retinue state, if surpass T
MaxDo not enter the retinue state yet, then this general particle is converted to free state; The general particle of retinue state, after characteristic particle arrival moving target, generate one [0 at random, 1] random number r, if r satisfies r≤i/N, wherein, i is that general particle is current with the capable intact number of path of characteristic particle, N is the path number in the set of paths of the characteristic particle that attracted, and then general particle transfers free state to;
4. after all particles were finished speed, position and state renewal, the renewal simulation time was t+T, repeats said process, finishes up to simulation process.
The embodiment of the invention can utilize software to realize, for example utilizes C language, assembler language to realize, corresponding software can be stored in the storage medium that can read, for example in the hard disk of computer, the internal memory.
The embodiment of the invention is by the motion process with particle movement emulation travelling carriage, have characteristics such as realizing simple, flexible configuration, authenticity and randomness, but also can show stochastic behaviour, personalized zone of action characteristic and the mobile affiliation characteristic of mobile individual movement.
The above only is preferred embodiment of the present invention, and is in order to restriction the present invention, within the spirit and principles in the present invention not all, any modification of being done, is equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (5)
1. the method with simulating motion process of mobile station based on particle motion is characterized in that, described method comprises:
Steps A: the initialization simulating area is provided with the sports rule of characteristic particle and general particle, and begins simulation process;
Step B: described characteristic particle calculates the reposition of all after dates of a simulation time step-length according to the sports rule of oneself;
Step C: described general particle calculates the reposition of all after dates of a simulation time step-length according to own current motion state and corresponding sports rule thereof;
Step D: after described characteristic particle and general particle moved to reposition respectively, described general particle was judged the motion state of whether upgrading oneself according to the reposition of oneself and the reposition of described characteristic particle, and upgraded simulation time;
Described general particle is judged the motion state of whether upgrading oneself and is comprised:
A) described general particle judges whether oneself change the free state time over to surpasses the threshold value that sets in advance, if, the attraction of then described general calculating particles oneself and all characteristic particles is if the attraction that has a characteristic particle greater than the threshold value that sets in advance, and satisfies v
Max〉=v
QSin β, then the motion state of described general particle is converted to the state of chasing by free state, and with speed v
p=v
QThe vertical direction that tan β moves along the pursuing of goal characteristic particle with its attraction maximum is to the motion of pursuing of goal characteristic particle, wherein v
QBe the speed of described general particle pursuing of goal characteristic particle, β is the angle of described general particle and the pursuing of goal characteristic particle line and the pursuing of goal characteristic particle direction of motion; Otherwise described general particle still keeps free state;
B) whether the distance between described general particle judgement oneself and pursuing of goal characteristic particle is smaller or equal to the threshold value that sets in advance, if then the motion state of described general particle is the retinue state by chasing state exchange; Otherwise described general particle judges that whether the time that oneself enters the state of chasing is greater than the time-out time of chasing that sets in advance, if, then the motion state of described general particle is a free state by chasing state exchange, otherwise described general particle still keeps chasing state;
C) described general particle generates a random number, if described random number is smaller or equal to i/N, wherein i is the capable intact number of path of the current retinue characteristic particle of described general particle, N is the path number in the set of paths of its accompanying characteristic particle, and then the motion state of described general particle is a free state by the retinue state exchange; Otherwise described general particle still keeps the retinue state.
2. the method with simulating motion process of mobile station based on particle motion as claimed in claim 1, it is characterized in that the content of initialization simulating area specifically comprises in the described steps A: be provided with characteristic particle and general particle number, be provided with the characteristic particle motion path subclass, electric charge vector dimension is set, be provided with characteristic particle and general particle with the electric charge vector, be provided with characteristic particle and general particle's velocity distribution and dead time distribution, simulation time step-length cycle and constant and threshold value variable are set.
3. the method with simulating motion process of mobile station based on particle motion as claimed in claim 2 is characterized in that, described characteristic particle with the electric charge vector be specially one-component be+1 and other component all be 0 finite dimension number vector; Described general particle with the electric charge vector to be specially at least one component be that negative and other component all are 0 finite dimension number vector; Described characteristic particle with electric charge vector and described general particle with the electric charge vector between attractive.
4. the method with simulating motion process of mobile station based on particle motion as claimed in claim 1 is characterized in that described simulating area is a full-mesh zone on the two dimensional surface.
5. the method with simulating motion process of mobile station based on particle motion as claimed in claim 1 is characterized in that the sports rule of described characteristic particle is specially the model of route at random in the described simulating area; The sports rule of described general particle is specially the point model of road at random in the described simulating area, chases motion and retinue motion;
The content of the described model of route at random is specially: described characteristic particle is chosen a motion path in the motion path set of himself, at the uniform velocity gone and stagnated behind the described motion path, and after reaching the dead time that sets in advance in dead time, again in the motion path set of himself, choose a new motion path motion, up to go himself motion path all motion paths in gathering;
The content of described road at random point model is specially: described general particle is chosen a moving target, and uniform motion is stagnated behind described moving target, and after reaching the dead time that sets in advance in dead time, reselects new moving target and to its motion;
Described content of chasing motion is specially: described general particle is attracted by certain characteristic particle, and to described characteristic particle motion;
The content of described retinue campaign is specially: the displacement that the displacement of described general particle reposition equals its accompanying characteristic particle last update position adds a random perturbation vector.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2008100469786A CN101242619B (en) | 2008-03-03 | 2008-03-03 | A method for simulating motion process of mobile station based on particle motion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2008100469786A CN101242619B (en) | 2008-03-03 | 2008-03-03 | A method for simulating motion process of mobile station based on particle motion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101242619A CN101242619A (en) | 2008-08-13 |
| CN101242619B true CN101242619B (en) | 2011-01-05 |
Family
ID=39933773
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2008100469786A Expired - Fee Related CN101242619B (en) | 2008-03-03 | 2008-03-03 | A method for simulating motion process of mobile station based on particle motion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101242619B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105824986B (en) * | 2016-03-08 | 2017-05-31 | 西安理工大学 | A kind of analogy method of two dimensional surface random point diffusion motion |
| CN106092496A (en) * | 2016-06-14 | 2016-11-09 | 上海交通大学 | For the APDSMC flow field detection method flowed across yardstick |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006068295A1 (en) * | 2004-12-21 | 2006-06-29 | Matsushita Electric Industrial Co., Ltd. | Hybrid mobile communication system comprising multi-hop-ad-hoc and circuit-switched modes |
| CN101001414A (en) * | 2006-12-29 | 2007-07-18 | 华中科技大学 | Block position managing method based on mixed mobile network |
-
2008
- 2008-03-03 CN CN2008100469786A patent/CN101242619B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006068295A1 (en) * | 2004-12-21 | 2006-06-29 | Matsushita Electric Industrial Co., Ltd. | Hybrid mobile communication system comprising multi-hop-ad-hoc and circuit-switched modes |
| CN101001414A (en) * | 2006-12-29 | 2007-07-18 | 华中科技大学 | Block position managing method based on mixed mobile network |
Non-Patent Citations (1)
| Title |
|---|
| 王芙蓉.下一代移动通信网移动性管理中位置管理研究.《华中科技大学博士学位论文》.2005, * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101242619A (en) | 2008-08-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Camara et al. | Pedestrian models for autonomous driving part ii: high-level models of human behavior | |
| Caesar et al. | nuplan: A closed-loop ml-based planning benchmark for autonomous vehicles | |
| Chen et al. | Cyber-physical system enabled nearby traffic flow modelling for autonomous vehicles | |
| Ma et al. | Estimating maximum queue length for traffic lane groups using travel times from video-imaging data | |
| CN105788335B (en) | A kind of method and terminal of navigation | |
| CN105723242B (en) | Measure the traffic speed in road network | |
| WO2018081670A4 (en) | Video data and gis mapping for traffic monitoring, event detection and change prediction | |
| Viswanathan et al. | Simulation-assisted exploration of charging infrastructure requirements for electric vehicles in urban environments | |
| Pei et al. | Velocity forecasts using a combined deep learning model in hybrid electric vehicles with V2V and V2I communication | |
| CN112818497A (en) | Traffic simulation method, traffic simulation device, computer equipment and storage medium | |
| CN109543285B (en) | Crowd evacuation simulation method and system integrating data driving and reinforcement learning | |
| CN109272032A (en) | Trip mode recognition methods, device, computer equipment and storage medium | |
| KR20130133778A (en) | Method for animating characters, with collision avoidance based on tracing information | |
| JP7054255B2 (en) | Virtual intelligence and optimization with multi-source, real-time, context-aware real-world data | |
| CN110110919A (en) | The object intercepting method of path minimum is reached based on particle swarm algorithm and region | |
| Ma et al. | An intelligence-based approach for prediction of microscopic pedestrian walking behavior | |
| CN101242619B (en) | A method for simulating motion process of mobile station based on particle motion | |
| Yang et al. | App-LSTM: Data-driven generation of socially acceptable trajectories for approaching small groups of agents | |
| CN103218489A (en) | Method for simulating vehicle personalized driving characteristic based on video sample | |
| Wenl et al. | LimSim: A long-term interactive multi-scenario traffic simulator | |
| CN114516336B (en) | Vehicle track prediction method considering road constraint conditions | |
| KR102647224B1 (en) | Trajectories embedding method for deep learning and route prediction method using the same | |
| CN115564800A (en) | Action track prediction method and device | |
| CN115148025A (en) | Traffic target track prediction method and readable storage medium | |
| Balamurugan et al. | The future of India creeping up in building a smart city: intelligent traffic analysis platform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C17 | Cessation of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20110105 Termination date: 20130303 |