CN115599120A - Unmanned aerial vehicle cluster AOA positioning track optimization method, system and device - Google Patents
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Abstract
The invention provides an optimization method, a system and a device for an AOA positioning track of an unmanned aerial vehicle cluster, and relates to the technical field of multi-unmanned aerial vehicle collaborative track planning. According to the scheme, the positioning accuracy and the positioning stability of the unmanned aerial vehicle can be rapidly improved, and then the optimal station configuration and the optimal track are provided for the unmanned aerial vehicle cluster detection ground radiation source target position.
Description
Technical Field
The invention relates to the technical field of multi-unmanned aerial vehicle collaborative track planning, in particular to an unmanned aerial vehicle cluster AOA positioning track optimization method, system and device.
Background
The positioning and tracking system can be divided into active positioning and passive positioning according to whether the positioning and tracking system emits electromagnetic waves, wherein the passive positioning can be divided into passive positioning based on target radiation and passive positioning based on external radiation sources according to different sources of the radiation sources. Because the passive positioning signal based on target radiation is not influenced by the target reflection sectional area, the passive positioning signal is already used as a main means for positioning and tracking the target of the ground radiation source.
Passive positioning can be further subdivided into: angle of arrival (AOA), received Signal Strength (RSS), time difference of arrival (TDOA), frequency difference of arrival (FDOA), and the like. The AOA positioning has the characteristics of low synchronization requirement and simple target calculation, and is widely applied in practice.
With the development of the technology, unmanned aerial vehicles have shown an increasingly greater role in a variety of fields such as exploration and rescue. However, in many scenes, only a single unmanned aerial vehicle cannot meet the positioning and tracking requirements of complex environments, multiple tasks and multiple targets. Therefore, the cooperative operation by using multiple unmanned aerial vehicles has become a development trend. However, due to the unreasonable configuration of the dynamic station arrangement of the multiple unmanned aerial vehicles, the problem that the positioning accuracy is reduced is increasingly highlighted. How to optimize the dynamic station configuration of the unmanned aerial vehicle and the track of the unmanned aerial vehicle becomes the key for solving the problems.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle cluster AOA positioning track optimization method, system and device, and aims to solve at least one technical problem in the prior art.
In order to solve the above technical problems, in a first aspect, the method for optimizing AOA positioning track of an unmanned aerial vehicle cluster includes: based on the system state, AOA measurement value and the constraint condition of a plurality of unmanned aerial vehicle current moment, through predicting calculation and obtaining ground radiation source target positioning estimated value to with FIM based on AOA measurement value as the objective function, carry out optimization calculation through solving the maximum value, obtain unmanned aerial vehicle flight azimuth angle control variable, based on unmanned aerial vehicle flight azimuth angle control variable, calculate through unmanned aerial vehicle control model, obtain the course point of each unmanned aerial vehicle next moment.
In a possible implementation manner, the unmanned aerial vehicle flight azimuth control amount refers to a deviation amount of an azimuth at the next moment and an azimuth at the current moment.
In a possible implementation mode, the AOA measurement value is obtained by measuring after the airborne AOA sensor receives the ground radiation source signal at the current time.
In a possible implementation, the system state of the drone at the current time includes the drone position, the flight direction, and the flight speed.
In one possible implementation, the constraints include: the unmanned aerial vehicle self course angle constraint, the distance upper limit constraint between the unmanned aerial vehicle and the ground radiation source target, the distance lower limit constraint between the unmanned aerial vehicle and the ground radiation source target, the collision prevention constraint between the unmanned aerial vehicles and the communication constraint between the unmanned aerial vehicles.
In a possible implementation manner, the method of the estimation calculation may be a least square method, and the ground radiation source target positioning estimation value is obtained through estimation based on AOA measurement values of a plurality of unmanned aerial vehicles on the ground radiation source target.
In a possible implementation manner, the FIM based on AOA measurement value refers to a Fisher information matrix based on AOA measurement value, and the specific construction process includes:
firstly, a plurality of unmanned aerial vehicle AOA positioning measurement models are constructed, an M unmanned aerial vehicle is assumed to be adopted to position a ground radiation source target, and the motion state of each unmanned aerial vehicle is as follows:
i＝1，2，…M
wherein, (.) T Representing a matrix transposition;
x i (k)＝(x i (k)，y i (k)) T
a position vector representing the ith drone;
representing the flight velocity vector of the ith drone;
the positions of the ground radiation source targets are as follows:
at this time, the AOA measurement value of the drone is: phi is a unit of i (x t )＝arc tan 2(x t -x i ，y t -y i )+e i ；
Wherein arctan2 represents an arctangent function with a value range of [0,2 π), e i Indicating the measurement error;
assuming that AOA positioning metrology values follow an additive gaussian distribution, the corresponding metrology vector for M drones can be expressed as:
wherein, phi (x) t )＝[φ 1 (x t ),L,φ M (x t )] T Set of vectors representing measurement values, e = [ e = [ ]) 1 ,L,e M ] T A set of vectors representing a metrology error;
assuming that the measurement errors between different drones are independent of each other, their covariance
Wherein I M Is an M-dimensional identity matrix; thus, in the case of a conventional liquid crystal display device,
can be expressed as:
i.e. obeying at phi (x) t ) To be expected, with R φ Is a normal distribution of variance.
Then constructing an error variance-distance-dependent change model, and assuming that the bandwidth of the received signal of the AOA sensor is a fixed value, correlating the measurement error with the signal-to-noise ratio of the received signal; under the condition of constant ground radiation source power and frequency, the signal-to-noise ratio is determined by the distance r between the sensor and the target i Determining; therefore, the error variance of the ith AOA sensor as a function of distance can be expressed as:
where a is the path loss factor, r 0 SNR is a critical value that positioning error just does not change with distance 0 The signal-to-noise ratio of the signal received for the current drone.
In the AOA positioning process of the unmanned aerial vehicle, the relative configuration between the unmanned aerial vehicle and the ground radiation source target is closely related to the positioning accuracy. The positioning accuracy of the unmanned aerial vehicle to the ground radiation source target under different configuration conditions can be reflected through the FIM, and the configuration corresponding to the minimum Cramer Rao boundary (CRLB) is selected as the optimal positioning configuration.
For unbiased estimate
Its cramer-mero bound can be expressed as:
wherein J represents FIM.
Assuming that the M measurements are independent of each other and obey an additive Gaussian distribution, the (i, J) th value of J can be expressed as:
wherein,
represent
Probability density function of (1):
bringing the AOA measurement into J i，j It is possible to obtain:
thus, it can be seen that J (i，j) Can be regarded as the antecedent J 1,(i,j) And the latter item J 2，(i，j) Summing;
wherein the preceding clauses
Front item J 1,(i,j) For the FIM of AOA measurements, the analytical expression can be expressed as:
to simplify the formula, let
Then canObtaining:
for the latter term
By simple derivation, can obtain
Then the user can either, for example,
can be respectively expressed as:
bonding matrix J 1,(i,j) And J 2，(i，j) The matrix J, an expanded expression of FIM based on AOA measurements, is obtained as follows:
according to the expression, the positioning error in the FIM based on the AOA measurement value is changed along with the change of the distance, the size of the matrix is related to the distance and the angle between each unmanned aerial vehicle and the ground radiation source, and the station configuration corresponding to the maximum value of the FIM determinant based on the AOA measurement value is solved, so that high-precision positioning can be rapidly realized.
In one possible implementation, the objective function may be expressed as: argmaxf (u) k+1 )＝det(J k+1 (r i ,φ i ))；
Wherein u is k+1 Representing the control quantity of each unmanned aerial vehicle flight azimuth angle at the next moment under the condition of the minimum measurement error; j. the design is a square k+1 Representing the FIM of each drone at the next time; r is a radical of hydrogen i Representing the distance between the unmanned aerial vehicle i and the ground radiation source target; phi is a i AOA measurements for drone i are shown.
Further, assuming that multiple drones adopt an angle control method, the drone control model may be expressed as:
X k+1 ＝f(X k ,u k ),k＝1,2,L,M；
wherein, X k Position vector X representing each unmanned aerial vehicle at time k k ＝[x 1 (k),L,x M (k)] T ，u k And (3) representing the control quantity of each unmanned aerial vehicle flight azimuth at the moment k: u. of k ＝[u 1 (k),u 2 (k),L u M (k)]。
Preferably, the equation of motion of the drone may be:
wherein v is 0 Indicating the flying speed, T 0 Representing the time interval of the motion control.
At each T 0 Within the range, the control quantity u of each unmanned aerial vehicle flight azimuth angle at the next moment under the condition of minimum measurement error is calculated through an optimization algorithm, such as a constrained solution differential evolution algorithm, by the ground radiation source target positioning estimation value and the FIM based on the AOA measurement value k+1 。
The differential evolution algorithm with constraint solving belongs to the prior art, has the characteristics of high calculation precision and small calculation amount, and can ensure the effectiveness and effectiveness of calculation.
In a possible implementation manner, the formula of the self-heading angle constraint of the unmanned aerial vehicle may specifically be:
||u i (k+1)-u i (k)||≤u max ；
wherein u is i (k) Unmanned aerial vehicle i self course angle u representing k moment i (k + 1) represents the self course angle u of the unmanned plane i at the moment of k + 1) max Representing the self heading angle constraint threshold of the unmanned plane.
In a possible implementation, the formula of the distance upper limit constraint between the drone and the ground radiation source target may specifically be:
wherein,
representing a ground radiation source target positioning estimated value at the moment k; x is a radical of a fluorine atom i (k + 1) represents a position vector of the unmanned aerial vehicle i at the moment of k + 1); r is h Represents an upper distance threshold, determined primarily by the signal-to-noise ratio of the on-board AOA sensor.
In a possible implementation, the formula of the distance lower limit constraint between the drone and the ground radiation source target may specifically be:
wherein,
representing a ground radiation source target positioning estimated value at the moment k; x is a radical of a fluorine atom i (k + 1) represents a position vector of the unmanned aerial vehicle i at the time of k + 1); r l Indicating a lower distance threshold.
In a possible implementation, the formula of the collision prevention constraint between the drones may specifically be:
||x i (k+1)-x j (k+1)||≥c l ；
wherein x is i (k + 1) represents a position vector of the unmanned aerial vehicle i at the moment of k + 1); x is the number of j (k + 1) represents a position vector of the unmanned aerial vehicle j at the moment of k + 1); c. C l Representing collision avoidance constraint thresholds between drones.
In a possible implementation, the formula of the inter-drone communication constraint may specifically be:
||x i (k+1)-x j (k+1)||≤c h ；
wherein x is i (k + 1) represents a position vector of the unmanned aerial vehicle i at the moment of k + 1); x is a radical of a fluorine atom j (k + 1) represents a position vector of the unmanned aerial vehicle j at the moment of k + 1); c. C h Representing an inter-drone communication constraint threshold.
In a possible implementation, the method for optimizing AOA positioning track of a cluster of drones further includes an end condition, and when the end condition is reached, the track optimization is stopped.
Further, the ending condition may specifically be determining a target position of the ground radiation source, or reaching a preset time, and the like.
In a second aspect, the present invention further provides an unmanned aerial vehicle cluster AOA positioning track optimization system, including: the data receiving module, the data processing module and the data generating module are as follows:
the data receiving module is used for receiving system state data, AOA (automatic optical inspection) measurement value data and ground radiation source signals of the unmanned aerial vehicle at the current moment;
the data processing module comprises an estimation unit, a FIM unit and a control unit:
the pre-estimation unit is used for receiving the ground radiation source signal and obtaining a ground radiation source target positioning estimation value after pre-estimation calculation;
the FIM unit is used for receiving the system state data and the AOA measurement value data of the unmanned aerial vehicle at the current moment, and performing optimization calculation for solving the maximum value by taking the FIM based on the AOA measurement value as an objective function to obtain the flight azimuth angle control quantity of the unmanned aerial vehicle;
the control unit is used for calculating through an unmanned aerial vehicle control model based on the unmanned aerial vehicle flight azimuth control quantity to generate a track point at the next moment;
and the data generation module is used for transmitting the track point at the next moment to the outside, judging whether the ending condition is met or not, and stopping track optimization if the ending condition is met.
Preferably, the end condition is that the estimated position of the target of the ground radiation source is the same at several consecutive time instants.
In a third aspect, the present invention further provides an unmanned aerial vehicle cluster AOA positioning track optimization device, which includes a processor, a memory and a bus, where the memory stores instructions and data readable by the processor, the processor is configured to call the instructions and data in the memory to execute the above unmanned aerial vehicle cluster AOA positioning track optimization method, and the bus connects the functional components to transmit information therebetween.
By adopting the technical scheme, the invention has the following beneficial effects:
according to the optimization method for the AOA positioning track of the unmanned aerial vehicle cluster, the flight azimuth angle control quantity of the unmanned aerial vehicle is obtained through optimization calculation by taking the FIM based on the AOA measurement value as a target function, the positioning precision and the positioning stability of the unmanned aerial vehicle can be rapidly improved, and the optimal station configuration and the optimal track are further provided for the unmanned aerial vehicle cluster to detect the ground radiation source target position.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description in the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an optimization method for AOA positioning track of an unmanned aerial vehicle cluster according to an embodiment of the present invention;
fig. 2 is an effect diagram of performing unmanned aerial vehicle cluster AOA positioning track optimization on a ground static radiation source target according to an embodiment of the present invention;
FIG. 3 is a comparison plot of the positioning error of FIG. 2;
fig. 4 is an effect diagram of performing unmanned aerial vehicle cluster AOA positioning track optimization on a ground dynamic radiation source target according to an embodiment of the present invention;
FIG. 5 is a comparison plot of the positioning error of FIG. 4;
fig. 6 is a diagram of an unmanned aerial vehicle cluster AOA positioning track optimization system provided in the embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The present invention will be further explained with reference to specific embodiments.
As shown in fig. 1, to solve the above technical problem, the method for optimizing AOA positioning track of unmanned aerial vehicle cluster provided by the present invention specifically includes the following steps:
Step 2, based on a plurality of AOA measurement values Z k The estimated value of the positioning of the ground radiation source target is obtained by a pre-estimation algorithm, such as a least square algorithm
Step 3, based on the ground radiation source target positioning estimated value
The constraint condition is that FIM based on the AOA measurement value is used as a track optimization target function, and the maximum value is solved through an optimization algorithm, such as a differential evolution algorithm, so that the flight azimuth angle control quantity of each unmanned aerial vehicle is obtained;
step 4, controlling quantity u based on flight azimuth angle of each unmanned aerial vehicle k+1 Calling the unmanned aerial vehicle control model to generate the next-time track point χ of each unmanned aerial vehicle i (k+1)；
Step 5, each unmanned aerial vehicle is based on track point chi i (k + 1) flying and returning to execute the step 1 until the end condition is reached.
Further, the flight azimuth angle control quantity of the unmanned aerial vehicle refers to a deviation quantity of an azimuth angle at the next moment and an azimuth angle at the current moment.
Furthermore, the AOA measurement value is obtained by measuring after receiving the ground radiation source signal at the current moment through an AOA sensor on board the unmanned aerial vehicle.
Further, the system state of the unmanned aerial vehicle at the current moment comprises the position, the flight direction and the flight speed of the unmanned aerial vehicle.
Further, the constraint conditions include: the method comprises the following steps of self course angle constraint of the unmanned aerial vehicle, distance upper limit constraint between the unmanned aerial vehicle and a ground radiation source target, distance lower limit constraint between the unmanned aerial vehicle and the ground radiation source target, collision prevention constraint between the unmanned aerial vehicles and communication constraint between the unmanned aerial vehicles.
Further, the FIM based on the AOA measurement value refers to a Fisher information matrix based on the AOA measurement value, and the specific construction concept is as follows:
firstly, a plurality of unmanned aerial vehicle AOA positioning measurement models are constructed, the ground radiation source target is positioned by adopting M unmanned aerial vehicles, and the motion state of each unmanned aerial vehicle is as follows:
i＝1，2，…M；
wherein, (. Cndot.) T Representing a matrix transposition;
x i (k)＝(x i (k)，y i (k)) T
a position vector representing an ith drone;
representing the flight velocity vector of the ith drone;
the positions of the ground radiation source targets are as follows:
at this time, the AOA measurement value of the drone is: phi is a unit of i (x t )＝arc tan2(x t -x i ，y t -y i )+e i ；
Wherein arctan2 represents an arctangent function with a value range of [0,2 π), e i Indicating the measurement error;
assuming that AOA positioning measurement values follow an additive gaussian distribution, the corresponding measurement vector for M drones can be expressed as:
wherein, phi (x) t )＝[φ 1 (x t ),L,φ M (x t )] T Set of vectors representing measurement values, e = [ e = [ ]) 1 ,L,e M ] T A set of vectors representing a metrology error;
assuming that the measurement errors between different drones are independent of each other, their covariance
Wherein I M Is an M-dimensional identity matrix; thus, the number of the first and second electrodes,
can be expressed as:
i.e. obeying at phi (x) t ) To expect, with R φ Is a normal distribution of variance.
Then constructing an error variance-distance-dependent change model, and assuming that the bandwidth of the received signal of the AOA sensor is a fixed value, correlating the measurement error with the signal-to-noise ratio of the received signal; under the condition of constant power and frequency of a ground radiation source, the signal-to-noise ratio is determined by the distance r between the sensor and the target i Determining; therefore, the relationship of the error variance of the ith AOA sensor with distance can be expressed as:
where a is the path loss factor, r 0 SNR is a critical value that positioning error just does not change with distance 0 The signal-to-noise ratio of the signal received for the current drone.
In the AOA positioning process of the unmanned aerial vehicle, the relative configuration between the unmanned aerial vehicle and the ground radiation source target is closely related to the positioning accuracy. The positioning accuracy of the unmanned aerial vehicle to the ground radiation source target under different configuration conditions can be reflected through FIM, and the configuration corresponding to the minimum Cramer Rao boundary (CRLB) is selected as the optimal positioning configuration.
For an unbiased estimate, its cramer-perot bound can be expressed as:
wherein J represents FIM.
Assuming that the M measurements are independent of each other and obey an additive Gaussian distribution, the (i, J) th value of J can be expressed as:
wherein,
to represent
Probability density function of (1):
bringing the AOA measurement into J i，j It is possible to obtain:
thus, it can be seen that J (i，j) Can be regarded as the antecedent J 1,(i,j) And the following item J 2，(i，j) Summing;
wherein the preceding item
Front item J 1,(i,j) For the FIM of AOA measurements, the analytical expression can be expressed as:
to simplify the formula, let
Then it is possible to obtain:
for the latter term
Through simple derivation, can obtainTo
Then the user can either, for example,
can be respectively expressed as:
bonding matrix J 1,(i,j) And J 2，(i，j) The matrix J, i.e., the expanded expression of FIM based on AOA measurements, is obtained as follows:
according to the expression, the positioning error in the FIM based on the AOA measurement value is changed along with the change of the distance, the size of the matrix is related to the distance and the angle between each unmanned aerial vehicle and the ground radiation source, and the station configuration corresponding to the maximum value of the FIM determinant based on the AOA measurement value is solved, so that high-precision positioning can be rapidly realized.
Further, the objective function may be expressed as: arg max f (u) k+1 )＝det(J k+1 (r i ,φ i ))；
Wherein u is k+1 Representing the control quantity of each unmanned aerial vehicle flight azimuth angle at the next moment under the condition of the minimum measurement error; j is a unit of k+1 Representing the FIM of each drone at the next time; r is a radical of hydrogen i Representing the distance between the unmanned aerial vehicle i and the ground radiation source target; phi is a unit of i AOA measurements for drone i are shown.
Further, assuming that a plurality of drones adopt an angle control method, the drone control model may be expressed as:
X k+1 ＝f(X k ,u k ),k＝1,2,L,M；
wherein, X k Position vector X representing each unmanned aerial vehicle at time k k ＝[x 1 (k),L,x M (k)] T ，u k The control quantity of each unmanned aerial vehicle flight azimuth at the current moment is represented as follows: u. of k ＝[u 1 (k),u 2 (k),L u M (k)]。
Preferably, the equation of motion of the drone may be:
wherein v is 0 Indicating the flying speed, T 0 Representing the time interval of the motion control.
At each T 0 Within the range, the control quantity u of each unmanned aerial vehicle flight azimuth angle at the next moment under the condition of minimum measurement error is calculated and obtained through an optimization algorithm, such as a difference evolution algorithm with constraint calculation, by the ground radiation source target positioning estimated value and the FIM based on the AOA measurement value k+1 。
The differential evolution algorithm with constraint solving belongs to the prior art, has the characteristics of high calculation precision and small calculation amount, and can ensure the effectiveness and effectiveness of calculation.
Further, the formula of the self-heading angle constraint of the unmanned aerial vehicle may specifically be:
||u i (k+1)-u i (k)||≤u max ；
wherein u is i (k) Unmanned aerial vehicle i self course angle u representing k moment i (k + 1) represents the self course angle u of the unmanned plane i at the moment of k + 1) max And representing the self course angle constraint threshold of the unmanned aerial vehicle.
Further, the formula of the distance upper limit constraint between the drone and the ground radiation source target may specifically be:
wherein,
representing a ground radiation source target positioning estimated value at the moment k; x is the number of i (k + 1) represents a position vector of the unmanned aerial vehicle i at the moment of k + 1); r h Represents an upper distance threshold, determined primarily by the signal-to-noise ratio of the on-board AOA sensor.
Further, the formula of the distance lower limit constraint between the unmanned aerial vehicle and the ground radiation source target may specifically be:
wherein,
representing a ground radiation source target positioning estimated value at the moment k; x is a radical of a fluorine atom i (k + 1) represents a position vector of the unmanned aerial vehicle i at the time of k + 1); r l Indicating a lower distance threshold.
Further, the formula of the collision prevention constraint between the unmanned aerial vehicles may specifically be:
||x i (k+1)-x j (k+1)||≥c l ；
wherein x is i (k + 1) represents a position vector of the unmanned aerial vehicle i at the moment of k + 1); x is the number of j (k + 1) represents a position vector of the unmanned aerial vehicle j at the moment of k + 1); c. C l Representing collision avoidance constraint thresholds between drones.
Further, the formula of the communication constraint between the drones may specifically be:
||x i (k+1)-x j (k+1)||≤c h ；
wherein x is i (k + 1) represents a position vector of the unmanned aerial vehicle i at the moment of k + 1); x is a radical of a fluorine atom j (k + 1) represents the position vector of the unmanned plane j at the moment of k + 1); c. C h Representing an inter-drone communication constraint threshold.
Further, the ending condition may be determining a target position of the ground radiation source or reaching a preset time.
The first embodiment is as follows:
carrying out AOA positioning simulation on a ground static radiation source target by using 3 unmanned aerial vehicles:
suppose the initial position of the ground static radiation source target is x t ＝[0,0] T ；
The initial state of the unmanned aerial vehicle is respectively as follows: x is a radical of a fluorine atom 1 (1)＝[-9200,-5000] T 、x 2 (1)＝[-10000,-5000] T 、x 3 (1)＝[-10000,-5800] T ；
The initial time direction of each unmanned aerial vehicle is: the machine head points to true north (pi/2 direction in the coordinate system);
the initial time flight speed of each unmanned aerial vehicle is: v. of 0 ＝10m/s；
Sampling time interval T =0.1s;
the simulation time is as follows: for 100s.
The self course angle of the unmanned plane is restrained: u. of max ＝15°；
And (3) distance upper limit constraint between the unmanned aerial vehicle and the ground radiation source target: r is h ＝15km；
The distance between the unmanned aerial vehicle and the ground radiation source target is limited by the lower limit: r is l ＝0.3km；
Communication constraint between unmanned aerial vehicles: c. C h ＝15km；
Collision prevention between unmanned aerial vehicles retrains: c. C l ＝40m。
Through simulation calculation, the method of the embodiment of the invention performs an effect diagram of optimizing the AOA positioning track of the unmanned aerial vehicle cluster on the ground static radiation source target, as shown in FIG. 2: the abscissa represents the x-axis position coordinate, and the ordinate represents the y-axis position coordinate, in meters. The real position of the static target is shown as a triangle in the figure; the estimated values of the three unmanned aerial vehicles to the target position at each moment are shown as hollow circles in the figure; the relative configuration effect between cluster platform that three unmanned aerial vehicle of initial moment constituteed and the target is relatively poor, represent three unmanned aerial vehicle with distance between the target is far away, and three unmanned aerial vehicle with contained angle between the target line is approximately zero, but as time goes on, optimizes through the flight path, each unmanned aerial vehicle constantly adjust its with relative configuration between the target to make three unmanned aerial vehicle more and more be close to the target, three unmanned aerial vehicle with contained angle between the target line is bigger and bigger, just three unmanned aerial vehicle are more and more close to the estimated value of target location every moment the true position of static target.
Fig. 3 shows the error comparison of the positioning of the static targets in two ways when three unmanned aerial vehicle clusters fly to the static targets in a fixed configuration and the flight path optimization is performed to fly to the static targets by using the scheme: the abscissa represents the positioning time in seconds; the ordinate represents the mean square error between the real position of the target and the positioning estimation position; the mean square error of the positioning of the target by the two modes at the initial moment is within the range of 3000-5000, the error (shown as a circle in the figure) after the track optimization is rapidly reduced along with the time, the descending amplitude is far higher than the positioning error under the fixed configuration condition, for example, when the simulation time reaches the position of 30 seconds, the positioning error under the optimized condition of the scheme is close to 0, and the positioning error under the fixed configuration condition is about 2000, so that the track optimization effect of the scheme is proved to be effective.
Example two:
carrying out AOA positioning simulation on the ground dynamic radiation source target by 3 unmanned aerial vehicles:
assuming the initial position of the ground static radiation source target as x t ＝[0,0] T ；
The initial state of the unmanned aerial vehicle is respectively as follows: x is the number of 1 (1)＝[-9200,-5000] T 、x 2 (1)＝[-10000,-5000] T 、x 3 (1)＝[-10000,-5800] T ；
The initial time direction of each unmanned aerial vehicle is: the handpiece points to true north (direction in the coordinate system);
the initial time flight speed of each unmanned aerial vehicle is: v. of 0 ＝10m/s；
Sampling time interval T =0.1s;
the simulation time is as follows: for 100s.
The self course angle of the unmanned plane is restrained: u. of max ＝15°；
And (3) distance upper limit constraint between the unmanned aerial vehicle and the ground radiation source target: r h ＝15km；
The distance between the unmanned aerial vehicle and the ground radiation source target is limited by the lower limit: r l ＝0.3km；
Communication constraint between unmanned aerial vehicles: r l ＝0.3km；
Collision prevention between unmanned aerial vehicles retrains: c. C l ＝40m。
Through simulation calculation, the method of the embodiment of the invention performs an effect diagram of optimizing the AOA positioning track of the unmanned aerial vehicle cluster on the ground dynamic radiation source target, as shown in FIG. 4: the abscissa represents the x-axis position coordinate, and the ordinate represents the y-axis position coordinate, in meters. The real position of the dynamic target is shown as a star in the figure; the estimated values of the three unmanned aerial vehicles to the target position at each moment are shown as hollow circles in the figure; the relative configuration effect between cluster platform that three unmanned aerial vehicles of inception moment are constituteed and the target is relatively poor, represent three unmanned aerial vehicles with distance between the target is far away, and three unmanned aerial vehicles with contained angle between the target line is near zero, but as time goes on, optimizes through the flight path, each unmanned aerial vehicle constantly adjust its with relative configuration between the target to make three unmanned aerial vehicles more and more be close to the target, three unmanned aerial vehicles with contained angle between the target line is bigger and bigger, just three unmanned aerial vehicles are more and more close to the estimated value of target position every moment the true position of dynamic target.
Fig. 5 shows the error comparison of positioning of the dynamic targets in two ways when three unmanned aerial vehicle clusters fly to the dynamic targets in a fixed configuration and when flight path optimization is performed to fly to the dynamic targets by using the scheme: the abscissa represents the positioning time in seconds; the ordinate represents the mean square error between the real position of the target and the positioning estimation position; the mean square error of the initial time and the positioning of the target in the two modes is about 10000, the error (shown as a circle in the figure) after the track optimization is rapidly reduced along with the time, the descending amplitude is far higher than the positioning error under the fixed configuration condition, for example, when the simulation time reaches the position of 60 seconds, the positioning error under the optimized condition of the scheme is close to 0, and the positioning error under the fixed configuration condition is about 6000, so that the track optimization effect of the scheme is proved to be effective.
On the other hand, based on the same inventive concept, the present invention further provides an unmanned aerial vehicle cluster AOA positioning track optimization system, as shown in fig. 6, including: the data receiving module, the data processing module and the data generating module:
the data receiving module is used for receiving system state data, AOA (automatic optical inspection) measurement value data and ground radiation source signals of the unmanned aerial vehicle at the current moment;
the data processing module comprises an estimation unit, an FIM unit and a control unit:
the pre-estimation unit is used for receiving the ground radiation source signal and obtaining a ground radiation source target positioning estimation value after pre-estimation calculation;
the FIM unit is used for receiving the system state data and the AOA measurement value data of the unmanned aerial vehicle at the current moment, and performing optimization calculation for solving the maximum value by taking the FIM based on the AOA measurement value as an objective function to obtain the flight azimuth angle control quantity of the unmanned aerial vehicle;
the control unit generates a track point at the next moment through unmanned aerial vehicle control model calculation based on the unmanned aerial vehicle flight azimuth angle control quantity;
and the data generation module is used for transmitting the track point at the next moment to the outside, judging whether the ending condition is met or not, and stopping track optimization if the ending condition is met.
Further, the end condition is to determine the position of the ground radiation source target, for example, the estimated values of the ground radiation source target location at several consecutive time instances are the same.
In yet another aspect, the present invention further provides an unmanned aerial vehicle cluster AOA positioning track optimization device, which includes a processor, a memory and a bus, where the memory stores instructions and data readable by the processor, and the processor is configured to call the instructions and data in the memory to execute the unmanned aerial vehicle cluster AOA positioning track optimization method described above, and the bus connects the functional components to transmit information therebetween.
In yet another embodiment, the present solution can also be implemented by a device, which can include corresponding modules for performing each or several steps in the above embodiments. The modules may be one or more hardware modules specifically configured to perform the respective steps, or implemented by a processor configured to perform the respective steps, or stored within a computer-readable medium for implementation by a processor, or by some combination.
The processor performs the various methods and processes described above. For example, method embodiments in this scenario may be implemented as a software program tangibly embodied on a machine-readable medium, such as a memory. In some embodiments, some or all of the software program may be loaded and/or installed via memory and/or a communication interface. When the software program is loaded into memory and executed by a processor, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the processor may be configured to perform one of the methods described above by any other suitable means (e.g., by means of firmware).
The device may be implemented using a bus architecture. The bus architecture may include any number of interconnecting buses and bridges depending on the specific application of the hardware and the overall design constraints. The bus connects together various circuits including one or more processors, memories, and/or hardware modules. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, external antennas, and the like.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (enhanced ISA) bus, or the like, and may be classified as an address bus, a data bus, a control bus, or the like.
In conclusion, according to the technical scheme of the invention, the problem of fast positioning of the ground radiation source by the unmanned aerial vehicle cluster can be well solved, the optimal calculation of solving the maximum value is carried out by taking the FIM based on the AOA measurement value as an objective function, the AOA positioning track optimization method of the unmanned aerial vehicle cluster based on the FIM criterion is constructed, and the positioning precision and the positioning stability can be improved in a short time.
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 (10)
1. An optimization method for an AOA positioning track of an unmanned aerial vehicle cluster is characterized by comprising the following steps: based on the system state, AOA measurement value and the constraint condition of a plurality of unmanned aerial vehicle current moment, through predicting calculation and obtaining ground radiation source target positioning estimated value to with FIM based on AOA measurement value as the objective function, carry out optimization calculation through solving the maximum value, obtain unmanned aerial vehicle flight azimuth angle control variable, based on unmanned aerial vehicle flight azimuth angle control variable, calculate through unmanned aerial vehicle control model, obtain the course point of each unmanned aerial vehicle next moment.
2. The method according to claim 1, wherein the unmanned aerial vehicle flight azimuth control amount is a deviation amount of an azimuth at the next moment from an azimuth at the current moment.
3. The method according to claim 1, wherein the AOA measurement value is obtained by measuring after an onboard AOA sensor receives a ground radiation source signal at the current time.
4. The method of claim 1, wherein the system state of the drone at the current time includes drone position, flight direction, and flight speed.
5. The method of claim 1, wherein the constraints comprise: the unmanned aerial vehicle self course angle constraint, the distance upper limit constraint between the unmanned aerial vehicle and the ground radiation source target, the distance lower limit constraint between the unmanned aerial vehicle and the ground radiation source target, the collision prevention constraint between the unmanned aerial vehicles and the communication constraint between the unmanned aerial vehicles.
6. The method of claim 1, wherein the FIM based on AOA measurement values refers to a Fisher information matrix based on AOA measurement values.
7. The method of claim 6, wherein the objective function is: arg max f (u) k+1 )＝det(J k+1 (r i ,φ i ) In which u) is k+1 Representing the control quantity of each unmanned aerial vehicle flight azimuth angle at the next moment under the condition of the minimum measurement error; j. the design is a square k+1 FIM representing AOA measurement value of each unmanned aerial vehicle at the next moment; r is i Representing the distance between the unmanned aerial vehicle i and the ground radiation source target; phi is a unit of i AOA measurements for drone i are shown.
8. The utility model provides an unmanned aerial vehicle cluster AOA fixes a position track optimization system which characterized in that includes: the data receiving module, the data processing module and the data generating module:
the data receiving module is used for receiving system state data, AOA measurement value data and ground radiation source signals of the unmanned aerial vehicle at the current moment;
the data processing module comprises an estimation unit, a FIM unit and a control unit:
the pre-estimation unit is used for receiving the ground radiation source signal and obtaining a ground radiation source target positioning estimation value after pre-estimation calculation;
the FIM unit is used for receiving the system state data and the AOA measurement value data of the unmanned aerial vehicle at the current moment, and performing optimization calculation for solving the maximum value by taking the FIM based on the AOA measurement value as an objective function to obtain the flight azimuth angle control quantity of the unmanned aerial vehicle;
the control unit generates a track point at the next moment through unmanned aerial vehicle control model calculation based on the unmanned aerial vehicle flight azimuth angle control quantity;
and the data generation module is used for transmitting the track point at the next moment to the outside, judging whether the ending condition is met or not, and stopping track optimization if the ending condition is met.
9. The system of claim 8, wherein the end condition is determining a ground radiation source target position.
10. An unmanned aerial vehicle cluster AOA (automatic optical inspection) positioning track optimization device is characterized by comprising a processor, a memory and a bus, wherein the memory stores instructions and data which can be read by the processor, the processor is used for calling the instructions and the data in the memory to execute the unmanned aerial vehicle cluster AOA positioning track optimization method according to any one of claims 1 to 7, and the bus is connected with all functional components to transmit information.
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