CN114216463A - Path optimization target positioning method and device, storage medium and unmanned equipment - Google Patents
Path optimization target positioning method and device, storage medium and unmanned equipment Download PDFInfo
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- CN114216463A CN114216463A CN202111300175.0A CN202111300175A CN114216463A CN 114216463 A CN114216463 A CN 114216463A CN 202111300175 A CN202111300175 A CN 202111300175A CN 114216463 A CN114216463 A CN 114216463A
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The invention discloses a path optimization target positioning method and device, a storage medium and unmanned equipment, which can realize the rapid and accurate estimation and positioning of targets (such as foreign object violation invasion, personnel search and rescue and the like) of a single unmanned aerial vehicle/unmanned ship in regional security through a novel proposed path optimization technology. The method mainly solves the problems of positioning speed and positioning precision of the target by a path optimization strategy, and solves the problem of local optimal traps in the traditional gradient method path optimization technology.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle application, in particular to a path optimization target positioning method and device, a storage medium and unmanned equipment.
Background
Unmanned aerial vehicles and unmanned boats are now widely used in practical applications such as regional security, target search and rescue, military monitoring and the like. In the applications, the accurate acquisition of the position and motion state information of the specific target is more urgent, and particularly, the requirement on the positioning accuracy is more strict in emergency, security and military scenes, so that the information of the target is required to be determined quickly and accurately.
The currently popular target positioning technologies are mainly divided into active and passive technologies. As the name suggests, the active positioning technology is to actively detect and send out relevant signals to determine the position information of the target through intelligent equipment carried by the unmanned aerial vehicle/boat, and on the contrary, the passive positioning method is to estimate the key information of the position of the target only by collecting the relevant information or the sent signals of the target. The key difference is whether or not a special signal of some kind needs to be actively emitted. Positioning by azimuth-of-Arrival (AOA) is a classical passive object positioning method, which is widely used in military and civilian fields. When AOA positioning is carried out, the position, the speed and other information of the target are determined according to the triangular geometrical relationship measured at different positions for many times. In other words, with the AOA measurement, the target state quantity can be estimated by the tracking estimator algorithm. This means that the measurements of the sensors and the location distribution of the sensors play a crucial role in target positioning, and therefore path optimization of the AOA sensor-equipped drone/boat is important.
Compared with other positioning technologies, AOA measurement can be directly obtained through a camera, a sonar and a radar receiver, the AOA positioning technology has no special requirements on the surrounding environment, the system complexity is low, high-precision positioning can be realized, the positioning ambiguity is avoided, and the method is widely applied to various positioning fields, especially the fields of rapid tracking, search and rescue and the like of regional invasive targets. However, the AOA target positioning with high speed and high precision needs to carry out relevant optimization on the observation position. Currently, the technology for optimizing the sensor-mounted path mainly focuses on traversal search, and path optimization for improving the target estimation accuracy is less, and the technologies mainly applied are gradient descent method and point searching method optimization. The existing method has the following main problems to be solved: 1. the existing area patrol security technology mostly does not consider the improvement of target positioning precision and only aims at traversing areas; 2. the problem of local optimal traps exists in the existing gradient path optimization technology which takes improvement of target estimation precision as a primary target; 3. the target estimation accuracy of the existing method is not improved fast enough.
Therefore, it is particularly important to design a new path optimization strategy to achieve rapid improvement of the estimation accuracy of the intrusion target.
Disclosure of Invention
The application aims to provide a path optimization target positioning method and device, a storage medium and unmanned equipment, and the method and the device can be used for realizing the rapid and accurate estimation and positioning of targets (such as foreign matter violation invasion, personnel search and rescue and the like) of a single unmanned aerial vehicle/unmanned ship in regional security through the provided novel path optimization technology. The method mainly solves the problems of positioning speed and positioning precision of the target by a path optimization strategy, and solves the problem of local optimal traps in the traditional gradient method path optimization technology.
In order to achieve the purpose of the application, the technical scheme provided by the application is as follows:
first aspect
The application provides a path optimization target positioning method, which comprises the following steps:
step 1: initializing a position estimation value and a covariance matrix value of a target;
step 2:
in the primary calculation: acquiring an AOA sensor measurement value based on the position of the unmanned equipment and current position information of the unmanned equipment, operating a pseudo-linear Kalman filtering algorithm by combining the position estimation value and the covariance matrix value obtained in the step 1, and solving to obtain a new position estimation value and a new covariance matrix value and updating;
and when the loop calculation is carried out: the method comprises the steps of obtaining an AOA sensor measurement value based on the position of the unmanned equipment and current position information of the unmanned equipment, operating a pseudo-linear Kalman filtering algorithm by combining a position estimation value and a covariance matrix value obtained in the last calculation, and solving to obtain a new position estimation value and a new covariance matrix value and updating;
and step 3: the method comprises the following steps:
step 3.1, calculating an initial step length;
and 3.2, solving an optimal observation position of the unmanned equipment based on an optimization algorithm of gradient descent by combining the data output in the step 3.1 and the step 2.
3.3, determining a plurality of other possible optimal observation positions according to the position in the step 3.2, and solving covariance matrixes corresponding to all possible optimal position points;
and 3.4, comparing all the positions to finally determine the optimal observation position.
And 4, step 4: and 3, moving the unmanned equipment to the optimal observation position obtained in the step 3, skipping to the step 2, and circularly calculating until the covariance matrix value reaches the requirement or the user terminates.
Wherein, unmanned equipment is unmanned aerial vehicle or unmanned ship.
Wherein the initial step size is in the order of hundreds of meters.
In step 3.3, a plurality of other possible optimal observation positions are determined by using a point searching method, and 3 points opposite to the optimal observation position determined in step 3.2 and at positions of 90 degrees left and right are additionally selected as optimal observation position points.
Second aspect of the invention
The application provides a path optimization target positioning device, which comprises the following units:
a first unit for initializing a position estimation value and a covariance matrix value of a target;
a second unit to:
in the primary calculation: the method comprises the steps of obtaining an AOA sensor measurement value based on the position of the unmanned equipment and current position information of the unmanned equipment, operating a pseudo-linear Kalman filtering algorithm by combining a position estimation value and a covariance matrix value obtained by a first unit, solving to obtain a new position estimation value and a new covariance matrix value, and updating;
and when the loop calculation is carried out: the method comprises the steps of obtaining an AOA sensor measurement value based on the position of the unmanned equipment and current position information of the unmanned equipment, operating a pseudo-linear Kalman filtering algorithm by combining a position estimation value and a covariance matrix value obtained in the last calculation, and solving to obtain a new position estimation value and a new covariance matrix value and updating;
a third unit to: calculating an initial step length; based on an optimization algorithm of gradient descent, combining the initial cloth length, a new position estimation value and a covariance matrix value, an AOA sensor measurement value and current position information of the unmanned equipment, and solving the optimal observation position of the first unmanned equipment; determining additional multiple other possible optimal observation positions according to the determined optimal observation position, and solving covariance matrixes corresponding to all the possible optimal observation positions; comparing all the positions to finally determine the optimal observation position; the drone is moved to the optimal viewing position that is ultimately determined.
Wherein, unmanned equipment is unmanned aerial vehicle or unmanned ship.
Wherein the initial step size is in the order of hundreds of meters.
The third unit determines a plurality of other possible optimal observation positions by adopting a point searching method, and additionally selects 3 points opposite to the optimal observation position of the first unmanned equipment and at positions of 90 degrees left and right as optimal observation position points.
Third aspect of the invention
There is provided a storage medium having stored therein at least one instruction, at least one program, set of codes, or set of instructions that is loaded and executed by a processor to implement the above-described path optimization goal localization method.
Fourth aspect of the invention
The present application provides a terminal device, which is characterized in that the terminal device includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the above-mentioned path optimization target location method.
Compared with the prior art, the method has the advantages that the method improves the step length self-adaption on the basis of the existing gradient path optimization method, designs the double insurance of additional point searching, and aims to find the optimal observation position for improving the target estimation precision more quickly. Therefore, the prior art is enabled to obviously improve the speed of improving the target estimation precision, and the estimation of the motion position and the motion speed of the invading target by a single unmanned aerial vehicle/boat area is realized quickly and accurately.
Drawings
FIG. 1 is a schematic flow chart of the method of the present application;
fig. 2 is a schematic view illustrating target tracking and azimuth measurement of a single drone in the embodiment of the present application;
FIG. 3 is a schematic diagram illustrating two problems of the prior art gradient method path optimization technique;
FIG. 4 is a diagram illustrating the effect of different adaptive step sizes on path optimization in finding a global optimum point;
FIG. 5 is a graph of real-time gradient values using different values;
FIG. 6 is a diagram illustrating additional point determination in a point-finding strategy;
FIG. 7 is a graph showing a comparison of simulation results of the first case using the proposed solution and the prior fixed step gradient method;
fig. 8 is a graph comparing the simulation effect of the proposed solution and the prior fixed step gradient method in the second case.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The invention is described in further detail below with reference to the figures and specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when used in this specification the singular forms "a", "an" and/or "the" include "specify the presence of stated features, steps, operations, elements, or modules, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It will be understood that when an element is referred to herein as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The invention mainly solves the problem of rapid and accurate positioning of the target position in the tracking and reconnaissance of the invasion target in the regional security patrol of the unmanned aerial vehicle (or the underwater unmanned ship). Compared with the existing method and technology, the strategy estimation algorithm is more stable, the path optimization technology is more efficient than the existing gradient method, and the target estimation precision can be improved more quickly.
In the invention, firstly, a pseudo-linear Kalman filtering target estimation algorithm based on target azimuth measurement is introduced, and then the problem of local traps in the existing path optimization technology based on a gradient method is analyzed in detail. Secondly, the patent provides an improved unmanned aerial vehicle/unmanned ship path optimization method combining gradient trial calculation step length self-adaptation, and an extra path point searching algorithm is designed to make up for the problem that the accuracy of a gradient path optimization method is poor when target estimation is inaccurate at the initial stage of target estimation. By using the method, the accurate positioning of the target can be quickly realized by adopting a single unmanned aerial vehicle/unmanned ship.
As shown in fig. 1, a method for positioning a path optimization target provided by the present application includes the following steps:
step 1: initializing a position estimation value and a covariance matrix value of a target;
the position estimation value of the target is initialized, the covariance matrix value is initialized, and the above two initial states are used in step 2.
Step 2:
the primary calculation process comprises the following steps: acquiring an AOA sensor measurement value based on the position of the unmanned equipment and current position information of the unmanned equipment, operating a pseudo-linear Kalman filtering algorithm by combining the position estimation value and the covariance matrix value obtained in the step 1, and solving to obtain a new position estimation value and a new covariance matrix value and updating;
and (3) a cyclic calculation process: the method comprises the steps of obtaining an AOA sensor measurement value based on the position of the unmanned equipment and current position information of the unmanned equipment, operating a pseudo-linear Kalman filtering algorithm by combining a position estimation value and a covariance matrix value obtained in the last calculation, and solving to obtain a new position estimation value and a new covariance matrix value and updating;
and step 3: the method comprises the following steps:
step 3.1, calculating an initial step length; in the step, the method for resolving the initial step length in the gradient algorithm provided by the patent is adopted to calculate the initial step length and input the initial step length into the next algorithm; by adopting the step length self-adaptive algorithm provided by the patent, the whole program is compiled to be used as a part of the next algorithm.
And 3.2, solving an optimal observation position of the unmanned equipment based on an optimization algorithm of gradient descent by combining the data output in the step 3.1 and the step 2. In the step, a gradient descent optimization algorithm is operated to obtain an optimal observation position of the unmanned aerial vehicle.
3.3, determining a plurality of other possible optimal observation positions according to the position in the step 3.2, and solving covariance matrixes corresponding to all possible optimal position points;
and 3.4, comparing all the positions to finally determine the optimal observation position.
And 4, step 4: and 3, moving the unmanned equipment to the optimal observation position obtained in the step 3, skipping to the step 2, and circularly calculating until the covariance matrix value reaches the requirement or the user terminates.
The following provides a specific embodiment of a drone/drone path optimization target positioning method.
In this embodiment, an unmanned aerial vehicle/unmanned surface vehicle equipped with an Angle-of-arrival (AOA) sensor is adopted, the unmanned aerial vehicle/unmanned surface vehicle adopts a moving speed with a constant scalar value, and the airborne sensor can acquire different azimuth measurement values of the target at discrete time k of 1,2, and 3 …, and further resolve the target state by combining an analytic geometry method and a corresponding estimation algorithm. The angle measurement schematic is shown in fig. 2.
Specifically, the ideal direction angle measurement mathematical model is as follows:
wherein p isk＝[pxk,pyk]And rk＝[rxk,ryk]Respectively the target at time k and the position of the drone itself. | | - | is the Euclidean norm, tan-1Is a four quadrant arctangent function. Inevitably, unmanned aerial vehicle receives factors influences such as wind, engine vibrations when flying, contains great gaussian noise in the airborne sensor measurationing, and can influence target positioning accuracy. Specifically, the azimuth angle measurement model containing noise is as follows:
where n and m are each zero mean and variance
And
independent additive white gaussian noise. The position and the speed of the unmanned aerial vehicle can be acquired by airborne navigation equipment and are definitely known. Observing the direction angle measurement model formula, the nonlinear relation between the angle measurement and the target state can be found. The objective of the patent is to obtain accurate position and velocity estimation values of a plurality of targets from the noisy nonlinear measurement information.
This patent takes two-dimensional environment as an example, and this technique can be promoted to the relevant problem in three-dimensional space equally. The state vector for the target in the two-dimensional environment space is defined as
Dynamic model satisfaction of the objective
Where T represents the time step between discrete time instants k and k + 1. q. q.skRepresenting the system process noise. QkIs the covariance matrix of the process noise, Wk|kRepresenting the updated estimated covariance matrix. Thus, the pseudo-linear kalman filter (PLKF) algorithm is as follows:
first, state prediction:
xk|k-1＝Uxk-1|k-1
Wk|k-1＝UWk-1|k-1UT+Qk-1
secondly, measurement gain calculation:
thirdly, updating the final state by combining state prediction and measurement gain:
xk|k＝xk|k-1+kkyk
the purpose of path optimization is to obtain more effective measurement information by changing the position of the unmanned aerial vehicle, so that the accuracy of target state estimation is rapidly improved. At time k, the drone obtains the azimuth measurement of the target, executes an estimation, path optimization algorithm and changes the flight path to reach a better observation position before obtaining the next measurement.
Firstly, in order to improve the target estimation precision, a trace of a covariance matrix which represents the target positioning precision in the PLKF estimator is selected as an optimization objective function, namely, the precision of the target positioning estimation is improved along with the reduction of the covariance trace. The mathematical model is as follows:
J(rk)＝tr(Wk|k) (5.6)
where tr (·) represents the matrix traceablility. To minimize (5.6), many mathematical optimization algorithms can be used, such as gradient descent. Flight path point satisfaction at each moment
rk+1-rk＝-vT·ΔJ (5.7)
Wherein, vT represents the flight distance of the drone within a certain time, and Δ J is the gradient of the cost function based on the estimated covariance matrix. In a two-dimensional problem, the gradient can be expressed as:
the hypothetical location measurement needed can be solved by estimating the state in real time, wherein δ represents a distance, which is a test distance in the gradient method, and can be approximated to a local gradient when the distance is close to 0. The following patent pair J (r)xk+ δ) solution an example solution is made. First, can obtain
Wherein(1) And (3) 1 st and 3 rd elements of the preceding vector. Further, substituting the result in (5.9) into the expression (5.4) can solve for hxk+δAnd xixk+δAnd then according to
And solving the required result:
J(rxk+δ)＝tr(Wk|k,xk+δ) (5.11)
similar other three variables can be obtained, so that the optimal position point at the next moment is solved according to the formulas (5.8) and (5.7), and the path optimization in the improvement of the target tracking estimation is realized.
Since the calculation of the next-moment optimal path point in the gradient path optimization method depends on the local real-time target state estimation value, when the target position estimation is inaccurate, the result of the path optimization is not ideal, and is most serious in the initial stage of target estimation tracking. In addition, in the gradient algorithm, the selection of the step distance δ will directly affect the performance of the gradient method. Wherein (1) if the step length distance is set to be too small, a local optimal trap point is easy to be trapped in the optimal path point calculation of the gradient path optimization, and the local optimal trap point cannot be got rid of due to the limitation of too small step length. And (2) if the step distance is set too large, a global optimum point may be missed in the best path solution, circling around it. These two problems of the prior art gradient method path optimization technique are visualized in fig. 3.
The effect of using the overall path optimization without using the appropriate step size in finding the global optimum point is shown visually in fig. 4.
In order to overcome the problems in the gradient path optimization method, the patent designs and develops a set of improved technology, which mainly comprises two parts: (1) step length self-adaptive strategy, initial step length test and determination method, and (2) special direction point searching accounting.
According to the characteristic that the existing gradient method optimization is combined with the pseudo-linear Kalman target estimation precision, namely the estimation precision of the target is improved along with the progress of the path optimization time, therefore, the local optimization only has obvious influence in the initial stage of target estimation tracking, and the later stage needs to ensure that the step length is smaller and can reach the global optimum point. Accordingly, this patent design
Where K represents the upper run time limit, δ0Representing the initial step size will affect the final effect of the target estimation and path optimization. In order to determine the initial step size, the patent proposes a method based on simulation testing. Determining that x is set in a region of several square kilometers in combination with practical application0|0＝[1400,16,800,23]T，
W0|0＝diag(104,104,104,104) And the target moves at a constant speed from (1000 ) m, the speed being (5,5) m/s. Then, the patent performs simulation by using different δ values, and solves the gradient cost function value when k is 3, as shown in fig. 5. According to the figure, the step length of linear segment change can be used for avoiding jump caused by the magnitude of the gradient value being too large or too small, and further generating too many local optimal traps. Therefore, the initial step size of this patent is selected to be in the order of hundreds of meters in such a problem, and thus, the initial step size in this order is one of the core technical details proposed in this patent.
Then, in order to further avoid trapping in a local optimal trap, the method designs four additional point-finding method calculation methods for optimizing the cost function. After the improved gradient method is solved, the patent additionally selects 3 points at the opposite and left and right 90-degree positions of the determined gradient method path optimization point, solves the cost function values of the 4 path points, the selection of the 4 points is shown in figure 6,
at this point, the point searching method is adopted for resolving
Specifically, the positions of 4 undetermined points are substituted into formulas (5.8) to (5.11) to calculate the cost function of each position, and finally the minimum position is selected for comparison as the optimal path point at the next moment.
The technology of the invention is verified by MATLAB simulation. In simulation, a single unmanned aerial vehicle is used for tracking and positioning a moving target. Two different conditions are simulated in simulation, and the motion state and the initial position of the target are different so as to verify the general performance of the scheme. In the target tracking task, the initial course of the unmanned aerial vehicle is the same as the positive direction of the x axis. The sensor sampling time interval in the simulation is 1 second, and the path optimization interval is also 1 second.
In simulation verification, the target estimation precision of the proposed improved path optimization method is always optimal, and the effectiveness of the proposed technology is verified. After the cost function of target estimation (namely the trace of the covariance matrix of the PLKF) obtained by simulation and the complete motion track result of the unmanned aerial vehicle and the target are obtained, the patent can draw a conclusion, and the method can quickly and accurately realize target estimation. The simulation verification effect of two different target estimation tracking tasks is shown in fig. 7 and 8.
In addition, corresponding to the method provided by the present application, the present application also provides a path optimization target positioning device, including the following units:
a first unit for initializing a position estimation value and a covariance matrix value of a target;
a second unit to:
in the primary calculation: the method comprises the steps of obtaining an AOA sensor measurement value based on the position of the unmanned equipment and current position information of the unmanned equipment, operating a pseudo-linear Kalman filtering algorithm by combining a position estimation value and a covariance matrix value obtained by a first unit, solving to obtain a new position estimation value and a new covariance matrix value, and updating;
and when the loop calculation is carried out: the method comprises the steps of obtaining an AOA sensor measurement value based on the position of the unmanned equipment and current position information of the unmanned equipment, operating a pseudo-linear Kalman filtering algorithm by combining a position estimation value and a covariance matrix value obtained in the last calculation, and solving to obtain a new position estimation value and a new covariance matrix value and updating;
a third unit to: calculating an initial step length; based on an optimization algorithm of gradient descent, combining the initial cloth length, a new position estimation value and a covariance matrix value, an AOA sensor measurement value and current position information of the unmanned equipment, and solving the optimal observation position of the first unmanned equipment; determining additional multiple other possible optimal observation positions according to the determined optimal observation position, and solving covariance matrixes corresponding to all the possible optimal observation positions; comparing all the positions to finally determine the optimal observation position; the drone is moved to the optimal viewing position that is ultimately determined.
In addition, the present application also provides a storage medium having at least one instruction, at least one program, a set of codes, or a set of instructions stored therein, which is loaded and executed by a processor to implement the path optimization goal positioning method.
In addition, the present application further provides a terminal device, which includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the path optimization goal positioning method. The terminal equipment is an unmanned aerial vehicle or an unmanned ship or other unmanned equipment.
The technical means not described in detail in the present application are known techniques.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A path optimization target positioning method is characterized by comprising the following steps:
step 1: initializing a position estimation value and a covariance matrix value of a target;
step 2:
in the primary calculation: acquiring an AOA sensor measurement value based on the position of the unmanned equipment and current position information of the unmanned equipment, operating a pseudo-linear Kalman filtering algorithm by combining the position estimation value and the covariance matrix value obtained in the step 1, and solving to obtain a new position estimation value and a new covariance matrix value and updating;
and when the loop calculation is carried out: the method comprises the steps of obtaining an AOA sensor measurement value based on the position of the unmanned equipment and current position information of the unmanned equipment, operating a pseudo-linear Kalman filtering algorithm by combining a position estimation value and a covariance matrix value obtained in the last calculation, and solving to obtain a new position estimation value and a new covariance matrix value and updating;
and step 3: the method comprises the following steps:
step 3.1, calculating an initial step length;
and 3.2, solving an optimal observation position of the unmanned equipment based on an optimization algorithm of gradient descent by combining the data output in the step 3.1 and the step 2.
3.3, determining a plurality of other possible optimal observation positions according to the position in the step 3.2, and solving covariance matrixes corresponding to all possible optimal position points;
and 3.4, comparing all the positions to finally determine the optimal observation position.
And 4, step 4: and 3, moving the unmanned equipment to the optimal observation position obtained in the step 3, skipping to the step 2, and circularly calculating until the covariance matrix value reaches the requirement or the user terminates.
2. The method of claim 1, wherein the unmanned device is an unmanned aerial vehicle or an unmanned boat.
3. The method according to claim 1 or 2, wherein the initial step size is in the order of hundreds of meters.
4. A method for positioning a route optimization target according to claim 1 or 2, wherein in step 3.3, a plurality of other possible best observation positions are determined by using a point finding method, and 3 points opposite to the best observation position determined in step 3.2 and at positions 90 degrees left and right are additionally selected as best observation position points.
5. A path optimization target positioning apparatus, comprising:
a first unit for initializing a position estimation value and a covariance matrix value of a target;
a second unit to:
in the primary calculation: the method comprises the steps of obtaining an AOA sensor measurement value based on the position of the unmanned equipment and current position information of the unmanned equipment, operating a pseudo-linear Kalman filtering algorithm by combining a position estimation value and a covariance matrix value obtained by a first unit, solving to obtain a new position estimation value and a new covariance matrix value, and updating;
and when the loop calculation is carried out: the method comprises the steps of obtaining an AOA sensor measurement value based on the position of the unmanned equipment and current position information of the unmanned equipment, operating a pseudo-linear Kalman filtering algorithm by combining a position estimation value and a covariance matrix value obtained in the last calculation, and solving to obtain a new position estimation value and a new covariance matrix value and updating;
a third unit to: calculating an initial step length; based on an optimization algorithm of gradient descent, combining the initial cloth length, a new position estimation value and a covariance matrix value, an AOA sensor measurement value and current position information of the unmanned equipment, and solving the optimal observation position of the first unmanned equipment; determining additional multiple other possible optimal observation positions according to the determined optimal observation position, and solving covariance matrixes corresponding to all the possible optimal observation positions; comparing all the positions to finally determine the optimal observation position; the drone is moved to the optimal viewing position that is ultimately determined.
6. The apparatus of claim 5, wherein the unmanned device is a drone or a drones.
7. A path optimisation object positioning device as claimed in claim 5 or 6 wherein the initial step size is in the order of hundreds of metres.
8. The apparatus according to claim 5 or 6, wherein the third unit determines a plurality of other possible optimal observation positions by using a point finding method, and selects 3 points opposite to the optimal observation position of the first unmanned device and at positions 90 degrees left and right as the optimal observation position points.
9. A storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the path optimization goal localization method according to any one of claims 1 to 4.
10. A terminal device, characterized in that it comprises a processor and a memory, in which at least one instruction, at least one program, a set of codes or a set of instructions is stored, which is loaded and executed by the processor to implement the path optimization goal positioning method according to any of claims 1 to 4.
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