Optimal primitive positioning array method for position regulation and control of unmanned aerial vehicle

Abstract

The invention discloses an optimal primitive positioning array research method for unmanned aerial vehicle position regulation and control. The method comprises the steps of firstly performing dimension reduction processing on the unmanned aerial vehicle formation flying in the space, then performing quantity classification of the unmanned aerial vehicles in an element positioning array and direction classification under each condition, performing research on the unmanned aerial vehicles in sequence by using a pure geometric analysis means in combination with known direction angle information, and determining the direction of the unmanned aerial vehicles according to the angle information utilization rate. And comparing and verifying the positioning deviation and the solving complexity. The experimental simulation result is that the primitive positioning array composed of three unmanned aerial vehicles is easier and more efficient to solve, has universality and wide application capability, and can perform pure orientation passive positioning on the unmanned aerial vehicles in the flight formation by using angle information to a greater extent.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) pure bearing passive positioning, and in particular to an optimal primitive positioning array method for controlling the position of an unmanned aerial vehicle (UAV). Background Art

[0002] In today's era of rapid development of the Internet and scientific research technology, from helping modern villages to empowering smart cities, to showing the demeanor of a great power, drones will have a place wherever aerial solutions are needed. In terms of military use, drones can effectively reduce casualties, have advantages such as low cost and flexible configuration, and their role in the military will continue to expand; in terms of civilian use, the current use of drones in aerial photography, agriculture, disaster relief, film and television shooting and other fields has greatly expanded the use of drones themselves, and many developed countries are also actively expanding the development and application of drone technology in various industries. However, in the application process of drones, they need to obtain their own location information and perceive the surrounding environment in real time in order to better perform flight missions, so the research on the control of drone positions is particularly important.

[0003] In the study of pure azimuth passive positioning of UAVs, there are a series of positioning strategies, such as TDOA / AOA and TDOA / FDOA joint positioning method, optimal control model positioning optimization method, iterative adjustment method, etc. However, it is found that most of the above studies have not fully considered the spatial distribution characteristics of UAVs, and have not studied the optimal solution for the number of UAVs emitting directional signals.

[0004] At the same time, for real-life situations such as drone spraying of medicines and drone rescue, it is necessary to have precise control of the drone's location information. However, due to the dynamic nature of drone flight, its spatial positioning becomes more complicated. Considering the relationship between the signal transmitting drone and the receiver, in order to solve the receiver positioning problem more simply and accurately, the research on the optimal solution for the number of signal transmitting drones has practical significance. Summary of the invention

[0005] In view of the problems existing in the technical background, the present invention proposes an optimal primitive positioning array method for unmanned aerial vehicle position control.

[0006] The technical solution of the present invention to solve the above problems is: since the height of drones is consistent during flight, the dimension of the drone formation flying in space is first reduced, and then the number of drones in the primitive positioning array and the direction classification in each case are performed. Combined with the known direction angle information, pure geometric analysis methods are used to study them in turn, and comparative verification is made through angle information utilization, positioning deviation and solution complexity.

[0007] In order to achieve the above purpose, the following steps are taken:

[0008] Step 1: Eliminate the height dimension of the UAV, project the circular formation flight plane onto the XOY plane, and then simplify the coordinate system to establish a two-dimensional dynamic plane rectangular coordinate system for the circular formation UAV.

[0009] Step 2: First, divide the number of drones in the primitive positioning array into 3 and 4, and then classify them by direction according to their relative position information with the drone to be positioned as follows:

[0010] (I) Primitive positioning array containing three drones

[0011] ① The drone receiving the position signal is located on either side of the drone transmitting the signal (on the same side);

[0012] ② The drone receiving the position signal is located between the drones transmitting the signal (on the opposite side);

[0013] (II) Primitive positioning array containing 4 drones

[0014] ③ The drone receiving the position signal is located on either side of the drone transmitting the signal (on the same side);

[0015] ④ There is one drone on the left that receives the position signal and two drones on the right that transmit the signal;

[0016] ⑤ There are 2 drones on the left that receive position signals, and 1 drone on the right that transmits signals;

[0017] Step 3: Combine the direction angle information and analyze the above situations through pure geometric methods:

[0018] (I) Primitive positioning array containing three drones

[0019] In case ①: let the positions of the aircraft at the center of the circular formation and any two aircraft on the circumference be points A1, B1, and C1 respectively, and the position of the aircraft to be located be point P. By establishing a Cartesian coordinate system, since B1 and C1 are both on the circumference, they both satisfy the equation (1):

[0020]

[0021] Where A1(0,0), B1(x1,y1), C1(x2,y2), R is the radius of the circle;

[0022] In the triangle A1PC1, the length of each side is expressed as formula (2):

[0023]

[0024] Where, P(x0,y0);

[0025] According to the cosine theorem, as shown in formula (3):

[0026] (PC1) 2 +(PA1) 2 -2·PC1·PA1·cos(α1+α2)=(A1C1) 2 (3) In the formula, α1 and α2 are the angles between the lines A1P and B1P, and B1P and C1P, respectively, which are the angle information provided by the UAV in the primitive positioning array;

[0027] In triangle A1PB1, the length of each side is expressed as formula (4):

[0028]

[0029] According to the cosine theorem, as shown in formula (5):

[0030] (PB1) 2 +(PA1) 2 -2·PB1·PA1·cosα1=(A1B1) 2 (5)

[0031] By solving equations (1), (3), and (4), the position coordinates of the drone to be located are expressed as follows:

[0032]

[0033] In case ②: Let the positions of the aircraft at the center of the circular formation and any two aircraft on the circumference be points A1, B2 (x3, y3), C2 (x4, y4) respectively, and the position of the aircraft to be located is still point P (x0, y0). By establishing a Cartesian coordinate system, we can get:

[0034]

[0035] By solving equation (7), the expression of the position coordinates of the drone to be located is shown in equation (8):

[0036]

[0037] (II) Primitive positioning array containing 4 drones

[0038] Since the number of parameters will increase when a drone transmitting a signal is added, in order to reduce the number of parameters, the polar coordinate system is considered: the positions of the aircraft at the center of the circular formation and any three aircraft on the circumference are A(0,0), B(R,0), C(R,θ1), D(R,θ2), and the position of the aircraft to be located is point P(ρ,θ):

[0039] In ΔABC, according to the cosine theorem, we get equation (9):

[0040] BC 2 =AC 2 +AB 2 -2·AC·AB·∠CAB(9)

[0041] In ΔABP, using the law of sines and the law of cosines, we get equation (10):

[0042]

[0043] In ΔBCP, using the sine theorem, we get equation (11):

[0044]

[0045] By listing the above equations, we get equation (12):

[0046]

[0047] In case ③: ∠APB=α1+α2+α3, ∠BPC=α3, Substituting into equation (12), we can get the polar coordinates of the position of the UAV to be located as equation (13):

[0048]

[0049] Where α1, α2, and α3 are the angles between the UAV in the primitive positioning array and the UAV to be positioned, that is, the angle information provided by the UAV in the primitive positioning array;

[0050] In case ④: ∠APB=α2+α3, ∠BPC=α3, Substituting into equation (12), we can get the polar coordinates of the position of the UAV to be located as equation (14):

[0051]

[0052] In case ⑤: ∠APB=α3, ∠BPC=α1+α2+α3, Substituting into equation (12), we can get the polar coordinates of the position of the UAV to be located as equation (15):

[0053]

[0054] Step 4: Obtain the directional information rate, positioning deviation and solution complexity under different primitive positioning arrays through training sample data, and make comparative verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The following is an abstract of the present invention.

[0056] Figure 2It is the dimension reduction map of the UAV’s spatial position.

[0057] Figure 3 This is a geometric diagram of the direction signals sent and received by three drones.

[0058] Figure 4 This is a geometric diagram of the direction signals sent and received by four drones. DETAILED DESCRIPTION

[0059] The present invention is further described below with reference to the accompanying drawings and examples.

[0060] This paper takes the circular formation as an example, uses the optimal primitive positioning array to simulate the UAV position control method, and compares the positioning results of using 3 primitive positioning arrays with 4 primitive positioning arrays to determine the number of UAVs required in the optimal primitive positioning array. The specific steps are as follows:

[0061] Step 1: In order to verify the positioning accuracy of the primitive positioning array constructed by different numbers of UAVs transmitting signals, as well as the difficulty of solving the problem, the fixed angle information is shown in Tables 1 and 2, and the parameter R is changed. Given a set of training sample data at a given position, as shown in Tables 3 and 4:

[0062] Table 1 Angle information of the primitive positioning arrays of 3 UAVs Table 2 Angle information of the primitive positioning arrays of 4 UAVs

[0063]

[0064] Table 3 Actual coordinates of the primitive positioning arrays of the three UAVsTable 4 Actual coordinates of the primitive positioning arrays of the four UAVs

[0065]

[0066] In actual situations, in a primitive positioning array containing 3 drones, the lines between the drones can form 3 edges, so a total of 3 angle information can be provided; similarly, in a primitive positioning array containing 4 drones, 6 angle information can be provided. Therefore, when training sample data, try to restore the positioning situation of the drone in actual flight state to make the positioning result more practical. In addition, the radii of R = 102m, R = 104m, and R = 106m are selected as fixed radii of uniform circles to observe the degree of change of the actual coordinates due to the change of the radius, and at the same time avoid accidental parameter settings that make the parameter results after solution coincidental.

[0067] Step 2: Using the position coordinate expressions under the primitive positioning array constructed by the above-mentioned different numbers of UAVs transmitting signals, calculate the position coordinates obtained from the direction information given in step 1, as shown in Tables 5 and 6:

[0068] Table 5 Calculated coordinates of the primitive positioning array of 3 UAVsTable 6 Calculated polar coordinates of the primitive positioning array of 4 UAVs

[0069]

[0070] Through MATLAB calculation and simulation, it is worth noting that the coordinate expression of the primitive positioning array under 3 UAVs is different from that under 4 UAVs. After the transformation formula from rectangular coordinates to polar coordinates, it can be easily seen that the difference between the solution results of the former and the latter is negligible. It is undeniable that the calculation accuracy of the primitive positioning array of 3 UAVs is lower than that of the primitive positioning array of 4 UAVs. However, combining Table 3 and Table 4, it is not difficult to compare the positioning deviations of the two. However, the positioning deviation of the 3 UAVs is lower than that of the latter. In this respect, it performs better and is more practical.

[0071] Step 3: In order to better select the number of drones transmitting signals in the primitive positioning array, this section first selects the direction angle information utilization rate and positioning deviation for comparative analysis, as shown in Table 7, Table 8 and Table 9:

[0072] Table 7 Angle information utilization rate of the primitive positioning array of 3 UAVs Table 8 Angle information utilization rate of the primitive positioning array of 4 UAVs

[0073]

[0074] Table 9 Positioning deviation of the UAV primitive positioning array

[0075]

[0076] It can be seen that the information utilization rate of 66.7% solved by the primitive positioning array of 3 UAVs is greater than the information utilization rate of 50% solved by the primitive positioning array of 4 UAVs. Although the solving accuracy of the latter is slightly higher than the former, the position deviation of the former is 1.3% lower than the position deviation of the latter is 2.0%. Secondly, in terms of solution complexity, since the solution expression in the primitive positioning array of 4 UAVs contains 5 parameter variables, while the solution expression in the primitive positioning array composed of 3 UAVs only contains 4 parameter variables; in summary, in the case of large-scale UAV positioning, the primitive positioning array composed of 3 UAVs is simpler and more efficient to solve.

[0077] According to the above simulation results, it can be proved that in terms of pure bearing passive positioning of drones, the number of drones in the primitive positioning array can greatly affect the utilization rate of angle information and positioning deviation, and the difficulty of solving the problem will also be different. The conclusion method obtained by the present invention can more easily and accurately locate drones in a flight formation, thereby avoiding unnecessary information waste and positioning deviation.

Claims

1. An optimal primitive positioning array method for drone position control, comprising the following steps: Step 1: Eliminate the height dimension of the UAV, project the circular formation flight plane onto the XOY plane, and then simplify the coordinate system to establish a two-dimensional dynamic plane rectangular coordinate system for the circular formation UAV; Step 2: Divide the number of drones in the primitive positioning array into 3 and 4, and then classify them by direction according to their relative position information with the drone to be positioned as follows: (I) Primitive positioning array containing three drones ① The drone receiving the position signal is located on either side of the drone transmitting the signal ② The drone receiving the location signal is between the drones transmitting the signal (II) Primitive positioning array containing 4 drones ③The drone receiving the position signal is located on either side of the drone transmitting the signal ④ 1 drone on the left receiving the position signal and 2 drones on the right transmitting the signal ⑤ There are 2 drones on the left that receive location signals and 1 drone on the right that transmits signals. Step 3: Combine the direction angle information and analyze the above situations through pure geometric methods; Step 4: Obtain the directional information rate, positioning deviation and solution complexity under different primitive positioning arrays through training sample data, and make comparative verification.

2. The optimal primitive positioning array method for drone position control according to claim 1 is characterized in that the primitive positioning array containing three drones is analyzed as follows: When the UAV receiving the position signal is located on either side of the UAV transmitting the signal: let the positions of the aircraft at the center of the circle and any two aircraft on the circumference be points A1, B1, and C1 respectively, and the position of the aircraft to be located be point P. By establishing a Cartesian coordinate system, since B1 and C1 are both on the circumference, they both satisfy the equation (1): Where A1(0,0), B1(x1,y1), C1(x2,y2), R is the radius of the circle; In the triangle A1PC1, the length of each side is expressed as formula (2): Where, P(x0,y0); According to the cosine theorem, as shown in formula (3): (PC1) 2 +(PA1) 2 -2·PC1·PA1·cos(α1+α2)=(A1C1) 2 (3) In the formula, α1 and α2 are the angles between the lines A1P and B1P, and B1P and C1P, respectively, which are the angle information provided by the UAV in the primitive positioning array; In triangle A1PB1, the length of each side is expressed as formula (4): According to the cosine theorem, as shown in formula (5): (PB1) 2 +(PA1) 2 -2·PB1·PA1·cosα1=(A1B1) 2 (5) By solving equations (1), (3), and (4), the expression of the position coordinates of the drone to be located is shown in equation (6): When the drone receiving the position signal is between the drones transmitting the signal: let the positions of the aircraft at the center of the circular formation and any two aircraft on the circumference be points A1, B2 (x3, y3), C2 (x4, y4) respectively, and the position of the aircraft to be located is still point P (x0, y0). By establishing a Cartesian coordinate system, we can get the following by the same logic: By solving equation (7), the expression of the position coordinates of the drone to be located is shown in equation (8):

3. The optimal primitive positioning array method for drone position control according to claim 1 is characterized in that the primitive positioning array containing 4 drones is analyzed as follows: As a drone transmitting a signal is added, the parameters will increase, thus reducing the number of parameters: let the positions of the aircraft at the center of the circular formation and any three aircraft on the circumference be points A(0,0), B(R,0), C(R,θ1), D(R,θ2), and the position of the aircraft to be located be point P(ρ,θ): In ΔABC, according to the cosine theorem, we get equation (9): BC 2 =AC 2 + AB 2 -2·AC·AB·∠CAB (9) In ΔABP, using the law of sines and the law of cosines, we get equation (10): In ΔBCP, using the sine theorem, we get equation (11): By listing the above equations, we get equation (12): When the drone receiving the position signal is on either side of the drone transmitting the signal: ∠APB=α1+α2+α3, ∠BPC=α3, Substituting into equation (12), we can get the polar coordinates of the position of the UAV to be located as equation (13): Where α1, α2, and α3 are the angles between the UAV in the primitive positioning array and the UAV to be positioned, that is, the angle information provided by the UAV in the primitive positioning array; When there is one drone on the left side receiving the position signal and two drones on the right side transmitting the signal: ∠APB=α2+α3, ∠BPC=α3, Substituting into equation (12), we can get the polar coordinates of the position of the UAV to be located as equation (14): There are 2 drones on the left that receive position signals and 1 drone on the right that transmits signals: ∠APB=α3,∠BPC=α1+α2+α3, Substituting into equation (12), we can get the polar coordinates of the position of the UAV to be located as equation (15):

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