A single-anchor-point observable UAV self-positioning method and system

Abstract

The present invention proposes a single-anchor-point observable unmanned aerial vehicle (UAV) self-positioning method and system, comprising the following steps: first, building a single-anchor-point UAV self-positioning system based on the AOA positioning principle and deriving an expression of the positioning measurement true value with respect to the self-positioning parameter; then, first, defining the positioning function of the built single-anchor-point UAV self-positioning system according to the self-positioning parameter and the self-positioning measurement true value, and proving the observability of the single-anchor-point UAV self-positioning system on this basis; then, establishing the original self-positioning problem of the single-anchor-point UAV self-positioning system according to the probability density function expression of the self-positioning measurement value, and constructing an equivalent self-positioning problem of the single-anchor-point UAV self-positioning system according to the monotonicity of the probability density function expression of the self-positioning measurement value with respect to the cost function; finally, solving the above equivalent self-positioning problem of the single-anchor-point UAV self-positioning system based on a modified maximum likelihood iterative algorithm, obtaining the optimal self-positioning parameter value, and realizing UAV self-positioning.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) self-positioning, and in particular relates to a UAV self-positioning method and system with a single anchor point observable. Background Art

[0002] Drones offer the advantages of low cost, high mobility, and high reliability. Their direct-line-of-sight links between ground and aerial nodes, both air-to-ground and air-to-air, offer higher quality than terrestrial cellular network channels. This makes drones a promising technology for wireless communication applications such as data collection, communication relay, and the Internet of Vehicles. The flexibility of drones introduces new dimensions to network design, and through the rational planning of drone communication systems, network performance can be effectively improved.

[0003] However, compared to terrestrial networks, the system design of UAV communication networks relies on the UAV's precise self-positioning and alignment with its local coordinate system—in other words, self-localization. In highly obscured urban environments, UAV self-localization primarily relies on anchor point signals. P. Sinha et al. studied the impact of three-dimensional antennas on the accuracy of UAV time of arrival (TOA) and time difference of arrival (TDOA) positioning, revealing the fundamental limitations of current UAV three-dimensional positioning. To leverage the advantages of both angle-based positioning and ranging positioning, X. Kang et al. studied UAV self-localization using a hybrid angle of arrival (AOA) and TDOA, significantly improving its accuracy. However, to meet the observability requirements of the positioning system, these studies require at least four anchor points with widely varying location distributions. Self-localization places high demands on the number and spatial distribution of anchor points, and is highly susceptible to external noise and interference, making it difficult to guarantee accurate positioning. Achieving observable UAV self-localization in complex environments with a small number of anchor points, or even a single anchor point, remains an open question in the academic community. Summary of the Invention

[0004] In view of the current situation that there is a lack of drone self-positioning methods with single anchor point observable in drone communication networks, the present invention proposes a drone self-positioning method and system with single anchor point observable, which is used to improve the drone self-positioning performance with the support of a small number of anchor points.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A single-anchor-observable UAV self-positioning method comprises the following steps:

[0007] Step 1: Build a single-anchor UAV self-positioning system based on the AOA positioning principle, set the self-positioning parameters, derive the self-positioning parameter expression of the true value of the self-positioning measurement, and obtain the true value of the self-positioning measurement;

[0008] Step 2: Setting the positioning function of the constructed single-anchor UAV self-positioning system based on the true value of the self-positioning measurement, and proving that the constructed single-anchor UAV self-positioning system is observable;

[0009] Step 3: Based on the true value of the self-positioning measurement, an expression for the self-positioning measurement value is obtained, and then a probability density function expression for the self-positioning measurement value is obtained based on the measurement noise distribution. Then, based on the probability density function expression for the self-positioning measurement value, the original self-positioning problem of the single-anchor UAV self-positioning system is established; and an equivalent self-positioning problem of the single-anchor UAV self-positioning system is constructed;

[0010] Step 4: Solve the self-positioning problem of the equivalent single-anchor UAV self-positioning system, obtain the optimal self-positioning parameter value, and realize UAV self-positioning.

[0011] Furthermore, the single-anchor-point UAV self-positioning system in step 1 includes an anchor point and a UAV that needs to self-position.

[0012] Furthermore, the self-positioning parameters in step 1 are:

[0013] in, is the alignment angle of the UAV’s local coordinate system, and the initial position of the UAV is ,in Representing drones in The initial position coordinates on the axis.

[0014] Furthermore, the true value of the self-positioning measurement described in step 1 is expressed as follows with respect to the self-positioning parameter:

[0015]

[0016] Among them, Representative The true value of the azimuth angle between the drone and the anchor point at the moment, Representative The true value of the pitch angle between the drone and the anchor point at this moment; Representing the The drone's displacement at the moment Components on the axis; Represents the alignment angle with the drone's local coordinate system The associated coordinate rotation matrix; Representative The drone moves at every moment. Represents the total number of moments; Represents the modulus of the first two elements of a vector, Represents matrix transpose.

[0017] Furthermore, the positioning function of the single anchor point UAV self-positioning system built in step 2 is:

[0018]

[0019] in, and Respectively represent the positioning functions of the single anchor point UAV self-positioning system with respect to azimuth and pitch angle, and their independent variables are , the dependent variables are and ; The symbol represents a A vector of dimension, express The set of true values ​​of the azimuth angle between the drone and the anchor point at each moment, express The set of true values ​​of the pitch angle between the drone and the anchor point at each moment.

[0020] Furthermore, in step 2, the method for proving that the constructed single-anchor UAV self-positioning system is observable is as follows:

[0021] Prove the positioning function of azimuth For about The injective function of

[0022] Prove the positioning function of the pitch angle For about The injective function of

[0023] prove and is a surjective function, then the positioning function is a bijective function with respect to the self-positioning parameters, that is, the constructed single-anchor UAV self-positioning system is observable.

[0024] Furthermore, the expression of the self-positioning measurement value in step 3 is:

[0025]

[0026] in Represents the azimuth measurement between the drone and the anchor point, Represents the pitch angle measurement between the drone and the anchor point, represents the azimuth measurement noise; Represents the pitch angle measurement noise.

[0027] Furthermore, the probability density function expression of the self-positioning measurement value in step 3 is:

[0028]

[0029] in, is the probability density function expression of the self-positioning measurement value, represents the cost function, represents the covariance matrix of azimuth angle measurement noise and elevation angle measurement noise; represents the covariance matrix of the azimuth measurement noise, represents the covariance of the azimuth measurement noise at the first moment, Representative The covariance of the azimuth measurement noise at each moment; represents the covariance matrix of the pitch angle measurement noise, represents the covariance of the pitch angle measurement noise at the first moment, Representative The covariance of the pitch angle measurement noise at each moment;

[0030] The original self-positioning problem of the single-anchor UAV self-positioning system described in step 3 is:

[0031]

[0032] The original self-positioning problem of the single-anchor UAV self-positioning system is equivalent to:

[0033]

[0034] Among them, Maximize the corresponding , which is the self-positioning parameter required for self-positioning.

[0035] Furthermore, in step 4, solving the self-positioning problem of the equivalent single-anchor UAV self-positioning system includes the following sub-steps:

[0036] Step 4.1. Initialize the drone's self-positioning parameter values , set the iteration threshold , and the number of iterations , and initialize the UAV self-positioning parameter value as the local point of iteration;

[0037] Step 4.2: In In the first iteration, the initial position of the drone is is the value corresponding to the local point of this iteration, that is , the equivalent self-positioning problem of the single-anchor UAV self-positioning system is transformed into The optimal value of the alignment angle of the UAV local coordinate system is obtained by solving the maximum likelihood estimation in the form of ;

[0038] Step 4.3: In In the iteration, the local coordinate system of the drone is aligned with the angle is the optimized value for this iteration, i.e. , then the equivalent self-positioning problem of the single anchor UAV self-positioning system is transformed into In the form of cost function Perform convex approximation to convert to upper bound convex form , solving convex approximation problems through convex optimization algorithms Get the optimized value of the drone's initial position ;

[0039] Step 4.4: If the relative decrease of the objective function of this iteration compared to the previous objective function is less than the iteration threshold , then the iteration is stopped, and the optimized value of this iteration is the value of the UAV self-positioning parameter; otherwise, it will be used as the local point of the next iteration and return to step 4.2.

[0040] In another aspect, the present invention provides a single-anchor-point observable UAV self-positioning system, comprising:

[0041] True value acquisition module: It is used to build a single-anchor UAV self-positioning system based on the AOA positioning principle, set self-positioning parameters, derive the true value of the self-positioning measurement with respect to the self-positioning parameter expression, and obtain the true value of the self-positioning measurement;

[0042] A positioning function construction module is used to set the positioning function of the constructed single-anchor UAV self-positioning system based on the true value of the self-positioning measurement, and prove that the constructed single-anchor UAV self-positioning system is observable;

[0043] A self-positioning problem construction module is used to obtain an expression for the self-positioning measurement value based on the true value of the self-positioning measurement, then obtain a probability density function expression for the self-positioning measurement value based on the measurement noise distribution, and then establish the original self-positioning problem of the single-anchor UAV self-positioning system based on the probability density function expression of the self-positioning measurement value; and construct an equivalent self-positioning problem of the single-anchor UAV self-positioning system;

[0044] Solving module: It is used to solve the self-positioning problem of the equivalent single-anchor UAV self-positioning system, obtain the optimal self-positioning parameter value, and realize UAV self-positioning.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] This paper proposes a single-anchor-observable UAV self-localization method and system, significantly reducing the number of external anchor points required for UAV self-localization while maintaining the observability requirements of the positioning system. Furthermore, a modified maximum likelihood iterative algorithm is proposed to efficiently solve the self-localization problem. This proposed single-anchor-observable UAV self-localization method and system can be widely applied to the self-localization of UAV swarms in urban environments where global positioning signals are blocked. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 Schematic diagram of a single-anchor-point observable UAV self-positioning system according to an embodiment of the present invention;

[0049] Figure 2 A flow chart of the method for implementing the present invention;

[0050] Figure 3 Schematic diagram comparing RMSE and CRLB of the self-positioning solution proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0052] Example 1

[0053] like Figure 1 As shown, this embodiment provides a single-anchor-observable drone self-positioning system and method, as follows:

[0054] Figure 1 A single-anchor-point UAV self-positioning system based on the AOA positioning principle is considered as a specific embodiment of the present invention. The system includes an anchor point and a UAV that needs to self-position. The anchor point sends a positioning signal to the UAV and realizes UAV self-positioning by applying the AOA positioning principle to the received signal. The self-positioning parameters are the initial position of the UAV and the alignment angle of the local coordinate system.

[0055] like Figure 2The flowchart of the method of the present invention is shown, and the implementation process includes the following steps:

[0056] Step 1: Build a single-anchor UAV self-positioning system based on the AOA positioning principle, set the self-positioning parameters, derive the self-positioning parameter expression of the true value of the self-positioning measurement, and obtain the true value of the self-positioning measurement;

[0057] First, a single-anchor UAV self-positioning system based on the AOA positioning principle was built. The system consists of an anchor point and a UAV that needs to be self-positioned. This self-positioning system uses the AOA positioning principle to achieve UAV self-positioning. The self-positioning parameters are the UAV's initial position and the alignment angle of the local coordinate system. Then, combining the anchor point position, the UAV's displacement in the local coordinate system at each moment, and the self-positioning parameters, an expression for the true self-positioning measurement with respect to the self-positioning parameters is derived.

[0058] The anchor point position described in step 1 is ; The initial position of the drone is ,in Representing the Drones at all times The initial position coordinates on the axis; the alignment angle of the drone's local coordinate system is , then the self-positioning parameters of the UAV self-positioning system are .

[0059] The displacement of the drone at each moment in step 1 is ,in Represents the number of moments, Representative The drone moves at every moment. Representing the The drone's displacement at the moment The component on the axis, Represents the total number of moments, where need To meet the observability requirements of subsequent self-positioning systems, Represents matrix transpose.

[0060] For the AOA self-positioning system, the self-positioning measurement is the azimuth and pitch angle between the drone and the anchor point. The true value of the self-positioning measurement described in step 1 is expressed in terms of the self-positioning parameters:

[0061]

[0062] Among them, Representative The true value of the azimuth angle between the drone and the anchor point at the moment, Representative The true value of the pitch angle between the drone and the anchor point at this moment; Representing the The drone's displacement at the moment Components on the axis; Representative The drone moves at every moment. Represents the total number of moments; Represents the modulus of the first two elements of a vector, Represents matrix transpose, and its expression is:

[0063]

[0064] Step 2: Setting the positioning function of the constructed single-anchor UAV self-positioning system based on the true value of the self-positioning measurement, and proving that the constructed single-anchor UAV self-positioning system is observable;

[0065] First, the positioning function of the constructed single-anchor UAV self-positioning system is defined based on the self-positioning parameters and the true values ​​of the self-positioning measurements. Then, the positioning function is proved to be an injective function with respect to the self-positioning parameters. Finally, combined with the surjective characteristics of the function, it is found that the positioning function is a bijective function with respect to the self-positioning parameters, indicating that the constructed single-anchor UAV self-positioning system is observable.

[0066] The positioning function of the single anchor point UAV self-positioning system described in step 2 is:

[0067]

[0068] in, and Respectively represent the positioning functions of the single anchor point UAV self-positioning system with respect to azimuth and pitch angle, and their independent variables are , the dependent variables are and ; The symbol represents a A vector of dimensions, table express The set of true values ​​of the azimuth angle between the drone and the anchor point at each moment, express The set of true values ​​of the pitch angle between the drone and the anchor point at each moment.

[0069] The positioning function described in step 2 is an injective function with respect to the self-positioning parameter. The process is as follows: First, prove that about is an injective function. , assuming there is a set of independent variables Make , that is, in time superior The corresponding function value is equal to The function value of , the following equation holds:

[0070]

[0071] Then we introduce a multiplier and add the left and right equations of the above time equation to get:

[0072]

[0073] in Representative The multiplier corresponding to each moment. Simplifying the above formula, we can get:

[0074]

[0075] because is an independent arbitrary value, the above formula is valid if 、 、 and At the same time. According to the first sub-condition ,Right now The condition is , about is an injective function.

[0076] Then prove about is an injective function, and it is also assumed that there is a UAV position coordinate Make , then the following equation holds:

[0077]

[0078] in , using the multiplier to add the square of the function value over time:

[0079]

[0080] because is an independent arbitrary value, the above formula is equal to , Constantly established, that is , about is an injective function.

[0081] Finally, according to step 1 ,but and is a surjective function, then the positioning function is a bijective function with respect to the self-positioning parameters, that is, the constructed single-anchor UAV self-positioning system is observable.

[0082] Step 3: Based on the true value of the self-positioning measurement, an expression for the self-positioning measurement value is obtained, and then a probability density function expression for the self-positioning measurement value is obtained according to the measurement noise distribution. Then, based on the probability density function expression for the self-positioning measurement value, the original self-positioning problem of the single-anchor UAV self-positioning system is established; and an equivalent self-positioning problem of the single-anchor UAV self-positioning system is constructed;

[0083] The expression of the self-positioning measurement value described in step 3 is:

[0084]

[0085] in Represents the azimuth measurement between the drone and the anchor point, Represents the pitch angle measurement between the drone and the anchor point. represents the azimuth measurement noise, and its covariance matrix is , represents the covariance of the azimuth measurement noise at the first moment, Representative The covariance of the azimuth measurement noise at each moment; represents the pitch angle measurement noise, and its covariance matrix is , represents the covariance of the pitch angle measurement noise at the first moment, Representative The covariance of the pitch angle measurement noise at each moment.

[0086] The probability density function expression of the self-positioning measurement value described in step 3 is:

[0087]

[0088] in represents the cost function, Represents the covariance matrix of azimuth angle measurement noise and elevation angle measurement noise.

[0089] The original self-positioning problem of the single-anchor UAV self-positioning system described in step 3 is:

[0090]

[0091] Its meaning is: to make Maximize the corresponding , which is the self-positioning parameter required for self-positioning. About the cost function Monotonically decreasing, the original self-positioning problem of the single-anchor UAV self-positioning system can be equivalent to:

[0092]

[0093] Step 4: Solve the self-positioning problem of the equivalent single-anchor UAV self-positioning system based on the modified maximum likelihood iterative algorithm, obtain the optimal self-positioning parameter value, and realize UAV self-positioning.

[0094] The specific steps of the iterative algorithm for modified maximum likelihood described in step 4 are as follows:

[0095] Step 4.1: Initialize the drone's self-positioning parameter values , set the iteration threshold , and the number of iterations . And the initialization of the UAV self-positioning parameter value is used as the local point of iteration;

[0096] Step 4.2: In In the first iteration, the initial position of the drone is is the value corresponding to the local point of this iteration, that is , then the equivalent self-positioning problem of the single-anchor UAV self-positioning system can be transformed into In the form of, this problem can be efficiently solved by maximum likelihood estimation to obtain the optimal value of the alignment angle of the UAV local coordinate system ;

[0097] Step 4.3: In In the iteration, the local coordinate system of the drone is aligned with the angle is the optimized value for this iteration, i.e. , then the equivalent self-positioning problem of the single-anchor UAV self-positioning system can be transformed into The cost function Perform convex approximation to convert to upper bound convex form , solving convex approximation problems through convex optimization algorithms Get the optimized value of the drone's initial position ;

[0098] Step 4.4: If the relative decrease of the objective function of this iteration compared to the previous objective function is less than the iteration threshold , then the iteration is stopped, and the optimized value of this iteration is the value of the UAV self-positioning parameter; otherwise, it will be used as the local point of the next iteration and return to step 4.2.

[0099] Figure 3 The root mean square error (RMSE) of the proposed self-localization scheme under different UAV displacements in the embodiment of the present invention is compared with the Cramer-Rao lower bound (CRLB). It can be seen that the RMSE of the proposed UAV self-localization scheme is extremely close to the CRLB theoretical performance, and has good self-localization performance.

[0100] Example 2

[0101] This embodiment provides a single-anchor-observable UAV self-positioning system, including:

[0102] True value acquisition module: It is used to build a single-anchor UAV self-positioning system based on the AOA positioning principle, set the self-positioning parameters of the UAV self-positioning system, derive the true value of the self-positioning measurement with respect to the self-positioning parameter expression, and obtain the true value of the self-positioning measurement;

[0103] A positioning function construction module is used to set the positioning function of the constructed single-anchor UAV self-positioning system based on the true value of the self-positioning measurement, and prove that the constructed single-anchor UAV self-positioning system is observable;

[0104] A self-positioning problem construction module is used to obtain an expression for the self-positioning measurement value based on the true value of the self-positioning measurement, then obtain an expression for the probability density function of the self-positioning measurement value based on the measurement noise distribution, and then establish the original self-positioning problem of the single-anchor UAV self-positioning system based on the self-positioning measurement value probability density function expression; and construct an equivalent self-positioning problem of the single-anchor UAV self-positioning system;

[0105] Solving module: It is used to solve the self-positioning problem of the equivalent single-anchor UAV self-positioning system, obtain the optimal self-positioning parameter value, and realize UAV self-positioning.

[0106] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0107] It should be understood that the above description of the preferred embodiments is relatively detailed and cannot be considered as limiting the scope of protection of the present invention. It is not necessary and impossible to list all embodiments here. Under the guidance of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which fall within the scope of protection of the present invention. The scope of protection of the present invention shall be based on the attached claims.

Claims

1. A single-anchor-observable UAV self-positioning method, characterized in that: The following steps are involved: Step 1: Build a single-anchor UAV self-positioning system based on the AOA positioning principle, set the self-positioning parameters, derive the expression of the true value of the self-positioning measurement with respect to the self-positioning parameters, and obtain the true value of the self-positioning measurement; the self-positioning parameters in step 1 are: in, is the alignment angle of the UAV’s local coordinate system, and the initial position of the UAV is ,in Representing drones in The initial position coordinates on the axis; the true value of the self-positioning measurement is expressed as follows with respect to the self-positioning parameter: Among them, Representative The true value of the azimuth angle between the drone and the anchor point at the moment, Representative The true value of the pitch angle between the drone and the anchor point at this moment; Representing the The drone's displacement at the moment Components on the axis; Represents the alignment angle with the drone's local coordinate system The associated coordinate rotation matrix; Representative The drone moves at every moment. Represents the total number of moments; ; Represents the modulus of the first two elements of a vector, Represents matrix transpose; Step 2: Setting the positioning function of the constructed single-anchor UAV self-positioning system based on the true value of the self-positioning measurement, and proving that the constructed single-anchor UAV self-positioning system is observable; Step 3: Based on the true value of the self-positioning measurement, an expression for the self-positioning measurement value is obtained, and then a probability density function expression for the self-positioning measurement value is obtained based on the measurement noise distribution. Then, based on the probability density function expression for the self-positioning measurement value, the original self-positioning problem of the single-anchor UAV self-positioning system is established; and an equivalent self-positioning problem of the single-anchor UAV self-positioning system is constructed; Step 4: Solve the self-positioning problem of the equivalent single-anchor UAV self-positioning system, obtain the optimal self-positioning parameter value, and realize UAV self-positioning.

2. The single-anchor-observable UAV self-positioning method according to claim 1, characterized in that: The single anchor point UAV self-positioning system in step 1 includes an anchor point and a UAV that needs to be self-positioned.

3. The single-anchor-observable UAV self-positioning method according to claim 1, characterized in that: The positioning function of the single anchor point UAV self-positioning system built in step 2 is: in, and Respectively represent the positioning functions of the single anchor point UAV self-positioning system with respect to azimuth and pitch angle, and their independent variables are , the dependent variables are and ; The symbol represents a A vector of dimension, express The set of true values ​​of the azimuth angle between the drone and the anchor point at each moment, express The set of true values ​​of the pitch angle between the drone and the anchor point at each moment.

4. The single-anchor-observable UAV self-positioning method according to claim 3, characterized in that: In step 2, the method for proving that the constructed single-anchor UAV self-positioning system is observable is as follows: Prove the positioning function of azimuth For about The injective function of Prove the positioning function of the pitch angle For about The injective function of prove and is a surjective function, then the positioning function is a bijective function with respect to the self-positioning parameters, that is, the constructed single-anchor UAV self-positioning system is observable.

5. The single-anchor-observable UAV self-positioning method according to claim 3, characterized in that: The expression of the self-positioning measurement value in step 3 is: in Represents the azimuth measurement between the drone and the anchor point, Represents the pitch angle measurement between the drone and the anchor point, represents the azimuth measurement noise; Represents the pitch angle measurement noise.

6. The single-anchor-observable UAV self-positioning method according to claim 5, characterized in that: The probability density function expression of the self-positioning measurement value in step 3 is: in, is the probability density function expression of the self-positioning measurement value, represents the cost function, represents the covariance matrix of azimuth angle measurement noise and elevation angle measurement noise; represents the covariance matrix of the azimuth measurement noise, represents the covariance of the azimuth measurement noise at the first moment, Representative The covariance of the azimuth measurement noise at each moment; represents the covariance matrix of the pitch angle measurement noise, represents the covariance of the pitch angle measurement noise at the first moment, Representative The covariance of the pitch angle measurement noise at each moment; The original self-positioning problem of the single-anchor UAV self-positioning system described in step 3 is: The original self-positioning problem of the single-anchor UAV self-positioning system is equivalent to: Among them, Maximize the corresponding , which is the self-positioning parameter required for self-positioning.

7. The single-anchor-observable UAV self-positioning method according to claim 5, characterized in that: In step 4, solving the self-positioning problem of the equivalent single-anchor UAV self-positioning system includes the following sub-steps: Step 4.

1. Initialize the drone's self-positioning parameter values , set the iteration threshold , and the number of iterations , and initialize the UAV self-positioning parameter value as the local point of iteration; Step 4.2: In In the first iteration, the initial position of the drone is is the value corresponding to the local point of this iteration, that is , the equivalent self-positioning problem of the single-anchor UAV self-positioning system is transformed into The optimal value of the alignment angle of the UAV local coordinate system is obtained by solving the maximum likelihood estimation in the form of ; Step 4.3: In In the iteration, the local coordinate system of the drone is aligned with the angle is the optimized value for this iteration, i.e. , then the equivalent self-positioning problem of the single anchor UAV self-positioning system is transformed into In the form of cost function Perform convex approximation to convert to upper bound convex form , solving convex approximation problems through convex optimization algorithms Get the optimized value of the drone's initial position ; Step 4.4: If the relative decrease of the objective function of this iteration compared to the previous objective function is less than the iteration threshold , then the iteration is stopped, and the optimized value of this iteration is the value of the UAV self-positioning parameter; otherwise, it will be used as the local point of the next iteration and return to step 4.

2.

8. A single anchor point observable UAV self-positioning system, characterized in that: include: True value acquisition module: It is used to build a single-anchor UAV self-positioning system based on the AOA positioning principle, set self-positioning parameters, derive the true value of the self-positioning measurement with respect to the self-positioning parameter expression, and obtain the true value of the self-positioning measurement; A positioning function construction module is used to set the positioning function of the constructed single-anchor UAV self-positioning system based on the true value of the self-positioning measurement, and prove that the constructed single-anchor UAV self-positioning system is observable; A self-positioning problem construction module is used to obtain an expression for the self-positioning measurement value based on the true value of the self-positioning measurement, then obtain a probability density function expression for the self-positioning measurement value based on the measurement noise distribution, and then establish the original self-positioning problem of the single-anchor UAV self-positioning system based on the probability density function expression of the self-positioning measurement value; and construct an equivalent self-positioning problem of the single-anchor UAV self-positioning system; Solving module: It is used to solve the self-positioning problem of the equivalent single-anchor UAV self-positioning system, obtain the optimal self-positioning parameter value, and realize UAV self-positioning; The single-anchor-point observable UAV self-positioning system is used to execute the steps in the single-anchor-point observable UAV self-positioning method described in any one of claims 1 to 7.

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