Azimuth-only element solution observation route generation method

Abstract

The invention provides a pure azimuth element calculation observation route generation method. The method comprises the following steps: observing the azimuth and motion relationship between an underwater vehicle and a target ship and modeling; constructing a geometric precision factor optimization function based on the target position and the speed estimation error of the underwater vehicle; an ant colony algorithm is adopted for solving, geometric accuracy factors are utilized, a mathematical optimization model is established, under the limitation of the motion ability of the underwater vehicle, an observation route of the underwater vehicle is generated through optimization methods such as ant colony search, and the convergence efficiency of solving the motion elements of the pure azimuth target is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of auxiliary decision making, and in particular relates to a method for generating an observation route by solving pure bearing elements. Background Art

[0002] It is an important part of attack and defense for underwater vehicles to solve target motion elements based on sonar detection information. However, directly solving target motion elements based on target azimuth information detected by sonar requires accumulating target azimuth sequences for a long time, which reduces the convergence efficiency of azimuth target motion element solution and is not conducive to the design of enemy tracking routes. Summary of the invention

[0003] In order to solve the above problems of the prior art, an embodiment of the present invention provides a method for generating an observation route by solving pure bearing elements.

[0004] According to one aspect of the present invention, a method for generating an observation route by solving a pure bearing element is provided, comprising the following steps:

[0005] S101, observe the position and motion relationship between the underwater navigation body and the target ship to model;

[0006] S102, constructing a geometric precision factor optimization function based on the target position and velocity estimation errors of the underwater vehicle;

[0007] S103, using ant colony algorithm to solve.

[0008] Preferably, the modeling process includes:

[0009] Assuming that the target ship is moving in a straight line at a constant speed, W0 and M0 represent the initial positions of the underwater vehicle and the target ship when the underwater vehicle finds the target. If the measurement time interval is T, in the i-th measurement, the underwater vehicle and the target ship are at W i (x wi ,y wi )、M i (x mi ,y mi ) position, H w , H m t i The heading of the underwater vehicle and the target ship at the moment, v w 、v m t i The speed of the underwater vehicle and the target ship at the moment; F i is the target azimuth at the time of the i-th measurement. Assume that the velocity components of the target in the x and y directions are If a total of n measurements are made, we get formula 1:

[0010]

[0011] Preferably, the calculation process of the geometric precision factor optimization function is as follows: Differentiate the formula once to obtain

[0012]

[0013] The matrix equation is

[0014] dV=HdXwhere

[0015] dV=[dF0 dF1...dF n ] T

[0016]

[0017] in

[0018]

[0019] The positioning error can be obtained as

[0020] dX=(H T H) -1 HkDJ

[0021] make

[0022] B=(H T H) -1 H

[0023] Then the covariance matrix of the positioning error is

[0024] P dX =Bcov(dV)B T

[0025] in

[0026]

[0027] in is the variance of the angle measurement error, and the geometric precision factor GDOP result is

[0028]

[0029] Preferably, the calculation process of the next moment position of the underwater vehicle is:

[0030] x w(i+1) =x wi +V wi sin H wi

[0031] y w(i+1) =y wi +V wicos H wi

[0032] (i=0,1,2,...)

[0033] The optimization problem to be solved can be written as

[0034]

[0035] st|H wi -H w(i-1) |≤ΔH w

[0036] |V wi -V w(i-1) |≤ΔV w

[0037] V wi ≥0

[0038] (i=4,5,6,...)

[0039] Among them, (x wi ,y wi ) is the current position, (x w(i+1) ,y w(i+1) ) is the position at the next moment, H wi is the heading of the underwater vehicle between the current moment and the next moment, V wi is the speed of the underwater vehicle between the current moment and the next moment.

[0040] Preferably, the solution process using the ant colony algorithm is as follows:

[0041] S201, initializing parameters;

[0042] S202, calculating the state transition probability of the ant;

[0043] S203, performing location update;

[0044] S204, updating pheromones;

[0045] S205: Determine whether to terminate.

[0046] Preferably, the parameters include the ant colony size n ant , maximum number of iterations iter max , pheromone volatility factor ρ, transition probability constant P0, local search step length r step .

[0047] Preferably, the state transition probability of the ant numbered k at the iter iteration is calculated as

[0048]

[0049] where k = 1, 2, ..., n ant , max is the maximum value of pheromone, Ta k is the pheromone of the ant numbered k.

[0050] Preferably, the location update calculation process is as follows:

[0051] If the state transition probability is not greater than the transition probability constant P0, a local search is performed, and the search formula is:

[0052] x new =x old +μr step λ

[0053] where x old 、x new is the value of the optimization variable of the ant before and after the update, μ is a random number in the interval [-1,1], and λ is the inverse of the current number of iterations;

[0054] If the state transition probability is greater than the transition probability constant P0, a global search is performed, and the search formula is:

[0055] x new =x old +μr range / 2

[0056] Where μ is a random number in the interval [-1,1], r range The length of the interval for optimizing variable values.

[0057] Preferably, the pheromone updating method is Ta new =(1-ρ)Ta old -f

[0058] Among them old 、 new is the pheromone before and after the update, and f is the objective function value.

[0059] The beneficial effects brought by the present invention are as follows:

[0060] It can be seen from the above scheme that an embodiment of the present invention provides a method for generating an observation route for solving pure bearing elements. It uses the geometric precision factor to establish a mathematical optimization model, and generates an observation route for the underwater vehicle through optimization methods such as ant colony search under the limitation of the motion ability of the underwater vehicle, thereby improving the convergence efficiency of solving pure bearing target motion elements. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1The present invention shows the situation of a target ship and an underwater vehicle under pure bearings tracking in a method for generating an observation route by solving pure bearings elements according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Example

[0064] 1. Modeling the relationship between observation orientation and motion

[0065] During the underwater vehicle approaching and tracking process, it is assumed that the target ship is moving straight at a constant speed. i The relative situation of the underwater vehicle and the target ship at the moment is shown in the figure below, where W0 and M0 represent the initial positions of the underwater vehicle and the target respectively when the underwater vehicle finds the target; if the measurement time interval is T, at the i-th measurement, the underwater vehicle and the target are at W i (x wi ,y wi )、M i (x mi ,y mi ) position, H w , H m t i The underwater vehicle heading and target heading at the moment, v w 、v m t i Underwater vehicle speed and target speed at all times; F i is the target azimuth at the time of the i-th measurement. Assume that the velocity components of the target in the x and y directions are If the measurement is n times, we know from the geometric relationship in the figure that

[0066]

[0067] 2. Constructing the geometric precision factor optimization function based on the target position and velocity estimation errors

[0068] Differentiating formula (1), we get

[0069]

[0070] The matrix equation is

[0071] dV=HdX (3)

[0072] in

[0073] dV=[dF0 dF1...dF n ] T

[0074]

[0075] in

[0076]

[0077] The positioning error can be obtained as

[0078] dX=(H T H) -1 HDV (6)

[0079] make

[0080] B=(H T H) -1 H

[0081] Then the covariance matrix of the positioning error is

[0082] P dX =Bcov(dV)B T (7)

[0083] in

[0084]

[0085] in is the variance of the angle measurement error, and the geometric precision factor GDOP result is

[0086]

[0087] By adjusting the position of the underwater vehicle at the next moment, the geometric precision factor GDOP can be minimized to improve the positioning and tracking accuracy. If the underwater vehicle is regarded as moving at a constant speed between the two measurement moments, the position at the next moment (x w(i+1) ,y w(i+1) ) can be obtained from the current position (x wi ,y wi ) and the underwater vehicle heading H between the two moments wi and speed V wi Recursively, we get

[0088] x w(i+1) =x wi +V wi sI wi

[0089] y w(i+1) =ywi +V wi oeLh wi

[0090] (i=0,1,2,...) (10)

[0091] Then the optimization problem to be solved can be written as

[0092]

[0093] st|H wi -H w(i-1) |≤ΔH w

[0094] |V wi -V w(i-1) |≤ΔV w

[0095] V wi ≥0

[0096] (i=4,5,6,...) (11)

[0097] Among them, the objective function geometric precision factor GDOP i is the geometric precision factor GDOP of the next moment calculated at the i-1th measurement moment, and the heading H of the underwater vehicle wi and speed V wi To optimize the variables, the changes of the two from the previous moment are limited by the maneuverability of the underwater vehicle, that is, they do not exceed the maximum speed change ΔV w and the maximum heading change ΔH w , and the speed is not less than 0.

[0098] 3. Use search optimization method to solve the problem.

[0099] Taking the ant colony algorithm as an example, the main process is as follows:

[0100] (1) Initialization parameters. Initialize relevant parameters at the beginning of the calculation, such as the ant colony size n ant , maximum number of iterations iter max , pheromone volatility factor ρ, transition probability constant P0, local search step length r step wait.

[0101] (2) Calculate the state transition probability. Randomly generate the initial positions of each ant in the ant colony within the range of the optimization variable, calculate the objective function value as the initial pheromone, and calculate the state transition probability of the ant numbered k at the iter iteration.

[0102]

[0103] where k = 1, 2, ..., n ant , max is the maximum value of pheromone, Ta k is the pheromone of the ant numbered k.

[0104] (3) Position update. If the state transition probability is not greater than the transition probability constant P0, a local search is performed. The search formula is:

[0105] x new =x old +μr step λ (13)

[0106] where x old 、x new is the value of the optimization variable before and after the update of the ant, μ is a random number in the interval [-1,1], and λ is the inverse of the current iteration number; if the state transition probability is greater than the transition probability constant P0, a global search is performed, and the search formula is

[0107] x new =x old +μr range / 2 (14)

[0108] Where μ is a random number in the interval [-1,1], r range The length of the interval for optimizing variable values.

[0109] (4) Pheromone update. The pheromone update method is:

[0110] Ta new =(1-ρ)Ta old -f (15)

[0111] Among them old 、 new is the pheromone before and after the update, and f is the objective function value.

[0112] (5) Determine whether to terminate. If the number of iterations is not less than iter max , then output the current optimal objective function value and the corresponding optimization variable, otherwise return to step (2) for the next iteration.

[0113] That is, at each moment when the route optimization can be performed, by controlling the speed and direction of the underwater vehicle at each moment, the position of the underwater vehicle at the next moment is adjusted, thereby minimizing the geometric precision factor GDOP, and finally generating the optimal observation route for the underwater vehicle.

[0114] The above are preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for generating an observation route by solving a pure bearing element, characterized in that: The steps include: S101, observe the position and motion relationship between the underwater navigation body and the target ship to model; S102, constructing a geometric precision factor optimization function based on the target position and velocity estimation errors of the underwater vehicle; S103, using ant colony algorithm to solve.

2. The method for generating an observation route by solving a pure bearing element according to claim 1, characterized in that: The modeling process includes: Assuming that the target ship is moving in a straight line at a constant speed, W0 and M0 represent the initial positions of the underwater vehicle and the target ship when the underwater vehicle finds the target. If the measurement time interval is T, in the i-th measurement, the underwater vehicle and the target ship are at W i (x wi ,y wi )、M i (x mi ,y mi ) position, H w , H m t i The heading of the underwater vehicle and the target ship at the moment, v w 、v m t i The speed of the underwater vehicle and the target ship at the moment; F i is the target azimuth at the time of the i-th measurement. Assume that the velocity components of the target in the x and y directions are If a total of n measurements are made, we get formula 1:

3. The method for generating an observation route by solving pure bearing elements according to claim 2, characterized in that: The calculation process of the geometric precision factor optimization function is as follows: Differentiating the above formula, we get The matrix equation is dV=HdX in dV=[dF0 dF1...dF n ] T in The positioning error can be obtained as dX=(H T H) -1 HdV make B=(H T H) -1 H Then the covariance matrix of the positioning error is P dX =Bcov(dV)B T in in is the variance of the angle measurement error, and the geometric precision factor GDOP result is 4. The method for generating an observation route by solving a pure bearing element according to claim 3, characterized in that: The calculation process of the next moment position of the underwater vehicle is: x w(i+1) =x wi +V wi born wi and w(i+1) =and wi +V wi cosH wi (i＝0,1,2,...) The optimization problem to be solved can be written as Among them, (x wi ,y wi ) is the current position, (x w(i+1) ,y w(i+1) ) is the position at the next moment, H wi is the heading of the underwater vehicle between the current moment and the next moment, V wi is the speed of the underwater vehicle between the current moment and the next moment.

5. The method for generating an observation route by solving a pure bearing element according to claim 1, characterized in that: The solution process using the ant colony algorithm is as follows: S201, initializing parameters; S202, calculating the state transition probability of the ant; S203, performing location update; S204, updating pheromones; S205: Determine whether to terminate.

6. The method for generating an observation route by calculating pure bearing elements according to claim 5, characterized in that: The parameters include the size of the ant colony n ant , maximum number of iterations iter max , pheromone volatility factor ρ, transition probability constant P0, local search step length r step .

7. The method for generating an observation route by calculating pure bearing elements according to claim 6, characterized in that: Calculate the state transition probability of the ant numbered k at the iter iteration: where k = 1, 2, ..., n ant , max is the maximum value of pheromone, Ta k is the pheromone of the ant numbered k.

8. The method for generating an observation route by solving a pure bearing element according to claim 5, characterized in that: The location update calculation process is as follows: If the state transition probability is not greater than the transition probability constant P0, a local search is performed, and the search formula is: x new =x old +μr step l where x old 、x new is the value of the optimization variable of the ant before and after the update, μ is a random number in the interval [-1,1], and λ is the inverse of the current number of iterations; If the state transition probability is greater than the transition probability constant P0, a global search is performed, and the search formula is: x new =x old +μr range / 2 Where μ is a random number in the interval [-1,1], r range The length of the interval for optimizing variable values.

9. The method for generating an observation route by solving a pure bearing element according to claim 5, characterized in that: The location update calculation process is as follows: The pheromone updating method is Ta new =(1-ρ)Ta old -f Among them old 、 new is the pheromone before and after the update, and f is the objective function value.

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