Multi-type measurement and control equipment comprehensive station distribution planning method based on genetic algorithm

Abstract

The invention discloses a multi-type measurement and control equipment comprehensive station distribution planning method based on a genetic algorithm, and the method comprises the following steps: configuring parameters for subsequent steps; loading data; the minimum geometric accuracy factor GDOP of the whole trajectory process is used as a station distribution planning objective function, the performance of measurement and control equipment, the station site condition, the link level and the number of observation stations are used as constraint conditions, the genetic algorithm is adopted to solve the layout objective function, and the optimal solution is obtained by optimizing a chromosome coding mode in the algorithm. Reasonable layout is realized for performing high-precision measurement on a target by adopting multiple types of measurement stations. According to the method, constraint conditions are constructed by using information such as task guarantee time periods, alternative sites, theoretical ballistic trajectories, equipment types, equipment quantity, equipment precision and equipment performance parameters, the ballistic trajectory precision is taken as a target function, a genetic algorithm is adopted for solving, and a chromosome coding mode in the algorithm is optimized, so that the accuracy of the target ballistic trajectory is improved. And a new thought is provided for the optimal station distribution problem of the measurement and control equipment.

Description

Technical Field

[0001] This invention relates to the field of aerospace telemetry and control technology, and in particular to a method for comprehensive deployment planning of multiple types of telemetry and control equipment based on genetic algorithms. Background Technology

[0002] Currently, the planning of telemetry and control equipment deployment is an important part of the field of ballistic measurement outside the target range. Its essence is to generate a scientific and reasonable telemetry and control deployment plan by simulating and analyzing the overall telemetry and control situation of the mission, given prior information such as telemetry and control equipment information, theoretical trajectory, and target positioning algorithm. This provides a theoretical basis for mission organization and planning, thereby improving the measurement accuracy of the target.

[0003] Currently, most station deployment planning problems focus on single types of telemetry and control (TT&C) equipment, failing to optimize deployment for multiple types. Because the observable range and measurement accuracy of a single type of TT&C equipment often cannot meet the requirements of range testing, even under geometrically optimal deployment, it is sometimes insufficient. Therefore, multiple types of TT&C equipment are needed to simultaneously track and measure targets. How to efficiently utilize existing measurement equipment at the range to form a network for high-precision target measurement is the key focus of current station deployment planning problems. Summary of the Invention

[0004] The purpose of this invention is to provide a comprehensive site planning method for multiple types of measurement and control equipment based on genetic algorithms. This method can expand the traditional site planning problem from a single type of measurement and control equipment to multiple types of measurement and control equipment, and optimize the encoding method in the genetic algorithm to make the site planning algorithm more applicable.

[0005] The technical solution adopted in this invention is as follows:

[0006] A comprehensive deployment planning method for multi-type telemetry and control equipment based on genetic algorithms includes the following steps: A: Configuring parameters for subsequent steps; including the selection of the coordinate system for the theoretical trajectory, which includes a geocentric rectangular coordinate system and a launch coordinate system. The selection is based on the actual coordinate system of the theoretical trajectory, the latitude and longitude of the launch point, the azimuth angle, and the antenna installation angle. The operating mode selection includes response mode and reflection mode. In response mode, a radiation pattern needs to be loaded to facilitate subsequent level calculations. In reflection mode, an RCS file needs to be loaded. The mission support period includes the start time t1 and the end time t2 for calculating the selected mission. A: Data loading: Includes ballistic data file, whose data columns include: time, x-direction position, y-direction position, z-direction position, x-direction velocity, y-direction velocity, z-direction velocity; station site data file, whose data columns include the number of N1 candidate stations and the station's latitude, longitude, and altitude; equipment information file, whose data columns include the number, name, type, measurement accuracy, operating distance, and observation elevation angle limit of N2 equipment; radiation pattern data; RCS data file; link level loss and receive level threshold file;

[0007] C: The objective function for station deployment planning is to minimize the geometrical precision factor (GDOP) throughout the ballistic trajectory. The constraints are the performance of the telemetry and control equipment, the site conditions, the link level, and the number of observation stations. A genetic algorithm is used to solve the deployment objective function. By optimizing the chromosome encoding method in the algorithm, a reasonable layout can be achieved by using multiple types of measurement stations to perform high-precision measurements on the target. The station deployment planning objective function is divided into an optical intersection accuracy model, a radar measurement accuracy model, a multi-velocity measurement accuracy model, and a heterogeneous sensor combination positioning accuracy model.

[0008] The constraints in step C are established by comparing the measurement elements of the equipment with the performance indicators of the equipment. First, the distance, azimuth, elevation angle, ground station receiving level, transponder receiving level calculated in response mode, and shielding angle of the station relative to the target are calculated. For optical equipment, the solar angle also needs to be calculated. Then, these are compared with the corresponding performance indicators of the equipment to determine whether the constraints are met.

[0009] The constraints include equipment performance constraints, site conditions constraints, link level constraints, and the number of observation stations, leading to the general expression of the optimization problem:

[0010]

[0011] In step C, the genetic algorithm is initialized by defining the initialization parameters of the genetic algorithm, including the maximum number of iterations, the population size, the encoding method of individuals, the selection probability, the crossover probability, the mutation probability, and the fitness function. The fitness function is the reciprocal of the GDOP value corresponding to the deployment situation represented by each individual. The calculation of the GDOP value is related to the equipment type, which is mainly divided into four categories: optical computing equipment, radar computing equipment, multi-velocity computing equipment, and heterogeneous sensor combination computing equipment.

[0012] The genetic algorithm solution process in step C is as follows: First, the fitness of individuals in the initial population is calculated. Then, based on the device type and deployment site selected by the individual, and combined with the data corresponding to different sites, it is determined whether the current individual meets the constraints. If an individual does not meet the constraints, the fitness of this individual is set to the maximum value of 10000. Then, the individuals in the population are selected, crossover, and mutated in sequence to generate a new population until the number of iterations reaches the preset maximum number of iterations. Finally, the individual with the best fitness in the population is selected as the final deployment scheme.

[0013] The construction of the site deployment planning objective function in step C specifically includes the following steps:

[0014] Optical intersection accuracy model: First, establish the equation between the measurement element AE and the ballistic position parameters. Then, calculate the partial derivative of the measurement element AE with respect to the ballistic position parameters according to the equation to form the Jacobian matrix. At the same time, combine the measurement error of the equipment to obtain the error covariance matrix of the ballistic position parameters, and then obtain the optical intersection accuracy model.

[0015] Radar measurement accuracy model: including single-station measurement accuracy model and multi-station measurement accuracy model; specifically including the following steps: First, establish the equation between the measurement element RAE and the ballistic position parameters. Then, calculate the partial derivative of the measurement element RAE with respect to the ballistic position parameters according to the equation to form the Jacobian matrix. At the same time, combine the measurement error of the equipment to obtain the error covariance matrix of the ballistic position parameters, and thus obtain the radar measurement accuracy model. The difference between single-station and multi-station models is the dimension of the Jacobian matrix, while the other calculation processes are the same.

[0016] Multi-velocity accuracy model: First, establish the measurement elements Equations relating ballistic position and velocity parameters are used, and then the measurement elements are calculated based on these equations. The partial derivatives of the ballistic position and velocity parameters are used to construct the Jacobian matrix. At the same time, the error covariance matrix of the ballistic position and velocity parameters is obtained by combining the measurement error of the equipment, and then the multi-velocity measurement accuracy model is obtained.

[0017] Heterogeneous sensor combination positioning accuracy model: When there are multiple device types, firstly, the Jacobian matrix calculated in the above accuracy model is called according to different devices. Then, the Jacobian matrices of different device types are spliced ​​together. At the same time, the error covariance matrix of ballistic position and velocity parameters is obtained by combining the measurement error of the devices, and then the heterogeneous sensor combination positioning accuracy model is obtained.

[0018] The calculation of the GDOP value in step A specifically includes the following steps:

[0019] Let the position and velocity vectors be... The covariance matrix is

[0020]

[0021] Then the GDOP of any point X on the target trajectory can be defined as:

[0022] Let x, y, and z be the variances of the positions, respectively. denoted as ρ, where ρ is the variance of velocity in the x, y, and z directions; w is the weighting coefficient of position relative to velocity, balancing the dimensions of position and velocity accuracy; the smaller the GDOP value, the better the station geometry.

[0023] A computer-readable storage medium storing a computer program thereon, wherein when the computer program is executed by a processor, the device on which the computer-readable storage medium is located executes the comprehensive site planning method for multi-type measurement and control equipment based on genetic algorithm.

[0024] An electronic device includes a memory and a processor, wherein the memory stores a program that can run on the processor, and the processor executes the program to implement the comprehensive site planning method for multi-type measurement and control equipment based on genetic algorithm.

[0025] This invention constructs constraints by utilizing information such as mission support time period, alternative station sites, theoretical trajectory, equipment type, equipment quantity, equipment accuracy, and equipment performance parameters (minimum elevation angle, maximum angular acceleration, etc.), and uses trajectory accuracy as the objective function. It then employs a genetic algorithm to solve the problem and optimizes the station deployment of telemetry and control equipment by optimizing the chromosome encoding method in the algorithm. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a diagram showing the relationship between the measurement station origins O1 and O2 and the spatial target positions according to the present invention.

[0028] Figure 2 This invention relates to the ground projection of the target trajectory and the location distribution of candidate stations;

[0029] Figure 3 This illustrates the iterative change trend of the objective function of the genetic algorithm in this invention.

[0030] Figure 4 The optimal site placement results of the genetic algorithm are displayed;

[0031] Figure 5 The optimal site layout results for the enumeration method are shown.

[0032] Figure 6 This is a flowchart of the improved genetic algorithm described in this invention;

[0033] Figure 7 This is a flowchart of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] like Figure 6 and 7 As shown, the present invention includes the following steps:

[0036] A: Configuration parameters are used in subsequent steps; these include the selection of the coordinate system for the theoretical trajectory, including a geocentric rectangular coordinate system and a launch coordinate system. The selection is based on the actual coordinate system of the theoretical trajectory, including the latitude and longitude of the launch point, the azimuth angle, and the antenna installation angle. The operating mode selection includes response mode and reflection mode. In response mode, a radiation pattern needs to be loaded for subsequent level calculations. In reflection mode, an RCS file needs to be loaded. The mission support period includes the start time t1 and end time t2. The GDOP value corresponding to the selected mission support period is calculated, thus determining the fitness of individuals in the genetic algorithm. Since the station deployment planning problem is essentially an optimization problem, the two key elements of optimization problem modeling are the objective function and constraints. From the problem background, the objective function should primarily focus on ballistic accuracy, and the constraints should include limitations imposed on measurements by equipment, environment, and calculation methods. Our goal is to select the optimal station deployment scheme to obtain optimal accuracy; therefore, the objective function of the optimization problem can be established based on the geometrical precision factor (GDOP) throughout the trajectory.

[0037] The calculation of the GDOP value in step A specifically includes the following steps:

[0038] Let the position and velocity vectors be... The covariance matrix is

[0039]

[0040] Then the GDOP of any point X on the target trajectory can be defined as:

[0041] Let x, y, and z be the variances of the positions, respectively. _x_, _y_, _z_ variance of velocity; _w_ is the weighting coefficient of position relative to velocity, balancing the dimensions of position and velocity accuracy; the smaller the GDOP value, the better the station geometry. B: Data Loading: Includes a ballistic data file, whose data columns include time: x-direction position, y-direction position, z-direction position, x-direction velocity, y-direction velocity, z-direction velocity; a station site data file, whose data columns include the numbers of N1 candidate stations and the station's latitude, longitude, and altitude; an equipment information file, whose data columns include the numbers, names, types, measurement accuracy, operating range, and observation elevation angle limits of N2 equipment; radiation pattern data; RCS data file; link level loss and receive level threshold files;

[0042] C: The objective function for station deployment planning is to minimize the geometrical precision factor (GDOP) throughout the ballistic trajectory. The constraints are the performance of the telemetry and control equipment, the site conditions, the link level, and the number of observation stations. A genetic algorithm is used to solve the deployment objective function. By optimizing the chromosome encoding method in the algorithm, a reasonable layout can be achieved by using multiple types of measurement stations to perform high-precision measurements on the target. The station deployment planning objective function is divided into an optical intersection accuracy model, a radar measurement accuracy model, a multi-velocity measurement accuracy model, and a heterogeneous sensor combination positioning accuracy model.

[0043] The constraints in step C are established by comparing the equipment measurement elements with the equipment performance indicators. First, the distance, azimuth, elevation angle, ground station receiving level, transponder receiving level calculated in response mode, and shielding angle of the station relative to the target are calculated. For optical equipment, the solar angle also needs to be calculated. Then, these are compared with the corresponding index range of the equipment performance to determine whether the constraints are met.

[0044] The constraints include equipment performance constraints, site conditions constraints, link level constraints, and the number of observation stations, leading to the general expression of the optimization problem:

[0045]

[0046] Where T represents the total time point of the ballistic trajectory, and t represents the corresponding time point. Indicates the positional accuracy in three directions. The velocity accuracy in three directions, f it This indicates site location constraints, g it Indicates the solar angle constraint, h it Indicates link level constraints, Sop it This indicates the number of positioning elements the device has, Sov. it This indicates the number of speed measuring elements that the device has.

[0047] The construction of the site deployment planning objective function in step C specifically includes the following steps:

[0048] Optical intersection accuracy model: First, establish the equation between the measurement element AE and the ballistic position parameters. Then, calculate the partial derivative of the measurement element AE with respect to the ballistic position parameters according to the equation to form the Jacobian matrix. At the same time, combine the measurement error of the equipment to obtain the error covariance matrix of the ballistic position parameters, and then obtain the optical intersection accuracy model.

[0049] Optical rendezvous and positioning typically involves two methods. One method uses two-station angular measurement data A and E (azimuth and elevation angles) and employs a combination of the "L", "K", and "M" formulas to solve for the target position. This involves projecting the measured target point onto different planes and using geometric relationships to calculate the ballistic parameters. The other method uses angular measurement data A and E from two or more stations and employs the least squares method. To facilitate the provision of accuracy calculation formulas and fully utilize measurement information, this invention first uses the "L", "K", or "M" formulas to obtain initial values ​​for the ballistic position parameters. Then, iteratively solving for the ballistic position and velocity information using the least squares method, and finally using the error propagation principle to calculate the ballistic estimation accuracy.

[0050] a) Formulas for “L”, “K”, and “M”

[0051] Assuming Oxyz is the launch coordinate system, there are two stations, O1 and O2, tracking a target M. Their positional relationship is as follows: Figure 1 As shown. Let the position parameters of target M be (x, y, z), and the coordinates of the two stations are known. 0i ,y 0i ,z 0i (i=1,2), the azimuth and elevation angles of the target point M measured by the two stations are A and B, respectively. i and E i (i = 1, 2).

[0052] 1) "L" formula

[0053] The target point M is now projected onto the horizontal plane xOz of the launch coordinate system. Using geometric relationships, the coordinates x and z of the projected point M on the horizontal plane of the launch coordinate system are calculated, and then the y coordinate of point M is obtained. The "L" formula has two similar expressions; only one set is introduced here.

[0054]

[0055] In the formula,

[0056] 2) "K" formula

[0057] Formula "K" projects the target point M onto the vertical plane yOz in the launch coordinate system, derives the y and z coordinates through geometric relationships, and finally determines the x coordinate. The specific expression is:

[0058]

[0059] In the formula,

[0060]

[0061] 3) "M" formula

[0062] The "M" formula projects the target point M onto another perpendicular plane xOy in the launch coordinate system, derives the x and y coordinates through geometric relationships, and finally determines the z coordinate. The specific expression is:

[0063]

[0064] In the formula,

[0065]

[0066] The selection criteria for the three sets of formulas "L", "K" and "M" are determined by whether their respective determinants approach zero; if they approach zero, they cannot be selected.

[0067] b) Least squares estimation method

[0068] Assuming that two or more optical devices can simultaneously observe the target at each measurement moment, at least four measurement elements can be obtained, while the position parameters to be determined are three. Therefore, the least squares method can be used to estimate the ballistic parameters.

[0069] First, establish the measurement element A. i and E i The relationship between the target position parameters x, y, and z is as follows:

[0070]

[0071] In the formula, x i ,y i ,z i Let be the station coordinates of the i-th station in the launch coordinate system.

[0072] Since the above measurement equations are nonlinear, they need to be linearized, and then the ballistic position parameters are calculated using least squares estimation. Taking a three-optical-device joint measurement as an example, the initial value x of the ballistic position parameter can be obtained using any one of the three sets of formulas "L", "K", and "M". 0 ,y 0 ,z 0 The measurement equations are linearized and given in matrix form.

[0073] ΔL=AΔx+ζ

[0074] In the formula, Δx is the correction value of the ballistic position vector, Δx=[xx 0 yy 0 ,zz 0 ], where x, y, z are the ballistic position components, x 0 ,y 0 ,z 0 These are the initial values ​​for the ballistic position components.

[0075] From the initial ballistic parameters x 0 ,y 0 ,z 0 Substituting into the system of equations yields the result.

[0076] A is the Jacobian matrix of the partial derivatives of the optical theodolite measurement data with respect to the ballistic parameters x, y, z:

[0077]

[0078] Assuming that the random errors among the measurement data are uncorrelated, their covariance matrix is ​​denoted as... in It depends on the equipment performance.

[0079] The precise ballistic parameters of the target are obtained by least squares estimation.

[0080]

[0081] and parameter vector The error covariance matrix is

[0082]

[0083] Since optical intersection measurement only locates the target and does not measure its velocity, the objective function for optical intersection positioning can be obtained as follows:

[0084]

[0085] The radar measurement accuracy model includes a single-station measurement accuracy model and a multi-station measurement accuracy model. Specifically, it includes the following steps: First, establish the equation between the measurement element RAE and the ballistic position parameters. Then, calculate the partial derivative of the measurement element RAE with respect to the ballistic position parameters based on the equation to construct the Jacobian matrix. At the same time, combine the measurement error of the equipment to obtain the error covariance matrix of the ballistic position parameters, and thus obtain the radar measurement accuracy model. The difference between single-station and multi-station models lies in the dimension of the Jacobian matrix, while other calculation processes are the same.

[0086] The radar equipment measures data including R, A, and E (distance from the target to the station, azimuth, and elevation). First, the measurement element A is established. i E i and R i The relationship between the target position parameters x, y, and z is as follows:

[0087]

[0088] Similar to the optical intersection model, since the above measurement equations are nonlinear, they need to be linearized, and then the ballistic position parameters are solved using least squares estimation. Taking the joint measurement of n (n≥1) radar devices as an example, the Jacobian matrix of the partial derivatives of the radar measurement data with respect to the ballistic parameters x, y, z can be obtained according to the error propagation algorithm:

[0089]

[0090] Assuming that the random errors among the measurement data are uncorrelated, their covariance matrix is ​​denoted as... in Determined by equipment performance. The final parameter vector can be obtained. The error covariance matrix is

[0091]

[0092] Since radar equipment only locates targets and does not measure their velocity, the target function for radar localization can be obtained as follows:

[0093]

[0094] The multi-velocity accuracy model includes solving the observation equations and nonlinear equations, and using a Taylor expansion linearization iterative process to obtain the ballistic position and velocity. Specifically, it includes the following steps:

[0095] First, establish the measurement elements. Equations relating ballistic position and velocity parameters are used, and then the measurement elements are calculated based on these equations. The partial derivatives of the ballistic position and velocity parameters are used to construct the Jacobian matrix. At the same time, the error covariance matrix of the ballistic position and velocity parameters is obtained by combining the measurement error of the equipment, and then the multi-velocity measurement accuracy model is obtained.

[0096] Observation Let j be the distance from the master station (xs0, ys0, zs0) to the target (x) at time j. j ,y j ,z j ) and the target to the secondary station (xs) i ,ys i ,zs i The radial velocity of ) is given by the following formula:

[0097]

[0098] in, Let i = 0, 1, ..., n be the receiving station index, n be the number of receiving stations in the speed measurement system, and subscript 0 represent the master station. When the master station's signal transmitting station and receiving station are at the same location, we take xs0 = xs1, ys0 = ys1, zs0 = zs1, R 0j=R 1j .

[0099] Since the above measurement equations are nonlinear, they need to be linearized before the ballistic position parameters are calculated using least squares estimation. The error propagation algorithm is still used when constructing the accuracy model, first calculating the measurement data... ballistic parameters Jacobian matrix of partial derivatives:

[0100]

[0101] Assuming that the random errors among the measurement data are uncorrelated, their covariance matrix is ​​denoted as... in Determined by equipment performance. The final error covariance matrix of the parameter vector is:

[0102]

[0103] The accuracy function for positioning and speed measurement of multiple speed measuring devices is defined as follows:

[0104]

[0105] Heterogeneous sensor combination positioning accuracy model: When there are multiple device types, firstly, the Jacobian matrix calculated in the above accuracy model is called according to different devices. Then, the Jacobian matrices of different device types are spliced ​​together. At the same time, the error covariance matrix of ballistic position and velocity parameters is obtained by combining the measurement error of the devices, and then the heterogeneous sensor combination positioning accuracy model is obtained.

[0106] When using heterogeneous sensor combinations (optical, radar, and multiple velocity measurement) for localization, a solution model needs to be considered. Similar to the accuracy model for multiple velocity measurement, the measurement element equations need to include optical and radar components. The accuracy model for heterogeneous sensor combination localization is as follows:

[0107] First, establish the corresponding observation equations:

[0108]

[0109] Where i = 0, 1, ..., n represents multiple velocity measurement radar stations, and k = n+1, ..., n+m represents optical and radar stations. A kj E kj R kj Let be the azimuth, elevation, and slant range of the optical and radar stations at time j. The accuracy calculation method is similar to the one described above. First, calculate the Jacobian matrix of the measured element with respect to the ballistic parameters. Then, calculate the error covariance matrix of the parameter vector based on the measurement error of the equipment.

[0110]

[0111] The accuracy function of heterogeneous sensor combination positioning is defined as:

[0112]

[0113] In step C, the genetic algorithm is initialized by defining the initialization parameters of the genetic algorithm, including the maximum number of iterations, the number of people in the population, the encoding method of individuals, the selection probability, the crossover probability, the mutation probability, and the fitness function. The fitness function is the reciprocal of the GDOP value corresponding to the station layout represented by each individual. The calculation of the GDOP value is related to the equipment type, which is mainly divided into four categories: optical equipment, radar equipment, multi-speed measurement equipment, and heterogeneous sensor combination equipment.

[0114] The genetic algorithm solution process in step C is as follows: First, the fitness of individuals in the initial population is calculated. Then, based on the device type and deployment site selected by the individual, and combined with the data corresponding to different sites, it is determined whether the current individual meets the constraints. If an individual does not meet the constraints, the fitness of this individual is set to the maximum value of 10000. Then, the individuals in the population are selected, crossover, and mutated in sequence to generate a new population until the number of iterations reaches the preset maximum number of iterations. Finally, the individual with the best fitness in the population is selected as the final deployment scheme.

[0115] The following provides a further explanation of the constraints, including equipment performance constraints.

[0116] The equipment at each station site must meet the following conditions in order to observe the target, and the i-th station must meet these conditions at time t:

[0117]

[0118] D it ≤D max

[0119] Among them, E it , A it , D it Let E be the pitch angle, pitch velocity, pitch acceleration, azimuth angle, azimuth velocity, azimuth acceleration, and distance to the target at time t for the i-th station. min , A min , E represents the lower limits of pitch angle, pitch velocity, pitch acceleration, azimuth angle, azimuth velocity, and azimuth acceleration. max , A max , D max Let f be the pitch angle, pitch rate, pitch acceleration, azimuth angle, azimuth rate, azimuth acceleration, and the upper limit of the distance to the target. These constraints can be denoted as f. ijt If the constraint condition is met, then f ijt =1, otherwise f ijt =0, which means the j-th constraint at time t of the i-th station.

[0120] For ease of representation, the above conditions can be represented by constraint f. it It means, that is

[0121]

[0122] Site constraints

[0123] The main constraints of the site itself are the topographical constraints, such as the shading angle and the angle of the sun.

[0124] Let the azimuth angle of the i-th station be A j The corresponding pitch angle should be greater than the azimuth angle A. j The angle of cover at time

[0125]

[0126] The angle between the optical equipment target, the sun, and the station at the i-th station must be greater than or equal to the threshold α. min .

[0127] α it ≥α min

[0128] For ease of representation, the above conditions can be represented by constraint g. it It means, that is

[0129]

[0130] Where g ijt That is, the j-th constraint at time t of the i-th station.

[0131] Link level constraints

[0132] Link level constraints are divided into two types: cooperative measurement and non-cooperative measurement. Cooperative measurement requires that the received signal level at the station be greater than or equal to the accuracy-guaranteed level. If there is an uplink signal, the received signal level of the transponder must meet certain conditions, which requires the radiation pattern of the transponder antenna or a radiation pattern model. Non-cooperative measurement requires that the received signal level be greater than the accuracy-guaranteed threshold, which requires target RCS characteristic data or the establishment of an RCS model and the acquisition of model parameters.

[0133] Collaborative measurement:

[0134] El it ≥El i_min

[0135] El t ≥El obj_min

[0136] El it Let El be the voltage level value of the i-th station at time t. min Let be the received level threshold for the i-th station. t Let El be the received signal level of the target transponder at time t. obj_min The target received level threshold.

[0137] Non-cooperative measurement:

[0138] El it ≥El i_min

[0139] For ease of representation, the constraints at time t of the i-th station can be represented by constraint h. it It indicates that device S i There are two possible measurement modes, with the set of devices for collaborative measurement being S. co The set of non-cooperative measurement devices is S. re ,but

[0140]

[0141] Constraints on the number of observation stations

[0142] In the arc segment required by the telemetry and control mission, positioning needs to meet at least three or more effective positioning elements, and velocity measurement needs to meet at least three or more effective velocity measurement elements. Based on this, constraints can be established.

[0143] Let T be the time set of the telemetry and control mission, and S be the set of equipment to be deployed. i The number of positioning elements it possesses is Sop i The number of speed measurement elements is Sov. i .

[0144]

[0145] General expression

[0146] Based on the above summary, the general expression for the optimization problem can be obtained as follows:

[0147]

[0148] Assuming there are n1 devices and n2 candidate sites, the goal of site deployment is to select n1 sites from the n2 candidate sites and then place different types of equipment at each selected site. Based on the problem description above, assuming all equipment types are different, our possible solutions are: This invention addresses the complex nonlinear optimization problem described above, which is difficult to solve using general gradient-based optimization methods and is prone to local optima. Furthermore, when the number of candidate sites is large, the traditional exhaustive search method is computationally intensive and inefficient. This invention employs an improved genetic algorithm that can find the optimal geometry in a shorter time, especially when the number of candidate sites is large, it can quickly and effectively find the global optimum and obtain the optimal site layout geometry. The specific solution steps are as follows:

[0149] (1) Population initialization:

[0150] First, the initial population size is set, and then the encoding method of the population is determined. This invention combines the 0 / 1 knapsack problem and the classic traveling salesman problem: Selecting n1 stations from n2 candidate stations can be simplified to a 0 / 1 knapsack problem, where selected stations are represented by 1 and unselected stations by 0. Arranging n1 devices among the selected n1 stations is the classic traveling salesman problem, i.e., a permutation problem. The specific encoding and initialization steps are as follows:

[0151] The number of bits (n2+n1) in the chromosome code for a single individual is determined based on the number of devices (n1) and the number of candidate sites (n2). The first n2 bits are binary, where 1 indicates that a corresponding site has been selected and 0 indicates that no site has been selected. The last n1 bits are integers, representing the correspondence between sites and devices. Assuming the number of devices to be planned is 5 (n1) and the number of candidate sites is 8 (n2), the possible coding formats for a single individual are as follows:

[0152]

[0153] The first part of the encoding format indicates that five stations (numbers 1, 4, 6, 7, and 8) were selected from a pool of eight candidate stations. Since these stations may contain various types of equipment, different devices need to be placed at the selected stations. The second part, a 5-bit code, represents the correspondence between stations and devices. The above code indicates that devices 3, 4, 2, 1, and 5 are placed at stations 1, 4, 6, 7, and 8, respectively. That is, the station placement scheme represented by this individual is: station 1 - device 3, station 4 - device 4, station 6 - device 2, station 7 - device 1, and station 8 - device 5. This encoding method can describe all individuals in the entire population.

[0154] (2) Individual choice:

[0155] The tournament selection algorithm is adopted, which involves repeatedly selecting a certain number of individuals with replacement from the population each time, and then selecting the individual with the highest fitness function. This operation is repeated until the new population size reaches the set size.

[0156] (3) Individual crossover:

[0157] Individual crossover refers to the process of randomly selecting two individuals from a population with a certain probability and exchanging a segment of their chromosomes according to a certain rule, thereby generating two new individuals. First, the crossover probability f of the population is set. c Then generate a random number 0 < r < 1, if r > f c Then, the first part of the code of the i-th individual is crossed with that of the (i+1)-th individual. Simultaneously, a random number is used to determine the starting point of the crossover between the two individuals. All genes between the random crossover point and n² positions of the chromosome code length are exchanged. The exchange principle is as follows: Assume the generated random number (i.e., the random crossover point position is 4) is 4.

[0158] Before the crossover:

[0159] After crossing:

[0160] Since the number of sites selected by the first individual after the crossover is less than the number of devices to be planned, one of its codes needs to be randomly changed from 0 to 1. Similarly, for the second individual, since the number of sites currently selected is greater than the number of devices to be planned, one of its codes needs to be randomly changed from 1 to 0. The new individuals generated after the crossover are as follows:

[0161] After crossing:

[0162] (4) Individual variation:

[0163] Mutation refers to changing a specific position on an individual's chromosome with a certain probability, thereby creating a new individual. This invention first sets the mutation probability and generates a random number. If the random number is greater than the mutation probability, the individual is mutated. If the number of candidate sites is greater than the number of devices to be planned, a position coded as 0 in the individual is randomly changed to 1, and a position coded as 1 is randomly changed to 0. If the number of candidate sites equals the number of devices to be planned, the order of the last n1 bits of the code is randomly shuffled. The mutation process is as follows:

[0164] 1) When the number of alternative sites exceeds the number of equipment to be planned

[0165] Before mutation: 1 0 0 1 0 1 1 1|3 4 2 1 5

[0166] After mutation: 1 1 0 1 0 1 0 1|3 4 2 1 5

[0167] 2) When the number of alternative sites equals the number of equipment to be planned

[0168] Before mutation: 1 1 1 1 1 | 3 4 2 1 5

[0169] After mutation: 1 1 1 1 1|1 3 5 4 2

[0170] Using MATLAB simulation tools, theoretical ballistics (launch system, firing angle 273°), equipment information, candidate site data, radiation patterns (antenna installation angle 100°, cooperative measurement) and other data were loaded. The ballistic observation time was set to 40-60 seconds (relative time). The genetic algorithm proposed in this invention was used for site optimization. The initial population size was set to 20,000, the crossover probability of the genetic algorithm was 0.95, the mutation probability was 0.15, and the maximum number of iterations was 20. Then, the optimal site layout scheme was determined using site planning software, and the results were displayed in the form of images and file records.

[0171] The iterative change trend of the objective function is as follows Figure 3 As shown, the optimal site placement results of the genetic algorithm and the GDOP results are as follows: Figure 4 As shown, the optimal site placement results of the enumeration method and GDOP are as follows: Figure 5 As shown in the figure. Simulation results show that the improved genetic algorithm can find the optimal result in the fifth iteration, and its optimal result (minGDOP) is consistent with the result of the enumeration method. That is, the improved genetic algorithm proposed in this paper can obtain the global optimal result and save a lot of computation time, thus being more efficient.

[0172] This invention utilizes a comprehensive site planning method for multiple types of measurement and control equipment based on a genetic algorithm, and designs and develops a site planning simulation software using the JAVA software development platform. Through interface parameter input and data loading, the software addresses the equipment planning and site selection problem before the task begins.

[0173] A computer-readable storage medium stores a computer program thereon. When executed by a processor, the computer program causes the device containing the computer-readable storage medium to perform the comprehensive site planning method for multi-type measurement and control equipment based on a genetic algorithm, as described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), random access memory, and other memories.

[0174] An electronic device includes a memory and a processor, wherein the memory stores a program that can run on the processor, and the processor executes the program to implement the integrated site planning method for multi-type measurement and control equipment based on genetic algorithms as described above.

[0175] If the modules / units integrated in the electronic device described in this application are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0176] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0177] The computer-readable storage medium stores computer-readable instructions, which are executed by a processor in an electronic device to implement the integrated site planning method for multi-type measurement and control equipment based on genetic algorithms as described in any of the above embodiments.

[0178] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0179] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0180] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0181] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0182] Note that the above description is merely a preferred embodiment and application of the technical principles of the present invention. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the specific embodiments described herein, and may include many other effective embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A comprehensive deployment planning method for multiple types of measurement and control equipment based on genetic algorithms, characterized in that: Includes the following steps: A: Configuration parameters, used in subsequent steps; This includes the selection of the coordinate system for the theoretical trajectory, which includes the geocentric rectangular coordinate system and the launch coordinate system. The selection is based on the actual coordinate system of the theoretical trajectory, including the latitude and longitude of the launch point, the azimuth angle, and the antenna installation angle. The operating mode selection includes response mode and reflection mode. In response mode, a radiation pattern needs to be loaded to facilitate subsequent level calculation. In reflection mode, an RCS file needs to be loaded. The mission support period includes the start time t1 and the end time t2. The GDOP value corresponding to the selected mission support period is calculated, and then the fitness of individuals in the genetic algorithm is obtained. B: Data loading: Includes ballistic data file, whose data columns include time, x-direction position, y-direction position, z-direction position, x-direction velocity, y-direction velocity, and z-direction velocity. The data file includes: station site data file, whose data columns include the station number, latitude, longitude, and altitude of N1 candidate stations; equipment information file, whose data columns include the number, name, type, measurement accuracy, operating distance, and observation elevation angle limit of N2 equipment; radiation pattern data; and RCS data file. Link level loss and receive level threshold file; C: The objective function for station deployment planning is to minimize the geometrical precision factor (GDOP) throughout the ballistic trajectory. The constraints are the performance of the telemetry and control equipment, the site conditions, the link level, and the number of observation stations. A genetic algorithm is used to solve the deployment objective function. By optimizing the chromosome encoding method in the algorithm, a reasonable layout can be achieved by using multiple types of measurement stations to perform high-precision measurements on the target. The station deployment planning objective function is divided into an optical intersection accuracy model, a radar measurement accuracy model, a multi-velocity measurement accuracy model, and a heterogeneous sensor combination positioning accuracy model.

2. The integrated deployment planning method for multiple types of measurement and control equipment based on genetic algorithm according to claim 1, characterized in that: The constraints in step C are established by comparing the equipment measurement elements with the equipment performance indicators. First, the distance, azimuth, elevation angle, ground station receiving level, transponder receiving level calculated in response mode, and shielding angle of the station relative to the target are calculated. For optical equipment, the solar angle also needs to be calculated. Then, these are compared with the corresponding index range of the equipment performance to determine whether the constraints are met. The constraints include equipment performance constraints, site conditions constraints, link level constraints, and the number of observation stations, leading to the general expression of the optimization problem:

3. The integrated deployment planning method for multiple types of measurement and control equipment based on genetic algorithm according to claim 1, characterized in that: In step C, the genetic algorithm is initialized by defining the initialization parameters of the genetic algorithm, including the maximum number of iterations, the population size, the encoding method of individuals, the selection probability, the crossover probability, the mutation probability, and the fitness function. The fitness function is the reciprocal of the GDOP value corresponding to the deployment situation represented by each individual. The calculation of the GDOP value is related to the equipment type, which is mainly divided into four categories: optical computing equipment, radar computing equipment, multi-velocity computing equipment, and heterogeneous sensor combination computing equipment.

4. The integrated station deployment planning method for multiple types of measurement and control equipment based on genetic algorithm according to claim 3, characterized in that: When determining the encoding method for individuals in the population, the 0-1 knapsack problem and the classic traveling salesman problem are combined. The specific encoding steps are as follows: Based on the number of devices n1 and the number of candidate sites n2, determine the number of bits n2+n1 of the chromosome encoding for a single individual. The first n2 bits of the encoding are binary, where 1 indicates that the corresponding site is selected and 0 indicates that the corresponding site is not selected. The last n1 bits are integers, representing the correspondence between the site and the device.

5. The integrated station deployment planning method for multiple types of measurement and control equipment based on genetic algorithm according to claim 1, characterized in that: The genetic algorithm solution process in step C is as follows: First, the fitness of individuals in the initial population is calculated. Then, based on the device type and deployment site selected by the individual, and combined with the data corresponding to different sites, it is determined whether the current individual meets the constraints. If an individual does not meet the constraints, the fitness of this individual is set to the maximum value of 10000. Then, the individuals in the population are selected, crossover, and mutated in sequence to generate a new population until the number of iterations reaches the preset maximum number of iterations. Finally, the individual with the best fitness in the population is selected as the final deployment scheme.

6. The integrated station deployment planning method for multiple types of measurement and control equipment based on genetic algorithm according to claim 1, characterized in that: The calculation of the GDOP value in step A specifically includes the following steps: Let the position and velocity vectors be... The covariance matrix is Then the GDOP of any point X on the target trajectory can be defined as: Let x, y, and z be the variances of the positions, respectively. denoted by , where is the variance of velocity in the x, y, and z directions; w is the position weighting coefficient, balancing the dimensions of position and velocity accuracy; the smaller the GDOP value, the better the station layout geometry.

7. The integrated deployment planning method for multiple types of measurement and control equipment based on genetic algorithm according to claim 1, characterized in that: The construction of the site deployment planning objective function in step C specifically includes the following steps: Optical intersection accuracy model: First, establish the equation between the measurement element AE and the ballistic position parameters. Then, calculate the partial derivative of the measurement element AE with respect to the ballistic position parameters according to the equation to form the Jacobian matrix. At the same time, combine the measurement error of the equipment to obtain the error covariance matrix of the ballistic position parameters, and then obtain the optical intersection accuracy model. The radar measurement accuracy model includes a single-station measurement accuracy model and a multi-station measurement accuracy model. Specifically, it includes the following steps: First, establish the equation between the measurement element RAE and the ballistic position parameters. Then, calculate the partial derivative of the measurement element RAE with respect to the ballistic position parameters based on the equation to construct the Jacobian matrix. At the same time, combine the measurement error of the equipment to obtain the error covariance matrix of the ballistic position parameters, and thus obtain the radar measurement accuracy model. The difference between single-station and multi-station models lies in the dimension of the Jacobian matrix, while other calculation processes are the same. The multi-velocity accuracy model includes solving the observation equations and nonlinear equations, and performing Taylor expansion and linearization. Specifically, it includes the following steps: First, establishing the measurement elements... Equations relating ballistic position and velocity parameters are used, and then the measurement elements are calculated based on these equations. The partial derivatives of the ballistic position and velocity parameters are used to construct the Jacobian matrix. At the same time, the error covariance matrix of the ballistic position and velocity parameters is obtained by combining the measurement error of the equipment, and then the multi-velocity measurement accuracy model is obtained. Heterogeneous sensor combination positioning accuracy model: When there are multiple device types, firstly, the Jacobian matrix calculated in the above accuracy model is called according to different devices. Then, the Jacobian matrices of different device types are spliced ​​together. At the same time, the error covariance matrix of ballistic position and velocity parameters is obtained by combining the measurement error of the devices, and then the heterogeneous sensor combination positioning accuracy model is obtained.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it causes the device containing the computer-readable storage medium to execute the integrated site planning method for multi-type measurement and control equipment based on genetic algorithms as described in any one of claims 1-7.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, wherein the memory stores a program that can run on the processor, and the processor executes the program to implement the integrated site planning method for multi-type measurement and control equipment based on genetic algorithms as described in any one of claims 1-7.

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