A hypersonic vehicle search method based on guidance information

Abstract

The present invention discloses a method for searching hypersonic aircraft based on guidance information, including: determining the search airspace of the radar according to the guidance information; establishing a hypersonic aircraft priority judgment model based on the target flight altitude, flight speed, and the distance between the target and the radar, and determining the search order according to the priority level of the aircraft to be searched; establishing a radar search model with the maximum cumulative detection probability, the shortest average discovery time, and the highest priority as the objective function; and proposing a hybrid optimization algorithm based on differential evolution and particle swarm algorithm to solve the radar search model, which enhances the diversity of the particle swarm and improves the global optimization ability of the algorithm. The present invention can allocate more search resources to targets with high threat levels according to the target priority; and can quickly adjust the radar search resource allocation plan in the scenario of multiple hypersonic aircraft attack, and give priority to capturing targets with high threat levels.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft search, and more specifically to a hypersonic aircraft search method based on guidance information. Background Art

[0002] Hypersonic vehicles have both strategic deterrence and tactical strike capabilities, and are characterized by strong maneuverability, long range, high speed, and accurate strike. With the continuous development and maturity of hypersonic technology, hypersonic weapons such as "Vanguard" and "Dagger" have been successively equipped, and the competition in near-space has intensified. Hypersonic vehicles have also become the focus and difficulty of aerospace defense.

[0003] When radar detects and searches for hypersonic aircraft, it usually hopes to capture the target in the shortest time and with the highest probability, which consumes a lot of radar resources. Radar usually not only performs search tasks, but also needs to perform tracking tasks. Therefore, how to improve search efficiency and optimize radar resource allocation when searching for hypersonic aircraft is an important issue.

[0004] Existing research on radar target search mainly focuses on the search of missiles and conventional aircraft. Usually, the maximum interception probability or the shortest search time is simply used as the objective function of the search. However, due to the strong maneuverability and special flight airspace of hypersonic aircraft, their target characteristics are different from those of conventional targets. There is currently a lack of public literature specifically for the search of hypersonic aircraft.

[0005] Therefore, in this context, research on hypersonic vehicle search algorithms is conducive to optimizing radar resources and improving search performance. Summary of the invention

[0006] The purpose of the present invention is to solve the problem that near-space hypersonic aircraft have strong maneuverability and special flight airspace, and a single radar has low search efficiency and high resource consumption when searching for targets. In order to overcome the above-mentioned shortcomings of the prior art, the present invention provides a hypersonic aircraft search method based on guidance information.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A hypersonic aircraft search method based on guidance information, the steps are as follows:

[0009] S1. Determine the radar search airspace according to the guidance information;

[0010] S2. Establish a hypersonic aircraft priority judgment model based on the target flight altitude, flight speed, and distance between the target and the radar, and determine the search order according to the priority level of the aircraft to be searched;

[0011] S3. A radar search model was established with the maximum cumulative detection probability, the shortest average discovery time and the highest priority as the objective function;

[0012] S4. A hybrid optimization algorithm based on differential evolution and particle swarm algorithm is proposed to solve the radar search model. This algorithm enhances the diversity of particle swarm and improves the global optimization ability of the algorithm.

[0013] S5. Verify the algorithm performance in a simulation scenario.

[0014] Technical effects and advantages of the present invention:

[0015] (1) A hypersonic vehicle priority judgment model is designed. Taking target altitude, target speed and target distance as indicators, a specific quantitative method is given. More search resources can be allocated to targets with high threat levels according to target priority.

[0016] (2) Taking into account the constraints of detection probability, dwell time, and total time resources, a radar search model with the maximum cumulative detection probability, the shortest average discovery time, and the highest priority as the objective function was established. In the scenario of multiple hypersonic aircraft attacking, the radar search resource allocation plan can be quickly adjusted to give priority to capturing targets with greater threats.

[0017] (3) A hybrid optimization algorithm based on differential evolution algorithm and particle swarm algorithm is proposed. The adaptive inertia weight and learning factor of the particle swarm algorithm are designed to improve the search efficiency. The mutation, crossover and selection operations of the differential evolution algorithm are used to perturb the particle swarm to enhance the diversity of the particle swarm, thereby improving the global optimization ability of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a curve diagram of the threat level change of the hypersonic aircraft altitude;

[0019] Figure 2 It is a curve diagram of the threat degree change of the speed of hypersonic aircraft;

[0020] Figure 3 It is a curve diagram of the threat level change of the distance of the hypersonic aircraft;

[0021] Figure 4 This is the relationship between the single detection probability and signal-to-noise ratio of a hypersonic vehicle;

[0022] Figure 5 This is the relationship between the number of pulse accumulation and signal-to-noise ratio of a hypersonic vehicle;

[0023] Figure 6 This is the relationship between the cumulative detection probability and the number of re-illuminations of hypersonic aircraft;

[0024] Figure 7 It is the inertia weight change curve of particle swarm algorithm;

[0025] Figure 8 This is the curve diagram of the learning factor change of the particle swarm algorithm;

[0026] Fig. 9 It is the change curve of the threshold value S;

[0027] Fig.10 This is the relationship between the total cumulative detection probability and the search task resource occupancy rate in the simulation experiment;

[0028] Fig.11 This is the relationship between the average discovery time and the resource occupancy rate of the search task in the simulation experiment;

[0029] Fig.12 The detection probability diagrams of three hypersonic targets in the simulation experiment under different search task resource occupancy rates by Method 1, Method 2 and the optimization method proposed by the present invention;

[0030] Fig.13 It is a time resource allocation diagram for three hypersonic targets under different search task resource occupancy rates of Method 1, Method 2 and the optimization method proposed by the present invention in the simulation experiment;

[0031] Fig.14 This is a comparison chart of the convergence of Method 1, Method 2 and the optimization method proposed by the present invention under different search task resource occupancy rates in the simulation experiment;

[0032] Fig.15 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0034] A hypersonic aircraft search method based on guidance information specifically comprises the following steps:

[0035] Step 1: Determine the radar search space based on guidance information

[0036] Guidance information provides an important basis for radar to search and capture targets as quickly as possible; it mainly includes estimated target position information, speed information and error information;

[0037] Assume that the position and speed of the hypersonic aircraft given by the guidance information are The radar needs to convert the guidance information of the target into azimuth and elevation information. The error of the guided information is expressed as The target’s true azimuth and elevation information is: Then we can get

[0038]

[0039] Assume that the probability density of the target appearing in the radar search space Ω is Then the probability of the target appearing in this airspace can be expressed as

[0040]

[0041] The size of the search airspace is mainly affected by the guidance information error. The smaller the guidance information error, the higher the accuracy of the guidance information, and the smaller the range of the search airspace. On the contrary, the larger the guidance information error, the lower the accuracy of the guidance information, and the larger the range of the search airspace. Although expanding the range of the search airspace is more conducive to covering the target and thus increasing the probability of capturing the target. However, an excessively large search airspace will also cause a waste of radar resources and reduce radar performance. Therefore, we usually determine the range of the search airspace based on the principle of three times the root mean square error (3δ). At the same time, according to the determined search airspace, the radar's search parameters are reasonably set to ensure that the radar's probability of intercepting the target in the search airspace is higher than the search threshold, that is,

[0042] P d ≥P conf (3)

[0043] Among them, P conf is the search threshold.

[0044] Step 2: Priority determination of hypersonic vehicles

[0045] Due to the distance and speed of the target, the priorities of different hypersonic vehicles are different. When performing a search mission, the radar will try to increase the probability of capturing the target with a high priority. Therefore, how to determine the target priority is the primary problem faced by the radar during the search process.

[0046] The target priority is usually determined based on guidance information. We propose three indicators to determine the priority of hypersonic vehicles: target altitude, target speed, and target distance.

[0047] (1) Target height

[0048] The target altitude reflects the target's motion state. For a hypersonic aircraft, the lower the flight altitude, the shorter the remaining flight time, the closer it is to its downward strike phase, the less time the defender has to respond, and the more difficult it is to intercept. Therefore, the lower the target altitude, the greater the threat. Hypersonic aircraft usually fly in an airspace range of 20km to 100km. The threat of the target altitude is modeled as a function of the interval [0, 1].

[0049]

[0050] Among them, H mid =(H max +H min ) / 2,H max =100km represents the upper limit of the flight altitude, H min =20km represents the lower limit of the flight altitude. Figure 1 shown.

[0051] (2) Target speed

[0052] The target speed reflects the target's attack capability. For a hypersonic vehicle, the faster the speed, the stronger the maneuverability, the stronger the attack capability, and the larger the attack range. Moreover, the high speed also makes it difficult for the detection system to find and track the target. Therefore, the faster the target speed, the greater the threat. The maneuvering speed of a hypersonic vehicle is usually between 5 and 20 Ma. The threat of the target speed is modeled as

[0053]

[0054] Among them, V = 20Ma represents the upper limit of the flight speed, γ represents the threat coefficient of the speed, and the value range of γ is generally [0.025, 0.035]. The speed threat coefficient curve is as follows: Figure 2 The present invention adopts γ = 0.03, V = 15Ma.

[0055] (3) Target distance

[0056] Target distance is an important indicator of target threat. The closer the target is, the more aggressive it is, the higher the risk factor is, and the corresponding threat level is greater. Assuming that the maximum detection distance of the radar is 1500km, the threat level of the target distance can be modeled as

[0057]

[0058] In the formula, represents the minimum threat level, and κ represents the threat level coefficient of the distance. Figure 3 As shown, through and κ can adjust the threat curve of distance. Usually set to a constant in the interval [0.1, 0.3]. κ is usually set to a constant in the interval [0.001, 0.003]. r represents the distance between the target and the radar. In the present invention, is 0.2, and κ is 0.002.

[0059] By judging the threat level of target height, target speed and target distance, we can get the comprehensive threat level of the target as:

[0060] W = x h W h +χ v W v +χ r W r (7)

[0061] In the formula, χ h , χ v and χ r They are the threat weights corresponding to target height, target speed and target distance respectively.

[0062] Normalizing the threat level of all targets, we can get the threat level value W corresponding to each target. i .

[0063] Step 3: Establish radar search model

[0064] 3.1 Optimization objective function

[0065] When a radar is searching for a target, it usually hopes to capture the target with the highest probability in the shortest time.

[0066] Assume that in a scenario where multiple targets are attacking from different directions, the number of airspaces that the radar needs to search is N. Assume that the search frame period of the i-th airspace is The time when the target appears in the airspace i follows a uniform distribution, so the average time when the target is found in the first search frame period is

[0067]

[0068]

[0069] P ki =p i ·(1-p i ) k (10)

[0070] In the formula, p i is the detection probability of the target within a single dwell time of the radar. According to the fluctuation characteristics of the hypersonic vehicle, it is modeled as a Swerling III type target, and the formula for its detection probability is:

[0071]

[0072] Where V T is the detection threshold, Γ I (·) is the incomplete gamma function, n p is the number of pulses accumulated in a single dwell time, and SNR is the signal-to-noise ratio.

[0073] It can be concluded that the average detection time of the target in airspace i is

[0074]

[0075] The cumulative detection probability of the radar to the target in the i-th airspace is

[0076]

[0077] Where n is the number of radar revisits.

[0078] Usually, we hope that the average discovery time of the target is as short as possible and the cumulative discovery probability is as large as possible.

[0079] Therefore, we design the optimization criterion as

[0080]

[0081] Where N is the total number of airspaces to be searched.

[0082] 3.2 Constraints

[0083] Due to the limitations of time and energy resources, the radar should also meet some constraints during the target search process, mainly including the following constraints:

[0084] (1) Detection probability constraints

[0085] During the radar search process, the target can only be captured if the cumulative detection probability of a single target is greater than 0.95.

[0086]

[0087] It can be seen that the cumulative detection probability is related to the single detection probability and the number of exposures. The greater the single detection probability and the more exposures, the greater the cumulative detection probability. The single detection probability is related to the signal-to-noise ratio. When the false alarm probability is constant, the relationship between the single detection probability and the signal-to-noise ratio is as follows: Figure 4 The signal-to-noise ratio is related to the number of pulse accumulation. The relationship between the signal-to-noise ratio and the number of pulse accumulation is shown in Figure 5 When the single detection probability is constant, the relationship between the cumulative detection probability and the number of re-illuminations is as follows: Figure 6 shown.

[0088] The number of simultaneous irradiations n should satisfy

[0089] 0 <n<T z / t i (16)

[0090] Among them, T z Represents the total time resources.

[0091] (2) Dwell time constraints

[0092] The radar range equation is:

[0093]

[0094] Among them, P t and G t are radar transmitter power and transmitting antenna power respectively, A r is the receiver cross-sectional area, σ is the target scattering cross-sectional area, L represents the total loss, k = 1.38 × 10 -23 J / K is the Boltzmann constant, T e =288K is the absolute temperature, B is the radar bandwidth.

[0095] The radar dwell time can be expressed as

[0096]

[0097] Here, τ represents the pulse width.

[0098] Substituting the above formula into formula (17) and rearranging it, we can get

[0099]

[0100] In the radar detection process, in order to avoid distance ambiguity, the pulse repetition period needs to meet

[0101]

[0102] Where C represents the speed of light, and its value is 3×10 8 m / s.

[0103] (3) Time resource constraints

[0104] The total time for the radar to perform the search mission is

[0105]

[0106] Among them, m i is the number of radar wave positions in the i-th airspace.

[0107] Phased array radar uses time division technology to perform search and tracking tasks simultaneously. Assuming that the proportion of search resources to total resources is λ, the total time for radar search needs to meet

[0108]

[0109] 3.3 Search Model

[0110] Taking into account the priority of the target and the constraints, we design the radar search parameter optimization formula as follows:

[0111]

[0112]

[0113] Step 4: Solve the search model based on the hybrid optimization algorithm of differential evolution and particle swarm optimization

[0114] It can be seen that the above optimization problem belongs to multi-objective and multi-constraint optimization. In order to improve the solution efficiency, we propose a hybrid optimization algorithm that combines differential evolution algorithm and particle swarm algorithm.

[0115] 4.1 Basic Particle Swarm Optimization

[0116] Particle Swarm Optimization (PSO) is a simplified model of swarm intelligence. It compares the optimization problem solving process to the foraging process of birds and the solution space to the flight space of birds. It searches for the optimal solution through the movement of particles in the solution space. PSO has simple parameters and is easy to implement. It has been widely used in function optimization, pattern classification, control engineering and other fields.

[0117] The PSO algorithm regards each particle as a potential optimal solution, determines the optimal position based on the fitness value of each particle, and updates the current movement speed and position of each particle based on its own historical optimal position and the historical optimal position of the group. At time t+1 (generation), its update formula is:

[0118]

[0119] x ij (t+1)=x ij (t)+v ij (t+1) (26)

[0120] In the formula, x ij represents the position of the particle, p ij represents the historical optimal position of the particle, p gi represents the historical optimal position of the group, v ijrepresents the speed of the particle, c1 and c2 are learning factors, r1 and r2 are random numbers distributed in the interval [0,1], i represents the i-th particle, j represents the j-th dimension, and w represents the inertia weight.

[0121] 4.2 Adaptive parameter design

[0122] The first term on the right side of formula (25) mainly represents the inertial motion of the particle itself, which is controlled by the inertia weight w. During the search process, w can be changed dynamically. In the early stage of the search, w takes a larger value, which is conducive to enhancing the particle's own inertia weight, thereby enhancing the global search capability; in the later stage of the search, w takes a smaller value, which is conducive to the rapid convergence of the algorithm and finding the local or even global optimal value. Here, a dynamic variable weight strategy is adopted, and w is set to

[0123]

[0124] In the formula, w max and w min Represent the maximum inertia weight and the minimum inertia weight, G max Indicates the maximum number of iterations.

[0125] The second and third items on the right side of formula (25) represent the self-cognition and social cognition of the particle, respectively. c1 is mainly used to control the influence of the particle's self-historical best position on the particle's movement. The larger the value of c1, the closer the particle tends to its self-historical best position. c2 is mainly used to control the influence of the particle group's historical best position on the particle's movement. The larger the value of c2, the closer the particle tends to its group's historical best position. In order to allow the particle to expand the search range as much as possible in the early stage of searching for the optimal solution and converge to the optimal position as quickly as possible in the later stage of searching, the learning factors c1 and c2 are designed as trigonometric functions of the number of iterations.

[0126]

[0127]

[0128] In the formula, c a 、c b 、c c and c d are all constants.

[0129] Figure 7 and Figure 8 The curves of inertia weight and learning factor changing with the number of iterations are shown. The three designed variables are substituted into formula (25) to replace the original update formula.

[0130] 4.3 Hybrid Optimization Algorithm

[0131] Since the particle swarm algorithm only considers the correlation between the optimal individual particle and the optimal population, it lacks population diversity and is prone to premature convergence and fall into local optimality. In order to overcome this defect, we combine the particle swarm algorithm with differential evolution and propose a hybrid optimization algorithm based on particle swarm algorithm and differential evolution.

[0132] Differential Evolution (DE) is a heuristic search algorithm that searches for the optimal solution through population mutation, crossover, and selection operations, and has strong global search capabilities. The mutation formula of differential evolution is as follows:

[0133]

[0134] Where r1, r2 and r3 are random numbers and are different from each other. F is the scaling factor, which is designed as a parameter for adaptive adjustment.

[0135] F=F0+2 λ (31)

[0136]

[0137] Where F0 is the mutation operator.

[0138] Differential evolution is used to perturb the evolution direction of the particle swarm algorithm. The formula is as follows:

[0139]

[0140] In the formula, when the random number in the interval [0,1] is less than or equal to the threshold S, the particle performs the mutation operation in differential evolution, followed by crossover and selection. When the random number in the interval [0,1] is greater than the threshold S, the particle performs the evolution method in the particle swarm algorithm according to formula (25). The threshold S is designed as an adaptive function

[0141]

[0142] Draw the curve of the threshold S changing with the evolutionary generation number as shown below Fig. 9 As shown in the figure, it can be seen that in the early stage of evolution, the value of S is relatively large, which allows particles to have a greater probability of performing differential evolution and increase population diversity. In the later stage of evolution, the value of S is relatively small, allowing particles to converge as quickly as possible and find the optimal value.

[0143] Simulation experiment

[0144] In order to reveal its internal mechanism and verify the effectiveness of the optimization algorithm proposed in this invention, numerical calculations are given. It is assumed that the radar parameter design is as shown in Table 1. The incoming targets are all hypersonic targets with a scattering cross-section of 0.01m 2 , the cumulative detection probability is 0.95 and the false alarm rate is 10-6 The following two methods are selected as comparison algorithms:

[0145] The first comparison algorithm is the particle swarm annealing algorithm, marked as Method 1.

[0146] The second comparison algorithm is the improved differential evolution algorithm, marked as Method 2.

[0147] Table 1 Radar parameters

[0148]

[0149] Assuming that there are three hypersonic aircraft coming from different directions, the radar determines the search range based on the warning information and estimates the distance, speed and altitude information of the hypersonic aircraft, as shown in Table 2. According to formulas (4) to (7), the priorities corresponding to the three hypersonic aircraft can be calculated to be 0.2670, 0.3147 and 0.4183 respectively. By using Method 1, Method 2 and the method of the present invention to allocate search resources to the three aircraft, the relationship between the total cumulative detection probability and the search task resource occupancy rate is obtained as follows: Fig.10 As shown in the figure, the relationship between the average discovery time and the search task resource occupancy rate is as follows: Fig.11 As shown. As the proportion of search task resource occupancy rate gradually increases, the total cumulative detection probability gradually increases, and the average discovery time gradually shortens. When the search task resource occupancy rate is greater than 0.7, the increase in the total cumulative detection probability gradually slows down, and the shortening of the average discovery time also gradually slows down. However, the optimization algorithm proposed in the present invention is always better than Method 1 and Method 2, especially when the search task resource occupancy rate is low, the advantage of the method proposed in the present invention is more obvious.

[0150] Table 2 Target status information and corresponding search parameters

[0151]

[0152] Fig.12 (1) Fig.12 (2) and Fig.12(3) The detection probabilities of the three targets under different search task resource occupancy rates for Method 1, Method 2 and the optimization method proposed in the present invention. It can be found that under the same search task resource occupancy rate, the interception probability of target three is greater than that of target two and target one. This is because the higher the priority of the target, the greater the corresponding weight, and the more search resources are obtained. The lower the search task resource occupancy rate, the more obvious this phenomenon is. It can also be found that when the search resources are seriously insufficient to cope with the search task of three targets, Method 1 and the algorithm proposed in the present invention both choose to abandon the search for target one according to the target priority in order to maximize the detection probability of target two and target three.

[0153] In order to analyze the performance of the algorithm of the present invention in the case of insufficient search resources in more detail, the time resource allocation corresponding to different algorithms is plotted when the search task resource occupancy rate is 0.1, as shown in Fig.13 As shown. It can be seen that the algorithm proposed in the present invention allocates 63% of time resources to target three, 30% of time resources to target two, and 7% of time resources to target one. Such an allocation method can give priority to the search of key targets.

[0154] The convergence of each algorithm is compared when the search task occupancy rate is 0.1, 0.5 and 1. Fig.14 As shown, it is found that when the search task occupancy rate is lower, the algorithm converges slower. At the same time, it can also be seen that the convergence speed of the algorithm proposed in the present invention is always better than the other two algorithms.

[0155] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A hypersonic aircraft search method based on guidance information, characterized in that: Here are the steps: S1. Determine the radar search airspace according to the guidance information; S2. Establish a hypersonic aircraft priority judgment model based on the target flight altitude, flight speed, and distance between the target and the radar, and determine the search order according to the priority level of the aircraft to be searched; S3. A radar search model was established with the maximum cumulative detection probability, the shortest average discovery time and the highest priority as the objective function; S4, using a hybrid optimization algorithm based on differential evolution and particle swarm optimization to solve the radar search model; Wherein, in step S3, the method for establishing the radar search model is: S3.1 Establishing the optimization objective function Define that in the scenario where multiple targets are attacking from different directions, the number of airspaces that the radar needs to search is N; assuming that the search frame period of the i-th airspace is t fi , the time when the target appears in the airspace i follows a uniform distribution, so the average time when the target is found in the first search frame period is t ki =k·t fi +t oi =(k+0.5)t fi (9) P ki =p i ·(1-p i ) k (10) In the formula, p i is the detection probability of the target within a single dwell time of the radar; according to the fluctuation characteristics of the hypersonic vehicle, it is modeled as a Swerling III target, and the formula for its detection probability is: Where V T is the detection threshold, Γ I (·) is the incomplete gamma function, n p is the number of pulses accumulated in a single dwell time, and SNR is the signal-to-noise ratio; It can be concluded that the average detection time of the target in airspace i is The cumulative detection probability of the radar to the target in the i-th airspace is Where n is the number of radar revisits; The optimization criterion is designed as: S3.2 Setting constraints (1) Setting detection probability constraints During the radar search process, the target can only be captured if the cumulative detection probability of a single target is greater than 0.

95. It can be seen that the cumulative detection probability is related to the single detection probability and the number of radar revisits. The greater the single detection probability and the more radar revisits, the greater the cumulative detection probability. The single detection probability is related to the signal-to-noise ratio. At the same time, the radar revisit times n should satisfy 0<n<T z / t i (16) Among them, T z Indicates total time resources; (2) Set the dwell time constraint The radar range equation is: Among them, P t and G t are radar transmitter power and transmitting antenna power respectively, A r is the receiver cross-sectional area, σ is the target scattering cross-sectional area, L represents the total loss, 1.38×10 -23 J / K is the Boltzmann constant, T e =288K is the absolute temperature, B is the radar bandwidth; The radar dwell time can be expressed as Where, τ represents the pulse width; Substituting the above formula into formula (17) and rearranging it, we can get In the radar detection process, in order to avoid distance ambiguity, the pulse repetition period needs to meet Where C represents the speed of light, and its value is 3×10 8 m / s. (3) Setting time resource constraints The total time for the radar to perform the search mission is Among them, m i is the number of radar wave positions in the i-th airspace; Assuming that the ratio of search resources to total resources is λ, the total time for radar to search needs to satisfy S3.3 Establishing the search model Taking into account the priority and constraints of the target, the radar search parameter optimization formula is designed as 2. A method for searching a hypersonic aircraft based on guidance information according to claim 1, characterized in that: In the step S1: the guidance information includes estimated target position information, speed information and error information; The position and speed of the hypersonic vehicle given by the guidance information are defined as The radar needs to convert the guidance information of the target into azimuth and elevation information. The error of the guided information is expressed as The target’s true azimuth and elevation information is: Then we can get The probability density of the target appearing in the radar search space Ω is defined as Then the probability of the target appearing in this airspace can be expressed as The range of the search airspace is determined according to the 3 times root mean square error 3δ principle; at the same time, according to the determined search airspace, the radar search parameters are reasonably set to ensure that the radar intercept probability of the target in the search airspace is higher than the search threshold value, that is, P d ≥P conf (3) Among them, P conf is the search threshold.

3. The method for searching for a hypersonic aircraft based on guidance information according to claim 1, characterized in that: In step S2, the priority determination method of the hypersonic aircraft is: Three indicators are defined to judge the priority of hypersonic vehicles, namely: target altitude, target speed, and target distance; Model the threat level of the target height as a function in the interval [0,1] Among them, H mid =(H max +H min ) / 2,H max Indicates the upper limit of the flight altitude, H min Indicates the lower bound of the flight altitude; The threat level of the target speed is modeled as Among them, V represents the upper limit of the flight speed, γ represents the threat coefficient of the speed, and the value range of γ is [0.025, 0.035]; The threat level of the target distance is modeled as In the formula, represents the minimum threat level, κ represents the threat level coefficient of the distance; and κ can adjust the threat curve of distance; is set to a constant in the interval [0.1, 0.3]; κ is set to a constant in the interval [0.001, 0.003]; r represents the distance between the target and the radar; By judging the threat level of target height, target speed and target distance, the comprehensive threat level of the target can be obtained as follows: W=x h W h +x v W v +x r W r (7) In the formula, χ h , χ v and χ r They are the threat weights corresponding to target height, target speed and target distance; By normalizing the threat levels of all targets, we can get the threat level value corresponding to each target.

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