Underground space vehicle-mounted through-wall radar path optimization method based on improved ant colony algorithm

Abstract

The application discloses an underground space vehicle-mounted through-wall radar path optimization method based on an improved ant colony algorithm. First, the particularity of underground space is considered, and adaptive rules of a heuristic function are designed according to the distance of the unmanned vehicle motion path and the imaging quality of the three-dimensional through-wall radar, so as to balance the global search performance and the convergence speed of the algorithm. Secondly, by solving a non-convex optimization problem containing multiple constraint conditions such as path length, imaging quality and Gini impurity, efficient and accurate environment perception, accurate imaging and path planning in a complex underground environment are ensured. The method can be applied to underground space unmanned detection and target search and rescue, and has strong adaptability and high efficiency.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of underground space unmanned system detection, and particularly relates to a wall-penetrating radar path optimization method for underground space vehicle based on an improved ant colony algorithm. BACKGROUND

[0002] The wall-penetrating radar is a device for detecting targets behind walls or other shelters by using low-frequency electromagnetic wave signals, and can penetrate the information of the sheltered space behind the wall without being affected by the obstruction. The wall-penetrating radar uses the strong penetration of the ultra-wideband radar signal to the non-metallic medium wall and other obstacles, and after penetrating the non-metallic obstacles such as the wall, a radar echo is generated after encountering a personnel target. The weak movements such as human body movement, breathing, heartbeat and limb swing will form a Doppler effect on the echo, and based on this, the received echo is analyzed and processed to obtain the position, quantity and other information of the target.

[0003] With the acceleration of urbanization and the development and utilization of underground space, the demand for underground detection is increasing. At present, the classic path planning algorithms such as A* algorithm, Dijkstra algorithm and Rapidly-exploring Random Tree (RRT) path search algorithm can have basic path planning ability in relatively simple environment, but in the underground space with high uncertainty and complexity, they will face problems such as high computational complexity, easy to fall into local optimum and low imaging quality. The traditional ant colony algorithm has strong global search ability and adaptability, but still has problems such as slow convergence speed and insufficient path diversity in practical application. Therefore, in the complex and dynamically changing underground space, how to efficiently plan the motion path of the vehicle-mounted radar to improve the detection efficiency and imaging quality has become a key problem to be solved. SUMMARY

[0004] To solve the above problems, the application provides a wall-penetrating radar path optimization method for underground space vehicle based on an improved ant colony algorithm. The application generalizes the principle of array optimization to suppress grating lobes to the unmanned vehicle-mounted wall-penetrating radar in view of the problems such as the existence of grating lobe effect in the three-dimensional tomography wall-penetrating radar imaging of the traditional unmanned vehicle and the inability of the traditional algorithm to adapt to the complex environment of the underground space, and realizes the joint optimization of the path length, non-uniform sampling and three-dimensional wall-penetrating radar imaging quality of the unmanned vehicle based on the improved ant colony algorithm, so as to realize high-quality imaging of the target behind the wall.

[0005] The specific technical scheme of the application is as follows: a wall-penetrating radar path optimization method for underground space vehicle based on an improved ant colony algorithm, and the steps are as follows:

[0006] Step 1, linear interpolation processing is performed on the known non-collision initial path to obtain a non-collision initial interpolation path, and then path nodes with fixed intervals are obtained.

[0007] Step 2, based on the demand of underground space vehicle-mounted through-wall radar detection, the original cost function of ant colony algorithm is improved, the improved cost function is established by combining path length, imaging quality and Gini impurity, and then the improved ant colony algorithm is obtained for evaluating the advantages and disadvantages of the path to obtain the optimized path.

[0008] Step 3, the path node with fixed interval is taken as the input of the improved ant colony algorithm, the pheromone matrix is initialized, the pheromone value between each fixed interval path node is equal, and the ant colony is initialized, each ant represents a path of unmanned vehicle movement.

[0009] Step 4, the improved ant colony algorithm is used to optimize the collision-free initial interpolation path, the cost of each unmanned vehicle movement path is calculated, the advantages and disadvantages of the path are judged to update the pheromone, and the selection probability of the optimal path is enhanced, the selection scheme of the path node is gradually optimized through multiple iterations until the optimal solution is converged or the preset iteration number is reached, and the optimal path scheme is output.

[0010] Step 5, based on the optimal path scheme, the vehicle-mounted through-wall radar obtains the echo signal, and through pulse compression, distance interpolation, back projection and coherent accumulation processing, the three-dimensional back projection imaging result is obtained.

[0011] Step 6, according to the three-dimensional back projection imaging result, the wall position, thickness and dielectric constant are determined, and the wall behind point target position is obtained.

[0012] Compared with the prior art, the present application has the following advantages:

[0013] (1) The present application can realize unmanned detection and target search and rescue in underground space.

[0014] (2) The present application proposes an improved ant colony algorithm, which takes into account the path length of unmanned vehicle movement, non-uniform sampling scheme and three-dimensional through-wall radar imaging quality, solves the joint optimization under multiple constraint conditions, and realizes high-quality imaging of the target behind the wall.

[0015] (3) The present application proposes an unmanned vehicle-mounted through-wall detection method based on the physical properties of electromagnetic waves, which is not affected by narrow and dim environments, has strong adaptability, and belongs to an efficient through-wall detection method.

[0016] (4) The present application improves the path heuristic function, introduces an adaptive adjustment factor ξ, and adaptively adjusts the weight of heuristic information to balance the global search ability and convergence speed of the algorithm, and can obtain the optimal path of the unmanned vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 It is a whole flowchart of the underground space vehicle-mounted through-wall radar path optimization method based on the improved ant colony algorithm.

[0018] Figure 2 This is a flowchart of the improved ant colony algorithm of the present invention. Detailed Implementation

[0019] The present invention will now be described in further detail with reference to the accompanying drawings.

[0020] Combination Figure 1 The present invention discloses a path optimization method for vehicle-mounted through-wall radar in underground space based on an improved ant colony algorithm, comprising the following steps:

[0021] Step 1: Perform linear interpolation on the known collision-free initial path to obtain the collision-free initial interpolation path, and then obtain the path nodes with fixed intervals.

[0022] Considering the position of the autonomous vehicle in three-dimensional space, the known collision-free initial path is represented as P = {p1, p2, ..., p...} i ,...,p n}, where pi=(xi,yi,zi) represents the three-dimensional spatial coordinates of the i-th collision-free initial path node, and n represents the total number of nodes.

[0023] Perform linear interpolation on the known collision-free initial path, determine the distance interval between interpolated path nodes as Δd, and calculate the number of interpolated points k inserted between each pair of collision-free initial adjacent path points:

[0024]

[0025] Where, p i+1 -p i p represents the initial adjacent path point without collision. i and p i+1 The Euclidean distance between them, floor() represents the floor function.

[0026] The total number of interpolation path nodes is m = n + k·(n-1), and the number of nodes on the optimized path of the autonomous vehicle is set to M, M≤m.

[0027] For each pair of collision-free initial adjacent nodes p i and p i+1 The interpolation path node p obtained after inserting a nodes between them ia The calculation formula is as follows:

[0028]

[0029] Where, p i+1 Indicates following closely behind p i The next node, p i+1 =(x i+1 ,yi+1 ,z i+1 ),p ia represents an interpolation path node, p ia =(x ia ,y ia ,z ia ), a = 1, 2, …, k.

[0030] p ia =(x ia ,y ia ,z ia ) The calculation formula is:

[0031]

[0032]

[0033] Wherein, x ia represents the x-axis coordinate of the a-th interpolation node in the interpolation path node p ia , y ia represents the y-axis coordinate of the a-th interpolation node in the interpolation path node p ia , z ia represents the z-axis coordinate of the a-th interpolation node in the interpolation path node p ia .

[0034] Repeat the above interpolation calculation, and finally obtain the total path with fixed interval after linear interpolation processing, that is, the initial collision-free interpolation path P':

[0035] P' = {p1, p 11 ,p 12 ,...,p2, p 21 ,...,p nk ,p n}

[0036] Wherein, P' contains collision-free initial path nodes p1, p2, …, p n and interpolation path nodes p 11 ,p 12 ,...p 1k ,p 21 ,...p 2k ,...p nk .

[0037] Step 2, based on the detection requirements of the wall-penetrating radar carried by the underground space vehicle, the original cost function of the ant colony algorithm is improved, the improved cost function is established by combining the path length, imaging quality and Gini impurity, and then the improved ant colony algorithm is obtained. The improved ant colony algorithm is used to evaluate the pros and cons of the path to obtain the optimized path.

[0038] Based on the demand of wall-penetrating radar detection of underground space vehicle, the cost function J is improved total is:

[0039] J total = w1·D + w2·R psl + w3·Gini

[0040] wherein D represents the length of the optimized path, R psl represents the peak side lobe ratio, Gini represents the Gini impurity, w1 is the hyperparameter weight for evaluating D, w2 is the hyperparameter weight for evaluating R psl , and w3 is the hyperparameter weight for evaluating Gini.

[0041]

[0042] D is represented by the sum of the Euclidean distances between each node of the optimized path, and M is the number of nodes on the optimized path of the unmanned vehicle; (x h , y h , z h ) represents the three-dimensional space coordinates of the hth node of the optimized path, and (x h+1 , y h+1 , z h+1 ) represents the three-dimensional space coordinates of the (h+1)th node of the optimized path.

[0043]

[0044] P sl represents the point set of the grid lobe, P ml represents the point set of the main lobe, and R psl is smaller, the higher the imaging quality is;

[0045] Gini impurity

[0046] Gini is the randomness of unequal interval sampling.

[0047] wherein F represents the sum of the number of different motion path schemes of the unmanned vehicle, p f represents the proportion of samples belonging to scheme f, i.e. wherein u f is the number of samples belonging to scheme f, i.e. the number of sampling points in a certain interval; and u is the total number of samples, i.e. the number of all sampling points.

[0048] If the intervals are evenly distributed, i.e. most of the sampling points fall in the same or adjacent intervals, the Gini impurity value will be low; if the sampling point intervals are distributed relatively dispersedly, i.e. distributed in multiple different intervals, the Gini impurity value will be high.

[0049] High Gini impurity: indicates that the distribution of sampling point intervals is relatively dispersed, reflecting that the randomness of the sampling points is relatively strong and the interval distribution is not concentrated; this situation reflects that the sampling points are distributed in a non-uniform and relatively random manner in space, which is usually beneficial to suppressing the grating effect and improving the resolution of radar imaging.

[0050] Step 3, taking the path nodes with fixed intervals as the input of the improved ant colony algorithm, initializing the pheromone matrix, the pheromone value between each fixed interval path node being equal, initializing the ant colony, and each ant representing a path of the unmanned vehicle movement, as follows:

[0051] Each unmanned vehicle optimized path contains M path nodes, and M≤m; each ant selects a path according to a probability, and the probability is determined by the pheromone concentration and the distance between the path nodes; the selection probability is calculated according to the following formula:

[0052]

[0053] wherein, is the selection probability of the ant from node b to node c at time t, τ bc (t) is the pheromone concentration of the ant from node b to node c at time t, η bc (t) is the path heuristic information of the ant at time t, and α and β are adjustment parameters; s represents a node that has not been reached, and s∈allowed m ; τ bs (t) is the pheromone concentration of the ant from node b to the node s that has not been reached at time t, η bs (t) is the inverse distance between the ant from node b to the node s that has not been reached at time t; allowed m represents the set of unmanned vehicle path nodes.

[0054] η bc (t) is expressed as follows:

[0055]

[0056] wherein, ξ is an adaptive adjustment factor, σ1 is the weight coefficient of R psl , σ2 is the weight coefficient of d bc ; λ is a decay coefficient, d bc is the Euclidean distance between node b and node c, iter is the iteration number at time t, iter max is the maximum iteration number, is a constant value.

[0057] Step 4, combining Figure 2The improved ant colony algorithm is used to optimize the initial collision-free interpolation path, the cost of each unmanned vehicle motion path is calculated, the path is judged to update the pheromone, and the selection probability of the optimal path is enhanced, the selection scheme of the path node is gradually optimized through multiple iterations until the optimal solution is converged or the preset iteration number is reached, and the optimal path scheme is output, and the specific process is as follows:

[0058] In the traditional ant colony algorithm, the pheromone is often updated according to the path length, and in the present application, the pheromone of the ant path is updated according to the weighted sum of the path length of the unmanned vehicle motion, the Gini impurity and the wall penetrating radar imaging quality. When the cost function of the path of an ant is smaller, the pheromone released by the ant on the path is more, and the probability of the next generation of ants walking on the path is greater, so that the feasible solution generated in the iteration process gradually converges to the direction of decreasing the cost function. The calculation formula of the pheromone update is as follows:

[0059]

[0060] Wherein, τ bc (t+1) represents the pheromone concentration between nodes at time t+1, τ bc (t) represents the pheromone concentration between nodes at time t, ρ is the pheromone evaporation factor, and ρ is in (0, 1), the size of ρ directly affects the global search ability and convergence speed of the ant colony; Δτ bc (t) is the new pheromone amount at t, which is usually related to the path quality, N c is the total number of ants; is the pheromone concentration between nodes of the ant g at time t, the pheromone increment Q0 is a constant, and the cost function of the ant g is the weighted sum of D, R psl and Gini, p b is the three-dimensional space coordinates of the bth collision-free initial path node, p c is the three-dimensional space coordinates of the cth collision-free initial path node.

[0061] Step 5, the wall penetrating radar on the vehicle obtains the echo signal based on the optimal path scheme, and through pulse compression, distance interpolation, back projection and coherent accumulation processing, the three-dimensional back projection imaging result is obtained.

[0062] The processing process of the radar echo signal is as follows: pulse compression is performed to improve the range resolution; the distance error between sampling points is corrected through the distance interpolation algorithm; the spatial position of the target object is reconstructed by using the back projection algorithm for three-dimensional imaging; the target signal is enhanced and the noise is suppressed by using the coherent accumulation technology to superimpose multiple echo signals.

[0063] The back-projection algorithm is as follows: assuming that the airborne radar transmits a linear frequency modulation continuous wave, then the single slow-time data imaging result will obtain a point spread function, as shown in the following formula:

[0064]

[0065] where Band is the radar bandwidth, C is the speed of light, mu is the radar wavelength, Delta R represents the difference between the "radar-target" distance and the "radar-sidelobe" distance, Delta R = P t -P a -(P s -P a ), P t is the target position, P a is the slow-time airborne radar position, P s is the position where the sidelobe is located, and j represents the imaginary part.

[0066] Step 6: According to the three-dimensional back-projection imaging result, the wall position, thickness, and dielectric constant are determined, and the point target position behind the wall is obtained.

[0067] By measuring the time from the transmission of the through-wall radar to the reception of the echo signal, combined with the known signal propagation speed, the wall position is calculated, and the specific method is as follows:

[0068]

[0069] where W d is the wall distance, C v is the propagation speed of electromagnetic waves in air, and Delta t is the round-trip time of the echo signal.

[0070] When the signal encounters the wall, part of the signal will be reflected, and part of the signal will penetrate the wall and be reflected again. By analyzing the time delay of the echo signal, the thickness of the wall can be determined. The first reflection peak of the signal represents the front of the wall, and the second peak represents the back of the wall. The wall thickness can be calculated using the following formula:

[0071]

[0072] where W h is the wall thickness, t1 and t2 are the arrival times of the front and back reflection peaks, respectively.

[0073] The measurement of the dielectric constant can be realized by analyzing the attenuation and phase change of the echo signal. According to the relationship between the dielectric constant and the electromagnetic wave speed, the known signal propagation speed and the phase delay of the reflected signal can be used to calculate using the following formula:

[0074]

[0075] where epsilon rW is the dielectric constant of the wall v W is the speed of electromagnetic wave propagation in the wall.

[0076] By analyzing the wall position, thickness and dielectric constant, the target position behind the wall can be corrected according to these physical parameters.

[0077] The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application. Any modification made according to the technical idea of the present application in the technical scheme falls within the protection scope of the present application.

Claims

1. A method for path optimization of a wall-penetrating radar mounted on a vehicle in underground space based on an improved ant colony algorithm, characterized in that, The method comprises the following steps: Step 1, linear interpolation processing is performed on the known initial collision-free path to obtain an initial interpolation path without collision, and then a path node with a fixed interval is obtained; Step 2, based on the detection requirements of the wall-penetrating radar carried by the underground space vehicle, the original cost function of the ant colony algorithm is improved, the improved cost function is established by combining the path length, the imaging quality and the Gini impurity, and then the improved ant colony algorithm is obtained, which is used to evaluate the advantages and disadvantages of the path to obtain an optimized path; Step 3, the path node with a fixed interval is taken as the input of the improved ant colony algorithm, the pheromone matrix is initialized, the pheromone values between each path node with a fixed interval are equal, the ant colony is initialized, and each ant represents a path of the unmanned vehicle movement; Step 4, the improved ant colony algorithm is used to optimize the initial interpolation path without collision, the cost of each path of the unmanned vehicle movement is calculated, the advantages and disadvantages of the path are judged to update the pheromone, and the selection probability of the optimal path is enhanced, the selection scheme of the path node is gradually optimized through multiple iterations until the optimal solution is converged or the preset iteration number is reached, and the optimal path scheme is output; Step 5, based on the optimal path scheme, the wall-penetrating radar carried by the vehicle obtains the echo signal, and through pulse compression, distance interpolation, back projection and coherent accumulation processing, a three-dimensional back projection imaging result is obtained; Step 6, according to the three-dimensional back projection imaging result, the wall position, thickness and dielectric constant are determined, and the position of the point target behind the wall is obtained.

2. The underground space vehicle-mounted through-wall radar path optimization method based on an improved ant colony algorithm according to claim 1, characterized in that: In step 1, the known initial collision-free path is subjected to linear interpolation processing to obtain an initial interpolation path without collision, and then a path node with a fixed interval is obtained, and the specific process is as follows: Considering the position of the autonomous vehicle in three-dimensional space, the known collision-free initial path is represented as P = {p1, p2, ..., p...} i ,...,p n }, where p i =(x i ,y i ,z i () represents the three-dimensional spatial coordinates of the i-th collision-free initial path node, and n represents the total number of nodes; The known initial collision-free path is subjected to linear interpolation processing, the distance interval between the interpolation path nodes is determined as Δd, the number of interpolation points inserted between each pair of adjacent initial collision-free path points is calculated as k: where ||p i+1 -p i || represents the Euclidean distance between the initial adjacent path points p i and p i+1 without collision, and floor() represents the rounding function. The total number of interpolation path nodes m = n + k·(n-1), the number of nodes on the optimized path of the unmanned vehicle is set as M, and M≤m; For each pair of initial adjacent nodes p i and p i+1 , the interpolation path node p ia obtained after inserting a nodes between p i and p i+1 is calculated as follows: where p i+1 represents the node immediately following p i , p i+1 = (x i+1 , y i+1 , z i+1 ), p ia represents an interpolated path node, p ia = (x ia , y ia , z ia ), a = 1, 2,..., k; p ia = (x ia ,y ia ,z ia ) formula: wherein x ia represents the x-axis coordinate of the a-th interpolation node in the interpolation path node p ia , y ia represents the y-axis coordinate of the a-th interpolation node in the interpolation path node p ia , and z ia represents the z-axis coordinate of the a-th interpolation node in the interpolation path node p ia . The above interpolation calculation is repeated, and finally the total path with a fixed interval after linear interpolation processing, i.e., the initial interpolation path without collision P' is obtained: P' = {p1, p 11 , p 12 , ..., p2, p 21 , ..., p nk , p n} where P' comprises collision-free initial path nodes p1, p2,..., p n and interpolation path nodes p 11 , p 12 , ...p 1k , p 21 , ...p 2k , ...p nk .

3. The underground space vehicle-mounted through-wall radar path optimization method based on the improved ant colony algorithm according to claim 2, characterized in that, In step 2, based on the detection requirements of the wall-penetrating radar carried by the underground space vehicle, the original cost function of the ant colony algorithm is improved, the improved cost function is established by combining the path length, the imaging quality and the Gini impurity, and then the improved ant colony algorithm is obtained, which is used to evaluate the advantages and disadvantages of the path to obtain an optimized path, and the specific process is as follows: Based on the demand of vehicle-mounted through-wall radar detection in underground space, the cost function J is improved total is: J total = w1 · D + w2 · R psl + w3 · Gini wherein D represents the optimized path length, R psl represents the peak side lobe ratio, Gini represents the Gini impurity, w1 is a hyperparameter weight for evaluating D, w2 is a hyperparameter weight for evaluating R psl , and w3 is a hyperparameter weight for evaluating Gini; D is represented by the sum of the Euclidean distances between each node of the optimized path, M is the number of nodes on the optimized path of the unmanned vehicle; (x h ,y h ,z h ) represents the three-dimensional space coordinates of the hth optimized path node, (x h+1 ,y h+1 ,z h+1 ) represents the three-dimensional space coordinates of the h+1th optimized path node; P sl a set of points representing the grid lobe, P ml a set of points representing the main lobe, R psl the smaller the better the imaging quality; gini impurity Gini is the randomness of unequal interval sampling; where F represents the sum of the number of different motion path schemes of the unmanned vehicle, p f represents the proportion of samples belonging to scheme f, i.e. where u f is the number of samples belonging to scheme f, i.e. the number of sampling points in the particular interval; u is the total number of samples, i.e. the number of all sampling points.

4. The underground space vehicle-mounted through-wall radar path optimization method based on the improved ant colony algorithm according to claim 3, characterized in that: In step 3, the path node with a fixed interval is taken as the input of the improved ant colony algorithm, the pheromone matrix is initialized, the pheromone values between each path node with a fixed interval are equal, the ant colony is initialized, and each ant represents a path of the unmanned vehicle movement, and the specific process is as follows: Each unmanned vehicle optimizes a path with M path nodes, M≤m; each ant selects a path according to a probability, which is determined by the pheromone concentration and the distance between path nodes; the selection probability The calculation formula is: wherein, is the selection probability of the ant from node b to node c at time t, τ bc (t) is the pheromone concentration of the ant from node b to node c at time t, η bc (t) is the path heuristic information of the ant at time t, α and β are adjustment parameters, s represents a node that has not been reached, s ∈ allowed m ; τ bs (t) is the pheromone concentration of the ant from node b to a node s that has not been reached at time t, η bs (t) is the inverse of the distance between the ant from node b to a node s that has not been reached at time t; allowed m allowed represents the set of nodes of each path of the unmanned vehicle; η bc (t) is expressed as follows: wherein ξ is an adaptive adjustment factor, σ1 is a weight coefficient of R psl , σ2 is a weight coefficient of d bc ; λ is an attenuation coefficient, d bc is the Euclidean distance from node b to node c, iter is the iteration number at time t, iter max is the maximum iteration number, and l is a constant value.

5. The underground space vehicle-mounted through-wall radar path optimization method based on the improved ant colony algorithm according to claim 4, characterized in that: In step 4, the improved ant colony algorithm is used to optimize the initial interpolation path without collision, and the cost of each path of the unmanned vehicle movement is calculated, and the specific process is as follows: The calculation of the cost of each path of the unmanned vehicle movement can be described as a multi-constraint optimization problem: min J total s.t.R ps1 = B(D, Gini) Wherein B represents an imaging operator based on the current unmanned vehicle wall-penetrating radar motion path length D and Gini, and the peak side lobe ratio R of the image is calculated from the unmanned vehicle motion path length D and Gini ps1 And the optimal driving path under the current evaluation criterion is obtained by minimizing the joint objective function.

6. The underground space vehicle-mounted through-wall radar path optimization method based on the improved ant colony algorithm according to claim 5, characterized in that: In step 4, the specific process of pheromone calculation is as follows: The formula of the pheromone concentration is: τ bc (t+1) = (1 - p) - τ bc (t) + Δτ bc (t) where τ bc (t + 1) represents the pheromone concentration between nodes at time t + 1; p is the pheromone evaporation factor, and p e (0, 1), the size of p directly affects the global search ability and convergence speed of the ant colony; Δτ bc (t) is the amount of new pheromone at time t, which is usually related to the path quality.

7. The underground space vehicle-mounted through-wall radar path optimization method based on the improved ant colony algorithm according to claim 6, characterized in that, The processing procedure of the radar echo signal in step 5 is as follows: pulse compression, distance interpolation, back-projection, and coherent accumulation. Based on the optimal path scheme, the vehicle-mounted through-wall radar obtains echo signals, performs pulse compression to improve the range resolution, corrects the distance error between sampling points by using the distance interpolation algorithm, uses the back-projection algorithm for three-dimensional imaging to reconstruct the spatial position of the target object, and performs coherent accumulation on multiple echo signals to enhance the target signal and suppress noise.

8. The underground space vehicle-mounted through-wall radar path optimization method based on the improved ant colony algorithm according to claim 7, characterized in that, The back-projection algorithm is used for three-dimensional imaging to reconstruct the spatial position of the target object, and the specific process is as follows: Assuming that the airborne radar transmits a linear frequency modulation continuous wave, the point spread function (PSF) of a single slow-time data imaging result is obtained, as shown in the following formula: where Band is the radar bandwidth, C is the speed of light, μ is the radar wavelength, j represents the imaginary part ΔR represents the difference between the "radar-target" distance and the "radar-sidelobe" distance; AR = P t - P a - (P s - P a ) where P t is the target position, P a is the slow-time airborne radar position, P s is the position where the grating lobes are located.

9. The underground space vehicle-mounted through-wall radar path optimization method based on the improved ant colony algorithm according to claim 8, characterized in that, In step 6, according to the three-dimensional back-projection imaging result, the wall position, thickness, and dielectric constant are determined, and the point target position behind the wall is obtained, and the specific process is as follows: The dielectric constant is determined by the attenuation and phase delay of the radar echo signal, the position and thickness of the wall are calculated by the reflection characteristics of the radar echo signal, and then the corrected point target position behind the wall is obtained.

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