A Genetic Algorithm-Based Method for Optimal Station Layout of Networked Radar

Abstract

This invention designs a method for optimizing networked radar station placement based on a genetic algorithm. Under distributed interference conditions, the optimal placement of networked radar stations is crucial. Existing networked radar station placement schemes are generated solely based on constraints and are suboptimal. This invention combines constraints with a genetic algorithm to optimize, calculate fitness expressions, and perform genetic operations to obtain an optimal station population. By using the genetic algorithm to rationally configure the positions of radars within the network, the radar network's detection area can be expanded while effectively monitoring key detection areas, thereby improving the anti-interference capability of the networked radar system.

Description

Technical Field

[0001] The present invention relates to the technical field of networked radar optimization station layout. Background Art

[0002] In modern warfare, the struggle between radar jamming and anti-jamming is becoming increasingly intense and complex, and jamming technology and its corresponding radar anti-jamming technology are constantly improving. Networked radars utilize various networking methods to form a comprehensive, three-dimensional, and multi-layered combat system on the battlefield. These radars feature full-band, multi-system, and multiple overlap coefficients, enhancing information interconnectivity and survivability. They can fully leverage the advantages of both system-based and group-based countermeasures, posing a significant challenge to traditional jamming methods.

[0003] Distributed jamming conditions can severely impact the detection capabilities of networked radars, rendering them unable to detect targets within the jamming sector. Distributed jamming is an electronic countermeasure developed in the form of drone-mounted and balloon-mounted jammers. It is typically used to shield targets in a specific area or create a false offensive posture within a region. Numerous, small, lightweight, and inexpensive jammers are dispersed across the airspace or terrain near the target, automatically or controlledly jamming selected radars and other electronic equipment. Because the jammers are distributed over a relatively wide spatial area, if they are evenly distributed, the power reaching the radar receiver is the combined power of multiple distributed jammers. The spatial distribution and interference characteristics of distributed jamming result in suppressive noise interference in the airspace, frequency domain, and time domain near the target radar. Due to its advantages in proximity and power, and its ability to spread across a large jamming sector, it can disrupt multiple radars within an airspace, posing a significant threat to networked radars. Summary of the Invention

[0004] The present invention proposes a networked radar optimization station layout method based on genetic algorithm, which optimizes the station selection of networked radar, increases the detection area of ​​the radar network, effectively monitors key detection areas, and improves the anti-interference capability of the networked radar system.

[0005] The technical solution to implement the present invention is to generate a networked station population that initially meets the constraints, iterate using a genetic algorithm to update it, determine whether the incremental objective function corresponding to the updated solution meets the acceptance criteria, and terminate the calculation when the new solution meets the convergence criteria. The technical solution includes:

[0006] Step 1: Set the deployment area and the number of deployed radar stations L, and randomly generate an initial station layout plan group S within the deployment area.

[0007] Step 2: Calculate the alert airspace of each radar in the network under no interference conditions and distributed interference conditions.

[0008] Step 3: Determine the initial networked radar station population based on the constraints.

[0009] Step 31: Calculate blind zone constraints and adjacent radar unit deployment spacing constraints;

[0010] Step 32: If the constraints in step 31 are met, the current population is used as the initial networked radar station population; if not, repeat steps 1, 2, and 3.

[0011] Step 4: Use the population in step 3 as the initial variable and calculate the fitness expression.

[0012] Step 41: Initialize the population. The stations that meet the constraints are used as the initial population. The number of population iterations is T.

[0013] Step 42: Calculate the fitness expression, use the objective function as the fitness function, and use t as the number of generations of the population;

[0014] Step 3: When the objective function value of the current population is greater than the objective function value of the previous generation population, the current population is taken as the latest population.

[0015] Step 5: Perform genetic operations on the networked radar station population.

[0016] Step 51: Determine whether the termination condition is met;

[0017] Step 52: Population selection, crossover and mutation calculation.

[0018] Step 6: When the termination conditions are met, output the optimized station layout results.

[0019] Compared with the existing technology, the present invention has the following significant advantages: using genetic algorithms to optimize the layout of networked radars and reasonably configuring the positions of each radar in the network can increase the detection area of ​​the radar network, while effectively monitoring key detection areas and improving the anti-interference ability of the networked radar system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart for implementing the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described below with reference to the accompanying drawings, but the protection scope of the present invention is not limited by the implementing regulations.

[0022] The present invention uses a station layout race that meets the constraint conditions as the initial input of the genetic algorithm to optimize the station layout of the networked radar, verify whether the new solution is acceptable, and output the optimal solution after the termination conditions are met. The preferred implementation process includes:

[0023] Step 1: Set the deployment area and the number of deployed radar stations L, and randomly generate an initial station layout plan group S within the deployment area.

[0024] Step 2: Calculate the alert airspace of each radar in the network under no interference conditions and distributed interference conditions.

[0025] Calculate the alert airspace S of each radar in the network under non-interference conditions i,h :

[0026]

[0027] in is the maximum effective range of the i-th radar at height h under interference-free conditions, h is the radar height, and θ is the angle.

[0028] Calculate the alert airspace S′ of each radar under distributed interference conditions i,h

[0029]

[0030] in, is the maximum effective range of the i-th radar at height h under distributed interference conditions.

[0031] Step 3: Determine the initial networked radar station population based on the constraints.

[0032] (1) Calculate blind zone constraints and adjacent radar unit deployment spacing constraints

[0033]

[0034]

[0035] stmin(R maxi,h )+min(R maxj,h )≥d

[0036] Where st represents the constraint and expression, ∩ represents the intersection, L0 represents the number of radars that can simultaneously detect the airspace, S core,h represents the core detection space, ζ represents the blind zone coefficient threshold, ∪ represents the union, S 0,h represents the alert airspace required by the h-th altitude layer networking system, δ represents the coverage factor required by the system under interference-free conditions, min represents the minimum value, and d represents the plane distance between two adjacent radar units;

[0037] (2) If the constraint conditions in (1) are met, the current population is used as the initial networked radar station population. If not, repeat steps 1, 2, and 3.

[0038] Step 4: Use the population in step 3 as the initial variable and calculate the fitness expression.

[0039] (1) Population initialization: the stations that meet the constraints are used as the initial population, and the number of population iterations is T;

[0040] (2) Calculate the fitness expression, take the objective function as the fitness function, use t as the number of generations of the population, use the formula to calculate the fitness F(h) of the t-th generation population, and when t = 1, it is the fitness of the initial population, and save the fitness;

[0041]

[0042] Where area(·) represents the area of ​​the region;

[0043] (3) When the objective function value of the current population is greater than the objective function value of the previous generation population, the current population is regarded as the latest population.

[0044] Step 5: Perform genetic operations on the networked radar station population.

[0045] (1) The population generation t increases by 1. If the termination condition is not met, enter (2). The termination condition is: the generation t reaches the iteration number T or FF′<ε, F is the objective function value of the current population, F′ is the objective function value of the previous generation population, and ε is the set iteration difference value;

[0046] (2) Population selection, crossover and mutation calculation: Use the roulette wheel method to select the t-generation population to obtain a population of size S; randomly divide the population into two subgroups A and B, pair each individual in A with each individual in B in the order of storage, and perform crossover operation on the pairs of individuals to obtain two new individuals. Otherwise, save the two individuals and create a new population. Perform the above operations on all pairs of individuals to obtain a new population; for each individual in the new population, re-judge the constraints of the new population and enter step 3.

[0047] Step 6: When the termination conditions are met, output the optimized station layout results.

[0048] When the number of iterations t reaches T or FF′<ε, the station layout optimization result is output.

[0049] The genetic algorithm-based networked radar optimization layout method proposed in this invention applies the genetic algorithm to the field of networked radar deployment, optimizing radar deployment under distributed interference. A modified population is obtained under existing basic constraints, fitness calculations and genetic operations are performed on it, and the resulting new deployment plan population is then subjected to constraint determination and correction. This method allows networked radars to rationally configure the positions of each radar within the network under distributed interference conditions, thereby expanding the radar network's detection area while effectively monitoring key detection areas and improving the networked radar system's anti-interference capabilities.

Claims

1. A method for optimizing networked radar station layout based on a genetic algorithm, characterized by: Step 1: Set the deployment area and the number of deployed radar stations L, and randomly generate an initial station layout plan group S within the deployment area; Step 2: Calculate the alert airspace of each radar in the network under no interference conditions and distributed interference conditions; Step 3: Determine the initial networked radar station population based on the constraints; Step 31: Calculate the blind zone constraints and the deployment spacing constraints of adjacent radar units, including: s.t.min(R maxi,h )+min(R maxj,h )≥d; Where st represents the constraint and expression, ∩ represents the intersection, L0 represents the number of radars that can simultaneously detect the airspace, S core,h represents the core detection space, ζ represents the blind zone coefficient threshold, λ represents the union, S 0,h represents the alert airspace required by the h-th altitude layer networking system, δ represents the coverage factor required by the system under interference-free conditions, min represents the minimum value, and d represents the plane distance between two adjacent radar units; Step 32: If the constraints in step 31 are met, the current population is used as the initial networked radar station population; if not, repeat steps 1, 2, and 3. Step 4: Use the population in step 3 as the initial variable and calculate the fitness expression; Step 41: Initialize the population. The stations that meet the constraints are used as the initial population. The number of population iterations is T. Step 42: Calculate the fitness expression, using the objective function as the fitness function and t as the number of generations of the population; wherein the fitness function calculation method includes: Where aera(·) represents the area of ​​the region; Step 43: When the objective function value of the current population is greater than the objective function value of the previous generation population, the current population is taken as the latest population; Step 5: Perform genetic operations on the networked radar station population, including: Step 51: The population generation number t is increased by 1. If the termination condition is not met, go to (2); the termination condition is: the generation number t reaches the iteration number T or FF′<ε, F is the objective function value of the current population, F′ is the objective function value of the previous generation population, and ε is the set iteration difference value; Step 52: Population selection, crossover, and mutation calculation: Use the roulette wheel method to perform selection operations on the t-th generation population to obtain a population of size S; randomly divide the population into two subpopulations, A and B, and pair each individual in A with each individual in B in the order they are stored. Perform crossover operations on these pairs of individuals to obtain two new individuals. Otherwise, save the two individuals and create a new population. Perform the above operations on all pairs of individuals to obtain a new population. For each individual in the new population, re-judge the constraints of the new population and proceed to step 3. Step 6: When the termination conditions are met, output the optimized station layout results.

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