A configuration optimization method for air-based radar network with separate transmission and reception for cooperative detection formation

Abstract

The present invention provides a method for optimizing the configuration of a collaborative detection formation with a networked airborne radar and a split transmitter-receiver configuration, relating to the technical field of multi-aircraft collaborative detection. The method first initializes the combat environment between the enemy and friendly forces and sets evaluation indicators for friendly forces' detection effectiveness. A radar equation for a system with split transmitters and receivers is then established to calculate the detection power of friendly forces' radars against enemy aircraft. A mathematical model is then established to evaluate the optimization effect of the split transmitter-receiver collaborative detection formation configuration. Finally, an improved artificial fish school algorithm is proposed to search for the optimal configuration of the split transmitter-receiver collaborative detection formation. The proposed method for optimizing the configuration of a networked airborne radar and a split transmitter-receiver collaborative detection formation not only improves the survivability of the transmitter but also satisfies the receiver's need for covert target detection.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-aircraft cooperative detection, and in particular to a configuration optimization method for an air-based radar network with separate transmission and reception for cooperative detection formation. Background Art

[0002] In modern warfare, gaining air superiority is crucial to victory. With the rapid development of stealth fighter technology, effectively detecting enemy aircraft at greater distances and over a wider range in a covert manner poses a significant challenge to air-based detection technology.

[0003] Coordinated detection using networked airborne radars is considered a key operational form in future air combat. In terms of safety, radar-equipped airborne platforms form a swarm formation, which can be viewed as a giant virtual detection platform, spatially separated but logically integrated. This detection model allows for complementary strengths between individual members of the formation. The destruction of any one node will not impact the normal operation of others, thereby enhancing the formation's survivability in complex battlefield environments. In terms of detection performance, any member of the swarm can act as a transmitter, while the others act as receivers. Leveraging the spatial diversity provided by multi-station airborne radars, the optimized spatial distribution of transmitters and receivers allows for more effective detection of enemy targets. Networking not only improves the survivability of transmitters but also meets the need for receivers to detect targets in a covert manner. This overcomes the limitations of current airborne radars in responding to battlefield changes, maximizing the combat effectiveness of the formation.

[0004] Based on research into the behavioral characteristics and internal mechanisms of fish schools in nature, this paper proposes and implements a method for optimizing the configuration of airborne radar networked, transceiver-distributed collaborative detection formations based on an improved artificial fish-schooling algorithm. This method reduces the probability of detection of key targets while also enhancing overall combat capability. This method meets the requirements for both security and optimization of collaborative detection. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the shortcomings of the existing technology in detection efficiency and detection effectiveness, and to provide a method for optimizing the configuration of an airborne radar network with separate transmission and reception for collaborative detection, so as to improve the formation configuration search efficiency and optimize the collaborative detection effect of multiple aircraft while protecting the safety of our aircraft as much as possible.

[0006] To solve the above technical problems, the technical solution adopted by the present invention is: a method for optimizing the configuration of an air-based radar network with separate transmission and reception for collaborative detection formation, comprising the following steps:

[0007] Step 1: Initialize battlefield information;

[0008] Initialize the receiver to a configuration proximity distance R relative to the transmitter. near , the far distance is R far ; The area of ​​the alert airspace is Ω S , and divide the warning airspace into multiple grids, with the horizontal step length of the grid unit being △x and the vertical step length being △y;

[0009] Step 2: Establish a bistatic radar equation for a bistatic system with separate receivers and transmitters, and calculate the detection power of the bistatic radar against enemy aircraft.

[0010] Considering the propagation factor of the bistatic radar, the bistatic radar equation is as follows:

[0011]

[0012] Among them, R T is the distance between the transmitter and the enemy aircraft, R R is the distance between the receiver and the enemy aircraft; P T is the transmitter power, P R is the detection power of the dual-station radar to the enemy aircraft; G T , G R are the gains of the transmitting antenna and the receiving antenna respectively; λ is the radar operating wavelength; F T 、F R are the transmission and reception pattern propagation factors respectively; σ B is the enemy aircraft RCS value, that is, the dual-station radar cross-section, which is expressed as σ B =σ(α T ,β T ; α R ,β R ), α T , β T and α R , β R are the azimuth and elevation angles of the enemy aircraft relative to the transmitter and receiver, respectively. When the values ​​are known, the dual-station radar cross-section σ of the enemy aircraft B This can be obtained by querying the dual-station RCS database;

[0013] Then, when other parameters are known, the dual-station radar equation is used to calculate the detection power P of the dual-station radar to the enemy aircraft. R ;

[0014] Step 3: Determine the evaluation index that affects the detection effectiveness of the dual-station radar;

[0015] The evaluation indicators that affect the detection effectiveness of the dual-station radar include: defense area S F , the maximum distance T between the transmitter and receiver and the detection boundary far 、R far, and the maximum width W of the detection area max ;

[0016] The defense area S F Equal to the radar detection area S d and protection area S p The sum is:

[0017] S F =S d +S p

[0018]

[0019]

[0020] Where n is the number of receivers and i is the serial number of the receiver; is the detection area of ​​the i-th receiver, is the sum of the overlapping areas of the detection areas of the i-th receiver and the receivers with serial numbers less than i; is the area of ​​the defense airspace formed by connecting the trailing edge of the detection area of ​​the i-th receiver and the transmitter, is the sum of the overlapping areas of the defense airspace of the i-th receiver and the receivers with serial numbers less than i;

[0021] The detection area of ​​the i-th receiver The calculation process is as follows:

[0022] (1) Starting from the first grid cell of the alert airspace, the azimuth and elevation of the enemy aircraft relative to the transmitter and receiver are calculated, and the corresponding RCS value of the enemy aircraft is obtained by searching the dual-station RCS database of the enemy aircraft;

[0023] (2) Calculate the detection power of the bistatic radar against an enemy aircraft located in a grid cell;

[0024] (3) If the detection power is greater than the minimum detectable power of the bistatic radar, it means that our bistatic radar can detect the enemy aircraft at that grid cell, and the detection area increases by △x × △y; otherwise, our bistatic radar cannot detect the enemy aircraft at that grid cell, and the detection area does not change;

[0025] (4) Determine whether all grid cells have been traversed; if so, the calculation is complete; otherwise, select the next grid cell and return to execute (1);

[0026] Step 4: Based on the four evaluation indicators that affect the detection effectiveness of the dual-station radar, establish an evaluation model for our multi-aircraft cooperative detection formation configuration with separate transmitters and receivers;

[0027] The configuration evaluation model of our multi-aircraft cooperative detection formation with separate transmitters and receivers is shown in the following formula:

[0028] F=w1S F / S a +w2T far / L a +w3R far / L a +w4W max / (X max -X min )

[0029] S a =(X max -X min )(Y max -Y min )

[0030] L a =((X max -X min ) 2 +(Y max -Y min ) 2 ) 1 / 2

[0031] Among them, F is the index value for evaluating the optimization effect of our multi-machine cooperative detection formation configuration, S a is the conceptual value of the detection area of ​​our transmitter and receiver, L a is the diagonal length of the detection area of ​​our transmitter and receiver; [X min ,X max ]、[Y min ,Y max ] are the coordinate intervals of the detection area on the X axis and Y axis respectively; ω1, ω2, ω3 and ω4 are the evaluation indicators S of detection performance F 、T far 、R far and W max The weight of , which indicates the importance of each indicator detection performance, and satisfies

[0032] Step 5: Searching for the optimal formation configuration of the cooperative detection formation with separate transmitters and receivers based on the improved artificial fish swarm algorithm;

[0033] Step 5.1, initialize the deployment area of ​​the receiver;

[0034] Divide the deployment area of ​​the receiver relative to the transmitter according to the number of receivers n: take the transmitter location as the ray launch point, divide the transmitter detection near and far boundaries into n equal segments, and the part surrounded by the near and far boundaries is divided into n regions, each of which serves as the location search area of ​​a receiver, from left to right, region 1 to region n;

[0035] Step 5.2, initialize the artificial fish swarm;

[0036] Considering the maximization of the defense area, the receiver configurations on both sides of the transmitter's detection airspace bisector need to be symmetrical. Therefore, configuration optimization only needs to be performed on one side, and the other side remains symmetrical. Therefore, only the artificial fish schools within the search area of ​​the receiver positions located on and to the left of the transmitter's detection airspace bisector are initialized. For ease of description, the detection area of ​​the receiver placed at the artificial fish position is defined as the artificial fish's detection area. The specific steps include:

[0037] Step 5.2.1: When the number of receivers is odd, perform the following two steps to initialize the artificial fish school:

[0038] (a) Initialize the artificial fish swarm in the receiver position search area on the transmitter detection space bisector; first, generate an artificial fish at the intersection of the transmitter detection space bisector and the near boundary line; then, generate new artificial fish in the opposite direction of the line connecting the artificial fish and the transmitter at intervals of △d; each time an artificial fish is generated, its position information and the corresponding detection area are stored; when the distance between the newly generated artificial fish and the transmitter is greater than R far When , no more generation will be continued;

[0039] (b) Initialize the artificial fish swarms in the remaining areas; generate k marked artificial fish individuals on the near boundary line of each area in an equal distance division manner;

[0040] Step 5.2.2: When the number of receivers is even, perform the following two steps to initialize the artificial fish school:

[0041] 1) Initialize the artificial fish swarm in the first receiver position search area on the left side of the transmitter detection space bisector. First, initialize k marked artificial fish individuals on the near boundary line according to the distance equalization method. Then, for each artificial fish, generate a new artificial fish in the opposite direction of the line connecting it to the transmitter at an interval of △d. If the detection area of ​​the newly generated artificial fish covers the transmitter detection space bisector, store the position information of the artificial fish and its detection area. Otherwise, do not generate the artificial fish. When the distance between the newly generated artificial fish and the transmitter is greater than R far When , no more generation will be continued;

[0042] 2) Initialize the artificial fish swarms in the remaining areas; generate k marked artificial fish individuals on the near boundary line of each area in an equal distance division method;

[0043] Step 5.3: The artificial fish school performs foraging behavior;

[0044] Step 5.3.1: Adaptively adjust the current vision and step size of the artificial fish in the area p, where p < n / 2;

[0045] Define A pq as the q-th artificial fish at the position of the search receiver in the p-th area. The specific method of this step is as follows: For each artificial fish A pq , where 1 ≤ p ≤ n and 1 ≤ q ≤ k, calculate the average value of the distances between itself and the other k - 1 artificial fish in the same area and use this average value as its vision at the t-th iteration, and then calculate the step size of the movement The calculation formula is:

[0046]

[0047]

[0048]

[0049] where, is the distance between the artificial fish A pq used to search for the position of the p-th receiver at the t-th iteration and the artificial fish A pg , where q ≠ g; and respectively represent the current positions of the artificial fish A pq and the artificial fish A pg at the t-th iteration; a is the visual step coefficient, 0 < a < 1;

[0050] Step 5.3.2: Optimize the positions of the artificial fish in the area p, where p ≤ [n / 2];

[0051] Step 5.3.2.1: From p = n / 2 to p = 1, sequentially judge the k artificial fish in the area p. If there is an artificial fish in the right adjacent area whose detection area is connected to its detection area, then sequentially execute Steps 5.3.2.2 - 5.3.2.4; otherwise, execute Step 5.3.2.5;

[0052] Step 5.3.2.2: According to the multi - machine cooperative detection formation configuration evaluation model with separated transceiver, calculate the optimization index values of the formation configurations formed by each pair of artificial fish with connected detection areas in this area and the right adjacent area, and find the artificial fish corresponding to the maximum value;

[0053] Step 5.3.2.3: Let the artificial fish with the maximum optimization index value in this area swim a step - size distance along the opposite direction of the line connecting it to the transmitter, and then calculate the maximum optimization index value of the formation configuration formed by it and the artificial fish in the right adjacent area. If the index value after the artificial fish swims is greater than the index value before swimming, then repeat this step; otherwise, the artificial fish returns to its position before swimming and stops swimming;

[0054] Step 5.3.2.4: After all artificial fish in each area have stopped swimming, select one artificial fish from each area in turn according to the principle of regional connectivity, and perform permutations and combinations. Calculate the optimal index value for each formation configuration, and mark each artificial fish in the artificial fish formation with the maximum index value as the optimal artificial fish.

[0055] Step 5.3.2.5: Return to step 5.1, increase the number of divided areas by 1, and redivide the receiver's location search area;

[0056] Step 5.4, the artificial fish school performs flocking behavior;

[0057] Step 5.4.1, adaptively adjust the current field of view and step length of the non-optimal artificial fish;

[0058] Step 5.4.2: From p = n / 2 to p = 1, judge the k artificial fish in region p in turn. If the distance between a non-optimal artificial fish and the optimal artificial fish in its region is greater than the step length, and if the non-optimal artificial fish can ensure that the detection area of ​​the artificial fish in the right region is connected to its detection area when it swims to the next position according to the step length, then the non-optimal artificial fish swims one step toward the optimal artificial fish in the same region according to its respective step length; otherwise, it stops swimming.

[0059] Step 5.4.3: According to the principle of connectivity of the detection areas, select one artificial fish in each area in turn to perform permutations and combinations, calculate the optimization index value of the formation configuration corresponding to each combination, and mark each artificial fish in the artificial fish formation corresponding to the maximum index value as the optimal artificial fish;

[0060] Step 5.4.4: Determine whether all artificial fish have stopped swimming. If so, proceed to step 5.5; otherwise, return to step 5.4.1.

[0061] Step 5.5: The artificial fish school performs the tail-chasing behavior;

[0062] Step 5.5.1: Adaptively adjust the current field of view and step size of the non-optimal artificial fish;

[0063] Step 5.5.2: From p = n / 2 to p = 1, the k artificial fish in region p are judged in sequence. After the clustering behavior is completed, the stopped artificial fish in each region perform a circular motion around the optimal artificial fish. If the next position of the non-optimal artificial fish after performing a circular motion around the optimal artificial fish with a step size can ensure that the detection area of ​​the artificial fish in the right area is connected to its detection area, the non-optimal artificial fish swims one step around the optimal artificial fish; otherwise, the non-optimal artificial fish stops swimming.

[0064] Step 5.5.3: Determine whether all artificial fish are in a stopped swimming state or have completed a 360-degree circular motion. If so, proceed to step 5.6; otherwise, return to step 5.5.1;

[0065] Step 5.6: Obtain the optimal position of the artificial fish formation after executing the three behaviors as the optimal position of our receiver configuration. At this point, the optimal formation configuration search is successful.

[0066] The beneficial effects of adopting the above technical solution are as follows: the present invention provides a method for optimizing the configuration of an airborne radar network with separate transmitters and receivers for collaborative detection formations, which reduces the probability of detection of important friendly targets while also enhancing overall combat capability. The algorithm improves upon the traditional artificial fish school algorithm, optimizing the formation configuration search process and resolving the slow computational time issues of traversal algorithms and traditional artificial fish school algorithms. This reduces computational complexity, avoids falling into local optimality, and theoretically improves the detection range and efficiency of enemy targets. The research method demonstrated in this invention can provide a reference for mission planning, tactical deployment, and coordinated strikes in the practical application of aircraft swarms, and is of great significance to the development of my country's military power. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A flow chart of a method for optimizing the configuration of an airborne radar network with separate transmission and reception for collaborative detection formations provided by an embodiment of the present invention;

[0068] Figure 2 A schematic diagram of an improved artificial fish swarm algorithm provided by an embodiment of the present invention;

[0069] Figure 3 A schematic diagram of the foraging behavior process of an artificial fish school provided by an embodiment of the present invention;

[0070] Figure 4 A schematic diagram of the process of artificial fish swarming behavior provided by an embodiment of the present invention;

[0071] Figure 5 A schematic diagram of the process of an artificial fish school performing tail-chasing behavior provided by an embodiment of the present invention;

[0072] Figure 6 This is a rendering of the detection area of ​​the optimal configuration under the "one transmit, two receive" scenario provided by an embodiment of the present invention;

[0073] Figure 7 This is a rendering of the detection area effect of the optimal configuration under the "one transmit and three receive" situation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0075] This example uses the proposed method for optimizing the configuration of a collaborative detection formation with a networked airborne radar and a separate transmitter-receiver configuration to simulate a fighter formation collaborative mission model development and integration test system. The experimental computer is configured with an Intel Core i7-7700HQ processor, 2.8 GHz main frequency, 16 GB memory, and QT software.

[0076] In this embodiment, a method for optimizing the configuration of an airborne radar network with separate transmission and reception for collaborative detection formation is provided. Figure 1 As shown, the following steps are included:

[0077] Step 1: Initialize battlefield information;

[0078] Initialize the receiver to a configuration proximity distance R relative to the transmitter. near =50KM, the far distance is R far =300KM; the warning airspace is 400KM long and 686KM wide; and the warning airspace is divided into multiple grids, with the horizontal step length of the grid unit being △x=2KM and the vertical step length being △y=2KM;

[0079] Setting the combat context for both sides: This includes setting the initial positions of both sides and the combat mode. In this embodiment, the initial positions of both sides are set to be 350 km apart, and a multi-to-multi coordinated air combat is conducted.

[0080] Set combat missions for both sides: Our mission: Execute transmitter and receiver coordinated tactics to detect the enemy, and adjust the formation configuration of transmitters and receivers in real time to maximize detection efficiency; Enemy mission: Multiple aircraft form formation flight;

[0081] Set the initial situation of the enemy and our side: the initial situation of the enemy and our side is a head-on situation;

[0082] In this embodiment, our team is the red team and the enemy team is the blue team. The initial state information of the two teams is shown in Table 1.

[0083] Table 1 Initial status information of enemy and friendly formations

[0084]

[0085] Step 2: Establish a bistatic radar equation for a bistatic system with separate receivers and transmitters, and calculate the detection power of the bistatic radar against enemy aircraft.

[0086] Considering the propagation factor of the bistatic radar, the bistatic radar equation is as follows:

[0087]

[0088] Among them, R T is the distance between the transmitter and the enemy aircraft, R R is the distance between the receiver and the enemy aircraft; P T is the transmitter power, P R is the detection power of the dual-station radar to the enemy aircraft; G T , G R are the gains of the transmitting antenna and the receiving antenna respectively; λ is the radar operating wavelength; F T 、F R are the transmission and reception pattern propagation factors respectively; σ B is the enemy aircraft RCS value, that is, the dual-station radar cross-section, which is expressed as σ B =σ(α T ,β T ; α R ,β R ), α T , β T and α R , β R are the azimuth and elevation angles of the enemy aircraft relative to the transmitter and receiver, respectively. When the values ​​are known, the dual-station radar cross-section σ of the enemy aircraft B This can be obtained by querying the dual-station RCS database;

[0089] Furthermore, when other parameters are known, the detection power P of the dual-station radar to the enemy aircraft can be calculated using the dual-station radar equation. R ;

[0090] Step 3: Determine the evaluation index that affects the detection effectiveness of the dual-station radar;

[0091] The evaluation indicators that affect the detection effectiveness of the dual-station radar include: defense area S F , the maximum distance T between the transmitter and receiver and the detection boundary far 、R far , and the maximum width W of the detection area max ;

[0092] The defense area S F Equal to the radar detection area S d and protection area S p The sum of

[0093] S F =S d +S p

[0094]

[0095]

[0096] Where n is the number of receivers and i is the serial number of the receiver; is the detection area of ​​the i-th receiver, is the sum of the overlapping areas of the detection areas of the i-th receiver and the receivers with serial numbers less than i; is the area of ​​the defense airspace formed by connecting the trailing edge of the detection area of ​​the i-th receiver and the transmitter, It is the sum of the overlapping areas of the defense airspace of the i-th receiver and the receivers with serial numbers less than i.

[0097] In this embodiment, the detection area of ​​the i-th receiver is The calculation process is as follows:

[0098] (1) Starting from the first grid cell of the alert airspace, the azimuth and elevation of the enemy aircraft relative to the transmitter and receiver are calculated, and the corresponding RCS value of the enemy aircraft is obtained by searching the dual-station RCS database of the enemy aircraft;

[0099] (2) Calculate the detection power of the bistatic radar against an enemy aircraft located in a grid cell;

[0100] (3) If the detection power is greater than the minimum detectable power of the bistatic radar, it means that our bistatic radar can detect the enemy aircraft at that grid cell, and the detection area increases by △x × △y; otherwise, our bistatic radar cannot detect the enemy aircraft at that grid cell, and the detection area does not change;

[0101] (4) Determine whether all grid cells have been traversed. If so, the calculation is complete; otherwise, select the next grid cell and return to execute (1);

[0102] The larger the area of ​​the protection area and the detection area, the larger the defense area S F The larger the T is, the stronger the ability to effectively defend against enemy aircraft and the ability to preemptively defeat the enemy will be. far and R far As the width of the detection area increases, the transmitter and receiver are more difficult to be discovered and attacked, thereby improving security and feasibility; the maximum width of the detection area W max The larger the value, the larger the range of the enemy aircraft detection barrier that can be formed, which is beneficial to improving the detection efficiency of enemy aircraft;

[0103] Step 4: Based on the four evaluation indicators that affect the detection effectiveness of the dual-station radar, establish an evaluation model for our multi-aircraft cooperative detection formation configuration with separate transmitters and receivers;

[0104] The configuration evaluation model of our multi-aircraft cooperative detection formation with separate transmitters and receivers is shown in the following formula:

[0105] F=w1S F / S a +w2T far / L a +w3R far / L a +w4W max / (X max -X min )

[0106] S a =(X max -X min )(Y max -Y min )

[0107] L a =((X max -X min ) 2 +(Y max -Y min ) 2 ) 1 / 2

[0108] Among them, F is the index value for evaluating the optimization effect of our multi-machine cooperative detection formation configuration, S a is the conceptual value of the detection area of ​​our transmitter and receiver, L a is the diagonal length of the detection area of ​​our transmitter and receiver; [X min ,X max ]、[Y min ,Y max ] are the coordinate intervals of the detection area on the X axis and Y axis respectively; ω1, ω2, ω3 and ω4 are the evaluation indicators S of the detection efficiency F 、T far 、R far and W max The weight of , which indicates the importance of each indicator detection performance, and satisfies

[0109] Step 5: Based on Figure 2 The improved artificial fish school algorithm shown searches for the optimal formation configuration of our transmitter and receiver;

[0110] Step 5.1, initialize the deployment area of ​​the receiver;

[0111] Divide the deployment area of ​​the receiver relative to the transmitter according to the number of receivers n: take the transmitter location as the ray launch point, divide the transmitter detection near and far boundaries into n equal segments, and the part surrounded by the near and far boundaries is divided into n regions, each of which serves as the location search area of ​​a receiver, from left to right, region 1 to region n;

[0112] Step 5.2, initialize the artificial fish swarm;

[0113] Considering the maximization of the defense area, the receiver configurations on both sides of the bisector of the transmitter's detection airspace need to be symmetric. Only the configuration optimization is carried out on one side, and the other side is symmetric. Therefore, only the artificial fish swarm within the receiver position search area on and to the left of the bisector of the transmitter's detection airspace is initialized. For the convenience of description, the detection area of the receiver placed at the position of the artificial fish is defined as the detection area of the artificial fish. The specific steps are as follows:

[0114] Step 5.2.1: When the number of receivers is odd, the artificial fish swarm is initialized through the following two steps:

[0115] (a) Initialize the artificial fish swarm within the receiver position search area on the bisector of the transmitter's detection airspace. First, generate an artificial fish at the intersection of the bisector of the transmitter's detection airspace and the near boundary line. Then, generate new artificial fish at intervals of △d in the opposite direction of the line connecting the artificial fish and the transmitter. For each generated artificial fish, store its position information and the corresponding detection area. When the distance between the newly generated artificial fish and the transmitter is greater than R far no more generation is continued;

[0116] (b) Initialize the artificial fish swarm in the remaining areas. Generate k marked artificial fish individuals on the near boundary line in each area according to the equal distance division method.

[0117] Step 5.2.2: When the number of receivers is even, the artificial fish swarm is initialized through the following two steps:

[0118] 1) Initialize the artificial fish swarm within the receiver position search area of the first receiver to the left of the bisector of the transmitter's detection airspace. First, initialize k marked artificial fish individuals on the near boundary line according to the equal distance division method. Then, for each artificial fish, generate new artificial fish at intervals of △d in the opposite direction of the line connecting it and the transmitter. If the detection area of the newly generated artificial fish covers the bisector of the transmitter's detection airspace, store the position information of the artificial fish and its detection area; otherwise, do not generate this artificial fish. When the distance between the newly generated artificial fish and the transmitter is greater than R far no more generation is continued;

[0119] 2) Secondly, initialize the artificial fish swarm in the remaining areas. Generate k marked artificial fish individuals on the near boundary line in each area according to the equal distance division method.

[0120] Step 5.3: The artificial fish swarm performs foraging behavior, as Figure 3 shown;

[0121] Step 5.3.1: Adaptively adjust the current vision and step size of the artificial fish in area p, where p < n / 2;

[0122] Definition A pq For the qth artificial fish searching for the receiver position in the pth area, the specific steps of this step are: each artificial fish A pq , 1≤p≤n, 1≤q≤k calculate the average distance between itself and the other k-1 artificial fish in the same area And use the average value as the field of view of the tth iteration, and then calculate the step length The calculation formula is:

[0123]

[0124]

[0125]

[0126] in, is the artificial fish A used to search for the p-th receiver position at the t-th iteration pq To artificial fish A pg The distance between them, q≠g; and They represent the artificial fish A at the t-th iteration respectively. pq and artificial fish A pg The current position; a is the visual step coefficient, 0 <a<1;

[0127] Step 5.3.2, optimize the position of the artificial fish in the region p, p≤[n / 2];

[0128] Step 5.3.2.1. From p = n / 2 to p = 1, sequentially judge the k artificial fish in region p. If the detection area of ​​an artificial fish in the adjacent region to the right is connected to its detection area, execute steps 5.3.2.2-5.3.2.4 in sequence; otherwise, execute step 5.3.2.5.

[0129] Step 5.3.2.2: Based on the multi-machine cooperative detection formation configuration evaluation model with separate transmitters and receivers, calculate the optimal index value for each pair of artificial fish that have connected detection areas in the current area and the adjacent area to the right, and find the artificial fish with the maximum value.

[0130] Step 5.3.2.3: Instruct the artificial fish with the largest optimization index value in this area to swim a step distance in the direction opposite to the line connecting it to the transmitter. Then calculate the maximum optimization index value of the formation configuration formed by it and the artificial fish in the adjacent area to the right. If the index value of the artificial fish after swimming is greater than the index value before swimming, repeat this step; otherwise, the artificial fish returns to its original position and stops swimming.

[0131] Step 5.3.2.4: After all artificial fish in each area have stopped swimming, select one artificial fish from each area in turn according to the principle of regional connectivity, and perform permutations and combinations. Calculate the optimal index value for each formation configuration, and mark each artificial fish in the artificial fish formation with the maximum index value as the optimal artificial fish.

[0132] Step 5.3.2.5: Return to step 5.1, increase the number of divided areas by 1, and redivide the receiver's location search area;

[0133] Step 5.4: The artificial fish swarm performs the flocking behavior, such as Figure 4 As shown;

[0134] Step 5.4.1: Adaptively adjust the current field of view and step size of the non-optimal artificial fish using the same method as step 5.3.1.

[0135] Step 5.4.2: From p = n / 2 to p = 1, judge the k artificial fish in region p in turn. If the distance between a non-optimal artificial fish and the optimal artificial fish in its region is greater than the step length, and if the non-optimal artificial fish can ensure that the detection area of ​​the artificial fish in the right region is connected to its detection area when it swims to the next position according to the step length, then the non-optimal artificial fish swims one step toward the optimal artificial fish in the same region according to its respective step length; otherwise, it stops swimming.

[0136] Step 5.4.3: According to the principle of connectivity of the detection areas, select one artificial fish in each area in turn to perform permutations and combinations, calculate the optimization index value of the formation configuration corresponding to each combination, and mark each artificial fish in the artificial fish formation corresponding to the maximum index value as the optimal artificial fish;

[0137] Step 5.4.4: Determine whether all artificial fish have stopped swimming. If so, proceed to step 5.5; otherwise, return to step 5.4.1.

[0138] Step 5.5: The artificial fish swarm performs the tail-chasing behavior, such as Figure 5 As shown;

[0139] Step 5.5.1: Adaptively adjust the current field of view and step size of the non-optimal artificial fish using the same method as step 5.3.1;

[0140] Step 5.5.2: From p = n / 2 to p = 1, the k artificial fish in region p are judged in sequence. After the clustering behavior is completed, the stopped artificial fish in each region perform a circular motion around the optimal artificial fish. If the next position of the non-optimal artificial fish after performing a circular motion around the optimal artificial fish with a step size can ensure that the detection area of ​​the artificial fish in the right area is connected to its detection area, the non-optimal artificial fish swims one step around the optimal artificial fish; otherwise, the non-optimal artificial fish stops swimming.

[0141] Step 5.5.3: Determine whether all artificial fish are in a stopped swimming state. If so, proceed to step 5.6; otherwise, return to step 5.5.1.

[0142] Step 5.6: Obtain the optimal position of the artificial fish formation after executing the three behaviors as the optimal position of our receiver configuration. At this point, the optimal formation configuration search is successful.

[0143] In this embodiment, the simulation verification results are shown in the attached Figure 6 , Attachment Figure 7 As shown, the optimal configurations and their corresponding detection areas are shown in the cases of "one launch and two receptions" and "one launch and three receptions" respectively. In the early stage of the simulation verification experiment, the optimization process was carried out by traversing the entire area. The time required for the one launch and one reception case, one launch and two receptions, and one launch and three receptions were about 10 seconds, 15 seconds, and 23 seconds respectively. In order to improve the computing efficiency, the improved artificial fish school algorithm was chosen. After using the algorithm, the required time was reduced to 3 seconds, 5 seconds, and 13 seconds respectively, saving more than half of the time, and the calculated optimal formation configuration was slightly different from the optimal configuration obtained by using the traversal algorithm. Therefore, the method of the present invention fully meets the requirements of maximizing the detection area and quickly searching for targets while reducing the probability of our important targets being discovered.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A method for optimizing the configuration of an airborne radar network with separate transmitter and receiver cooperative detection formations, characterized by: The following steps are involved: Step 1: Initialize battlefield information; Step 2: Establish a bistatic radar equation for a bistatic system with separate receivers and transmitters, and calculate the detection power of the bistatic radar against enemy aircraft. Step 3: Determine the evaluation index that affects the detection effectiveness of the dual-station radar; The evaluation indicators that affect the detection effectiveness of the dual-station radar include: defense area S F , the maximum distance T between the transmitter and receiver and the detection boundary far 、R far , and the maximum width W of the detection area max ; Step 4: Based on the four evaluation indicators that affect the detection effectiveness of the dual-station radar, establish an evaluation model for our multi-aircraft cooperative detection formation configuration with separate transmitters and receivers; Step 5: Search for the optimal formation configuration of our transmitter and receiver based on the improved artificial fish school algorithm; Step 5.1, initialize the deployment area of ​​the transmitter; Divide the deployment area of ​​the receiver relative to the transmitter according to the number of receivers n: take the transmitter location as the ray launch point, divide the transmitter detection near and far boundaries into n equal segments, and the part surrounded by the near and far boundaries is divided into n regions, each of which serves as the location search area of ​​a receiver, from left to right, region 1 to region n; Step 5.2, initialize the artificial fish swarm; To maximize the defense area, the receiver configurations on both sides of the transmitter's detection airspace bisector must be symmetrical. Configuration optimization only needs to be performed on one side, while the other side remains symmetrical. Therefore, only the artificial fish swarms within the search area of ​​the receiver positions on and to the left of the transmitter's detection airspace bisector are initialized. The detection area of ​​the receiver placed at the artificial fish position is defined as the artificial fish's detection area. Step 5.3: The artificial fish school performs foraging behavior; Step 5.4: The artificial fish school performs flocking behavior; Step 5.5: The artificial fish school performs the tail-chasing behavior; Step 5.6: Obtain the optimal position of the artificial fish formation after executing the three behaviors as the optimal position of our receiver configuration. At this point, the optimal formation configuration search is successful.

2. The method for optimizing the configuration of an airborne radar network with separate transmission and reception for cooperative detection formation according to claim 1, characterized in that: The battlefield information initialized in step 1 includes the configuration proximity distance R between the receiver and the transmitter. near , the far distance is R far ; The area of ​​the alert airspace is Ω S , and divide the warning airspace into multiple grids, with the horizontal step length of the grid unit being Δx and the vertical step length being Δy.

3. The method for optimizing the configuration of an airborne radar network with separate transmission and reception for cooperative detection formation according to claim 2, characterized in that: The bistatic radar equation described in step 2 takes into account the propagation factor of the bistatic radar, as shown in the following formula: Among them, R T is the distance between the transmitter and the enemy aircraft, R R is the distance between the receiver and the enemy aircraft; P T is the transmitter power, P R is the detection power of the dual-station radar to the enemy aircraft; G T , G R are the gains of the transmitting antenna and the receiving antenna respectively; λ is the radar operating wavelength; F T 、F R are the transmission and reception pattern propagation factors respectively; σ B is the enemy aircraft RCS value, that is, the dual-station radar cross-section, which is expressed as σ B =σ(α T ,β T ; α R ,β R ), α T , β T and α R , β R are the azimuth and elevation angles of the enemy aircraft relative to the transmitter and receiver, respectively; Then, when other parameters are known, the dual-station radar equation is used to calculate the detection power P of the dual-station radar to the enemy aircraft. R .

4. The method for optimizing the configuration of an airborne radar network with separate transmission and reception for cooperative detection formation according to claim 3, characterized in that: The defensive area S described in step 3 F Equal to the radar detection area S d and protection area S p The sum is: S F =S d +S p Where n is the number of receivers and i is the serial number of the receiver; is the detection area of ​​the i-th receiver, is the sum of the overlapping areas of the detection areas of the i-th receiver and the receivers with serial numbers less than i; is the area of ​​the defense airspace formed by connecting the trailing edge of the detection area of ​​the i-th receiver and the transmitter, is the sum of the overlapping areas of the defense airspace of the i-th receiver and the receivers with serial numbers less than i; The detection area of ​​the i-th receiver The calculation process is as follows: 1) Starting from the first grid cell of the alert airspace, calculate the azimuth and elevation of the enemy aircraft relative to the transmitter and receiver, and obtain the corresponding enemy aircraft RCS value by searching the dual-station RCS database of the enemy aircraft; 2) Calculate the detection power of the bistatic radar against an enemy aircraft located in a grid cell; 3) If the detection power is greater than the minimum detectable power of the bistatic radar, it means that the bistatic radar can detect the enemy aircraft at that grid cell, and the detection area increases by Δx × Δy. Otherwise, the bistatic radar cannot detect the enemy aircraft at that grid cell, and the detection area does not change. 4) Determine whether all grid cells have been traversed; if so, the calculation is complete; otherwise, select the next grid cell and return to execute 1).

5. The method for optimizing the configuration of an airborne radar network with separate transmission and reception for cooperative detection formation according to claim 4, characterized in that: The configuration evaluation model of our multi-aircraft cooperative detection formation with separate transmitters and receivers established in step 4 is shown in the following formula: F=w1S F / S a +w2T far / L a +w3R far / L a +w4W max / (X max -X min ) S a =(X max -X min )(AND max -AND min ) L a =((X max -X min ) 2 +(And max -AND min ) 2 ) 1 / 2 Among them, F is the index value for evaluating the optimization effect of our multi-machine cooperative detection formation configuration, S a is the conceptual value of the detection area of ​​our transmitter and receiver, L a is the diagonal length of the detection area of ​​our transmitter and receiver; [X min ,X max ]、[Y min ,Y max ] are the coordinate intervals of the detection area on the X axis and Y axis respectively; w1, w2, w3 and w4 are the evaluation indicators S of detection performance F 、T far 、R far and W max The weight of , which indicates the importance of each indicator detection performance, and satisfies 6. The method for optimizing the configuration of an airborne radar network with separate transmission and reception for cooperative detection formation according to claim 5, characterized in that: The specific method of step 5.2 is: Step 5.2.1: When the number of receivers is odd, perform the following two steps to initialize the artificial fish school: a) Initializing an artificial fish swarm within a receiver position search area on a transmitter detection airspace bisector; first, generating an artificial fish swarm located at the intersection of the transmitter detection airspace bisector and the near boundary line; Then, a new artificial fish is generated in the opposite direction of the line connecting the artificial fish and the transmitter at an interval of Δd. Each time an artificial fish is generated, its position information and the corresponding detection area are stored. When the distance between the newly generated artificial fish and the transmitter is greater than R far When , no more generation will be continued; b) Initialize the artificial fish swarms in the remaining areas; generate k marked artificial fish individuals on the near boundary line of each area in an equal distance manner; Step 5.2.2: When the number of receivers is even, perform the following two steps to initialize the artificial fish school: 1) Initialize the artificial fish swarm in the search area of ​​the first receiver position on the left side of the transmitter detection space bisector; First, initialize k marked artificial fish individuals on the near boundary line according to the distance equalization method; Then, for each artificial fish, a new artificial fish is generated forward at intervals of Δd in the opposite direction of the line connecting it to the transmitter; if the detection area of ​​the newly generated artificial fish covers the bisector of the transmitter's detection space, the position information of the artificial fish and its detection area are stored; Otherwise, the artificial fish is not generated; when the distance between the newly generated artificial fish and the transmitter is greater than R far When , no more generation will be continued; 2) Initialize the artificial fish swarms in the remaining areas; generate k marked artificial fish individuals on the near boundary line of each area in an equal distance division manner.

7. The method for optimizing the configuration of an airborne radar network with separate transmission and reception for cooperative detection formation according to claim 6, characterized in that: The specific method of step 5.3 is: Step 5.3.1, adaptively adjust the current field of view and step size of the artificial fish within the region p, p < n / 2; Definition A pq For the qth artificial fish searching for the receiver position in the pth area, the specific steps of this step are: each artificial fish A pq , 1≤p≤n, 1≤q≤k calculate the average distance between itself and the other k-1 artificial fish in the same area And use the average value as the field of view of the tth iteration, and then calculate the step length The calculation formula is: in, is the artificial fish A used to search for the p-th receiver position at the t-th iteration pq To artificial fish A pg The distance between them, q≠g; and They represent the artificial fish A at the t-th iteration respectively. pq and artificial fish A pg The current position; a is the visual step coefficient, 0 <a<1; Step 5.3.2, optimize the position of the artificial fish in the region p, p≤[n / 2]; Step 5.3.2.

1. From p = n / 2 to p = 1, sequentially judge the k artificial fish in region p. If the detection area of ​​an artificial fish in the adjacent region to the right is connected to its detection area, execute steps 5.3.2.2 to 5.3.2.4 in sequence; otherwise, execute step 5.3.2.

5. Step 5.3.2.2: Based on the multi-machine cooperative detection formation configuration evaluation model with separate transmitters and receivers, calculate the optimal index value for each pair of artificial fish that have connected detection areas in the current area and the adjacent area to the right, and find the artificial fish with the maximum value. Step 5.3.2.3: Instruct the artificial fish with the largest optimization index value in this area to swim a step distance in the direction opposite to the line connecting it to the transmitter. Then calculate the maximum optimization index value of the formation configuration formed by it and the artificial fish in the adjacent area to the right. If the index value of the artificial fish after swimming is greater than the index value before swimming, repeat this step; otherwise, the artificial fish returns to its original position and stops swimming. Step 5.3.2.4: After all artificial fish in each area have stopped swimming, select one artificial fish from each area in turn according to the principle of regional connectivity, and perform permutations and combinations. Calculate the optimal index value for each formation configuration, and mark each artificial fish in the artificial fish formation with the maximum index value as the optimal artificial fish. Step 5.3.2.5: Return to step 5.1, increase the number of divided areas by 1, and re-divide the receiver's location search area.

8. The method for optimizing the configuration of an airborne radar network with separate transmission and reception for cooperative detection formation according to claim 7, characterized in that: The specific method of step 5.4 is: Step 5.4.1, adaptively adjust the current field of view and step length of the non-optimal artificial fish; Step 5.4.2: From p = n / 2 to p = 1, judge the k artificial fish in region p in turn. If the distance between a non-optimal artificial fish and the optimal artificial fish in its region is greater than the step length, and if the non-optimal artificial fish can ensure that the detection area of ​​the artificial fish in the right region is connected to its detection area when it swims to the next position according to the step length, then the non-optimal artificial fish swims one step toward the optimal artificial fish in the same region according to its respective step length; otherwise, it stops swimming. Step 5.4.3: According to the principle of connectivity of the detection areas, select one artificial fish in each area in turn to perform permutations and combinations, calculate the optimization index value of the formation configuration corresponding to each combination, and mark each artificial fish in the artificial fish formation corresponding to the maximum index value as the optimal artificial fish; Step 5.4.4: Determine whether all artificial fish have stopped swimming. If so, proceed to step 5.5; otherwise, return to step 5.4.

1.

9. The method for optimizing the configuration of an airborne radar network with separate transmission and reception for cooperative detection formation according to claim 8, characterized in that: The specific method of step 5.5 is: Step 5.5.1: Adaptively adjust the current field of view and step size of the non-optimal artificial fish; Step 5.5.2: From p = n / 2 to p = 1, the k artificial fish in region p are judged in sequence. If the next position of the non-optimal artificial fish after performing a circular motion around the optimal artificial fish with a step size can ensure that the detection area of ​​the artificial fish in the right area is connected to its detection area, the non-optimal artificial fish swims one step around the optimal artificial fish; otherwise, the non-optimal artificial fish stops swimming. Step 5.5.3: Determine whether all artificial fish are in a stopped swimming state or have completed a 360-degree circular motion. If so, proceed to step 5.6; otherwise, return to step 5.5.1.

Images