Warship-aircraft cooperative detection method based on marine environment influence factors

Abstract

In the modern underwater wars, the complex and changeable marine environment has great influence on the detection capability of sonar. A surface ship and an antisubmarine helicopter execute search tasks according to respective characteristics and cannot give full play to the advantage of cooperative combat. According to the invention, ship-aircraft cooperative detection method research based on marine environment influence factors is carried out, from an actual scene, an optimization method for sonar detection distance analysis is provided, the actual sonar detection distance of an undetermined search area is visualized, a ship-aircraft cooperative search algorithm is provided, a search platform motion model is established and optimized, and the sonar detection distance of the undetermined search area is optimized. And a key parameter adjustment strategy for enabling the ship-aircraft collaborative search algorithm to reach the maximum search probability is obtained. And selecting the search probability as an evaluation index to carry out an experimental test. Researches show that when a surface ship and an antisubmarine helicopter are in cooperative operation, high detection probability can be stably realized. Therefore, the feasibility and effectiveness of the ship-aircraft cooperative detection method based on the marine environment influence factors are proved.

Description

Technical Field

[0001] The invention relates to a ship-aircraft cooperative detection method based on marine environmental influencing factors, and belongs to the field of underwater target searching. Background Art

[0002] The underwater battlefield has become one of the main battlefields of naval warfare today due to its good concealment. Germany's submarine warfare in World War II once caused great damage to the Allied forces' maritime transportation lines, making countries around the world realize the tremendous power of underwater warfare. After the end of World War II, with the development of technologies such as vibration reduction and noise reduction, and intelligent torpedoes, submarines have made great progress in concealment, maneuverability, and attack range. All countries have also attached great importance to the development of underwater combat technology, and are constantly strengthening the construction of underwater platforms and underwater weapons and equipment to improve their underwater combat capabilities.

[0003] For a long time, relying on a single branch of the military, such as surface ships, submarines, and aviation, to carry out anti-submarine operations has been far from adapting to the development of the new situation. Although there has been considerable in-depth research on surface ship anti-submarine and helicopter anti-submarine, there are still some unavoidable problems. The anti-submarine capability of surface ships is relatively limited. Due to the speed of navigation, it is difficult to move quickly to the mission point when performing on-call anti-submarine missions; and because surface ships are exposed on the water, surface ships are relatively easy to be detected and attacked; at the same time, the movement of surface ships is limited in severe sea conditions, which may affect the execution of their anti-submarine missions. There are also problems with helicopter anti-submarine operations. The endurance of anti-submarine helicopters is usually more limited than that of surface ships, and they need to return to the mother ship or base regularly for fuel refueling; the weapon load of helicopters is relatively limited, and they can usually only carry a limited number of torpedoes or depth charges; helicopter anti-submarine operations usually rely on mother ships or bases, and require appropriate logistical support. Surface ship anti-submarine and helicopter anti-submarine each have their own advantages and limitations. With the development of submarines in noise reduction, speed increase, and avoidance of anti-submarine aircraft detection, relying solely on single-base submarine detection such as anti-submarine helicopters and surface ships can no longer meet the requirements of anti-submarine operations. It is imperative to seek a multi-base coordinated submarine search method with various forms of combination.

[0004] In the field of anti-submarine ship-aircraft coordinated detection, the research so far has mainly focused on analyzing the combat effects of surface ships and anti-submarine helicopters. When these research results are applied to actual submarine search missions, surface ships and anti-submarine helicopters will perform search missions according to their own characteristics, and cannot give full play to the advantages of coordinated operations. Zhang Yuhang et al. proposed a method of combined dual-base submarine search with towed sonar and helicopter-suspended sonar to improve the effectiveness of on-call submarine search. Combining the actual movement characteristics of surface ships and helicopters, this paper established a dual-base joint submarine search model of extended square, extended circle, and extended spiral. The submarine search probability of each formation dual-base and non-dual-base was compared through simulation, and the influence of the initial distance of the submarine, the position distribution of the submarine, and the speed distribution of the submarine on the submarine search probability under the on-call submarine search formation was analyzed horizontally. Yang Yuanchao et al. established an anti-submarine formation optimization mathematical model during the formation crossing process, and conducted a sensitivity analysis of the factors affecting the formation anti-submarine combat effectiveness.

[0005] The ocean is the combat space for submarines and anti-submarine forces. The ocean environment plays a decisive role in the detection and tracking of acoustic sensors. It is crucial for submarines and anti-submarine forces to fully understand and master the ocean battlefield environment. Rational use of the ocean environment and efficient use of sonar effectiveness are the basic conditions for winning underwater battles. With the continuous deepening of research on the effects of the ocean environment, the impact of many phenomena and effects in the underwater acoustic environment on the design and use of sonar equipment can be quantitatively described. Because the ocean environment is changing all the time, the impact of environmental information on war is sometimes huge, and the underwater environment has a certain impact on the propagation of underwater acoustic signals, which will interfere with the detection probability of sonar. Based on this, experts and scholars at home and abroad have begun to focus on studying the impact of the underwater environment on sonar detection capabilities.

[0006] Aiming at the important combat task of target search in underwater combat, the present invention starts from the actual scenario, introduces the ocean environment data of the to-be-determined search area, proposes an optimization method for sonar detection distance analysis, and visualizes the actual sonar detection distance of the to-be-determined search area; designs a rectangular search algorithm for surface ships, proposes a ship-aircraft collaborative search algorithm based on collaborative search, establishes a search platform motion model, and realizes the improvement of search probability; optimizes the ship-aircraft collaborative search algorithm based on collaborative search, and obtains the key parameter adjustment strategy that enables the ship-aircraft collaborative search algorithm to achieve the maximum search probability under the collaborative search mode. Summary of the invention

[0007] The technical problem solved by the present invention is to propose a ship-aircraft collaborative detection method based on marine environmental influencing factors.

[0008] A ship-aircraft collaborative detection method based on marine environmental influencing factors of the present invention comprises:

[0009] S1. Starting from the actual scenario, the ocean environment data of the pending search area is introduced, and an optimization method for sonar detection distance analysis is proposed to visualize the actual sonar detection distance of the pending search area;

[0010] S2. Design a rectangular search algorithm for surface ships, propose a ship-aircraft collaborative search algorithm based on collaborative search, and establish a search platform motion model;

[0011] S3. The ship-aircraft collaborative search algorithm based on collaborative search was optimized, and the key parameter adjustment strategy for the ship-aircraft collaborative search algorithm to achieve the maximum search probability under the collaborative search mode was obtained.

[0012] Preferably, the S1 includes:

[0013] S11. Select a sea area as the pending search area, extract the marine environment data of the pending search area, select a coordinate point in the pending search area as the sonar detection point; with the detection point as the center, randomly select a direction as the azimuth, and combine the marine environment data of the pending search area to obtain the detection distance of the sonar at the detection point along the direction; with the detection point as the center, uniformly select azimuths, and the number of azimuths increases continuously from 1. First, calculate the detection distance of the sonar at the detection point along the selected azimuth through the sonar detection capability analysis model, and then calculate the median as the actual detection distance of the sonar at the detection point, and obtain the relationship between the number of selected azimuths and the actual detection distance of the sonar at the detection point. The calculation formula of the sonar quality factor is:

[0014] FOM=SL-NL-DT+DI+TS

[0015] Where: SL represents the sound source level, NL represents the noise level, DT represents the detection threshold of the sonar equipment, and DI represents the receiving directivity index.

[0016] The calculation formula of sonar detection probability can be expressed as:

[0017]

[0018] Where: SE represents the signal margin, erfc[x] is the complementary error function.

[0019] The calculation formula of signal margin can be expressed as:

[0020] SE=FOM-2TL

[0021] Where: TL represents the propagation loss on the transmit path and the receive path.

[0022] S12. Define a threshold to determine the number of azimuths where the actual detection distance tends to be stable, so as to obtain the actual detection distance of each coordinate point in the undetermined search area and draw a distribution diagram of the actual detection distance of the sonar. The rate of change of the actual detection distance can be expressed as:

[0023]

[0024] Where: D N It represents the actual detection distance of the sonar calculated at N azimuth angles, and ΔD represents the rate of change of the actual detection distance of the sonar after adding M azimuth angles.

[0025] Preferably, in S2, a surface ship is set up, which starts from the surface ship initial position, travels in a direction close to the expected initial position of the submarine, and goes to the surface ship search starting position. The surface ship search starting position is determined by the search line width. The coordinates of the surface ship search starting position are located on a rectangular path with the expected initial position of the submarine as the center and the search line width as the side length. After reaching the search starting position, the surface ship will start searching in a clockwise direction along the rectangular search path; combining the surface ship rectangular search algorithm and the anti-submarine helicopter spiral search algorithm, a ship-aircraft collaborative search algorithm based on collaborative search is proposed. After the combat starts, the surface ship and the anti-submarine helicopter respectively start from the surface ship initial position and start searching according to their respective search algorithms.

[0026] Preferably, in S3, a certain combat environment is set, key parameters of the ship-aircraft collaborative search algorithm are adjusted, changes in the search probability in the collaborative search mode are observed, simulation experiments are designed, the relationship between the key parameters and the search probability of the ship-aircraft collaborative search algorithm in the collaborative search mode is obtained, and the key parameter values ​​of the rectangular search algorithm for surface ships and the spiral search algorithm for anti-submarine helicopters in the independent search mode are compared to obtain the key parameter adjustment strategy for achieving the maximum search probability using the ship-aircraft collaborative search algorithm in the collaborative search mode.

[0027] The beneficial effects of the present invention are as follows: firstly, the present invention provides an optimization method for analyzing the sonar detection distance in an underwater combat system, combines the actual ocean environment data, selects the median as a statistical method, objectively describes the actual detection capability of the sonar at the detection point, and further draws a distribution diagram of the actual sonar detection distance of the to-be-determined search area; secondly, a rectangular search algorithm for surface ships is designed, and a ship-aircraft collaborative search algorithm is proposed in combination with the anti-submarine helicopter spiral search algorithm, and an experiment is designed to compare the search probabilities of the ship-aircraft collaborative search algorithm in the collaborative search mode and the surface ship rectangular search algorithm and the anti-submarine helicopter spiral search algorithm in the independent search mode, proving the feasibility and effectiveness of the ship-aircraft collaborative search algorithm; finally, the changes in key parameters when the search probability is maximized under the ship-aircraft collaborative search algorithm are explored, and the key parameter adjustment strategy that maximizes the search probability in the collaborative search mode is obtained, and the optimization of the collaborative algorithm is completed. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is the overall framework diagram of the ship-aircraft collaborative detection method based on the influencing factors of the marine environment.

[0029] Figure 2 It is a framework diagram of the optimization method for sonar detection distance analysis.

[0030] Figure 3 It is the search path diagram of the rectangular search algorithm for surface ships.

[0031] Figure 4 It is a framework diagram of the ship-aircraft collaborative search algorithm and its optimization method.

[0032] Figure 5 is the sound velocity profile.

[0033] Figure 6 is the propagation loss distribution diagram.

[0034] Figure 7 It is the sound propagation diagram.

[0035] Figure 8 This is a graph showing the relationship between the number of azimuth angles and the actual detection distance of the sonar.

[0036] Fig. 9 It is the distribution diagram of the actual detection distance of sonar. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the examples described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0039] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0040] This embodiment provides a ship-aircraft collaborative detection method based on the influencing factors of the marine environment. This method combines the complex marine environment, proposes an optimization method for sonar detection distance analysis, and visualizes the actual sonar detection distance of the undetermined search area; designs a rectangular search algorithm for surface ships, proposes a ship-aircraft collaborative search algorithm based on collaborative search, and optimizes it, using the search probability as the overall evaluation index, and conducts experimental tests. The overall block diagram of this embodiment is shown below. Figure 1 The method of this embodiment includes:

[0041] S1. Starting from the actual scenario, the ocean environment data of the pending search area is introduced, and an optimization method for sonar detection distance analysis is proposed to visualize the actual sonar detection distance of the pending search area, including:

[0042] S11. Select a sea area as the pending search area, extract the marine environment data of the pending search area, select a coordinate point in the pending search area as the sonar detection point; with the detection point as the center, randomly select a direction as the azimuth, and combine the marine environment data of the pending search area to obtain the detection distance of the sonar at the detection point along the direction; with the detection point as the center, uniformly select azimuths, and the number of azimuths increases continuously from 1. First, calculate the detection distance of the sonar at the detection point along the selected azimuth through the sonar detection capability analysis model, and then calculate the median as the actual detection distance of the sonar at the detection point, and obtain the relationship between the number of selected azimuths and the actual detection distance of the sonar at the detection point. The calculation formula of the sonar quality factor is:

[0043] FOM=SL-NL-DT+DI+TS

[0044] Where: SL represents the sound source level, NL represents the noise level, DT represents the detection threshold of the sonar equipment, and DI represents the receiving directivity index.

[0045] The calculation formula of sonar detection probability can be expressed as:

[0046]

[0047] Where: SE represents the signal margin, erfc[x] is the complementary error function.

[0048] The calculation formula of signal margin can be expressed as:

[0049] SE=FOM-2TL

[0050] Where: TL represents the propagation loss on the transmit path and the receive path.

[0051] The main steps for calculating the relationship between the azimuth angle and the actual detection distance of the sonar are shown in Table 1.

[0052] S12. Define a threshold to determine the number of azimuths where the actual detection distance tends to be stable, so as to obtain the actual detection distance of each coordinate point in the undetermined search area and draw a distribution diagram of the actual detection distance of the sonar. The rate of change of the actual detection distance can be expressed as:

[0053]

[0054] Where: D N It represents the actual detection distance of the sonar calculated at N azimuth angles, and ΔD represents the rate of change of the actual detection distance of the sonar after adding M azimuth angles.

[0055] In order to further describe the pros and cons of the sonar detection performance, combined with S11, the actual sonar detection distance of the entire undetermined search area is calculated. The main steps of the calculation process are shown in Table 2. The overall framework of the optimization method for sonar detection distance analysis is shown in Figure 2 shown.

[0056] Table 1 The relationship between the calculated azimuth number and the actual detection distance of the sonar

[0057]

[0058]

[0059] Table 2 Calculation of actual sonar detection distance of the undetermined search area

[0060]

[0061] S2. Design a rectangular search algorithm for surface ships, propose a ship-aircraft collaborative search algorithm based on collaborative search, and establish a search platform motion model:

[0062] A surface ship is set up. It starts from the surface ship initial position and travels in the direction close to the expected initial position of the submarine to the surface ship search starting position. The surface ship search starting position is determined by the search line width. The coordinates of the surface ship search starting position are located on a rectangular path with the expected initial position of the submarine as the center and the search line width as the side length. After reaching the search starting position, the surface ship will start searching in a clockwise direction along the rectangular search path. Combining the rectangular search algorithm of the surface ship and the spiral search algorithm of the anti-submarine helicopter, a ship-aircraft collaborative search algorithm based on collaborative search is proposed. After the combat begins, the surface ship and the anti-submarine helicopter start searching from the initial position of the surface ship respectively according to their respective search algorithms.

[0063] The steps for implementing the surface ship rectangle search algorithm are shown in Table 3. The search path is as follows: Figure 3 The implementation steps of the ship-aircraft collaborative search algorithm are shown in Table 4.

[0064] Table 3 Surface ship rectangle search algorithm

[0065]

[0066]

[0067] Table 4 Ship-aircraft collaborative search algorithm

[0068]

[0069] S3. The ship-aircraft collaborative search algorithm based on collaborative search was optimized, and the key parameter adjustment strategy for the ship-aircraft collaborative search algorithm to achieve the maximum search probability in the collaborative search mode was obtained:

[0070] A certain combat environment is set, the key parameters of the ship-aircraft collaborative search algorithm are adjusted, the changes in the search probability under the collaborative search mode are observed, and a simulation experiment is designed to obtain the relationship between the key parameters and the search probability of the ship-aircraft collaborative search algorithm under the collaborative search mode. The key parameter values ​​of the rectangular search algorithm for surface ships and the spiral search algorithm for anti-submarine helicopters in the independent search mode are compared to obtain the key parameter adjustment strategy for achieving the maximum search probability using the ship-aircraft collaborative search algorithm in the collaborative search mode.

[0071] The optimization steps of the ship-aircraft collaborative search algorithm are shown in Table 5. The implementation steps of the ship-aircraft collaborative search algorithm and the overall framework of its optimization method are shown in Table 5. Figure 4 shown.

[0072] Table 5 Ship-aircraft collaborative search algorithm optimization

[0073]

[0074]

[0075] Experimental data processing and analysis algorithm:

[0076] 1. Experimental environment

[0077] The ship-aircraft collaborative detection method based on marine environmental influencing factors proposed in this embodiment is implemented through Matlab programming. The experimental equipment uses a processor Inter(R)Core(TM)i7-9700 CPU@3.00GHz, 32G installed memory, 64-bit Windows 10 operating system, and GPU model GEFORCE RTX 2080Ti.

[0078] 2. Hydrological environment dataset

[0079] The WOA2018 dataset is an ocean hydrological dataset released by the US NOAA organization in 2018. It includes data such as water temperature, salinity, and oxygen content, with a longitude and latitude accuracy of up to 15 minutes. The topo_19.1 dataset was released by Dr. Sandwell's research team in 2014 and contains sea depth data for the global ocean with an accuracy of up to 1 minute.

[0080] 3. Evaluation indicators

[0081] A Monte Carlo experiment method is designed. The simulation experiment is repeated N times. The number of times the sonar searches for submarines is recorded as n. The search probability is the ratio of the number of times the sonar searches for submarines to the total number of searches, expressed as:

[0082] p=n / N

[0083] 4. Experimental parameters

[0084] The sea area centered at (132°E, 22°N) was selected as the pending search area, and a 100×100 mission area was simulated and generated. Combined with the actual marine environment data, the actual sonar detection distance distribution map of the pending search area was obtained.

[0085] After determining the pending search area, the surface ship rectangular search algorithm is used to calculate the distance from the initial position of the surface ship to the initial position of the submarine and the relationship between the surface ship search line width and the search probability. The main steps and parameters are shown in Tables 3 and 5.

[0086] After determining the pending search area, the ship-aircraft collaborative algorithm is used to calculate the distance from the initial position of the surface ship to the initial position of the submarine and the relationship between the search line width of the surface ship and the search probability. The main steps and parameters are shown in Tables 4 and 5.

[0087] Simulation and results analysis

[0088] After selecting the sea area centered at (132°E, 22°N) as the undetermined search area and determining the coordinates of the detection point, the sound velocity profile of the detection point is calculated as follows: Figure 5 In order to analyze the influence of ocean environment and seabed topography on sonar detection performance, the seabed propagation loss distribution and sound line propagation curve were drawn, as shown in Figure 6 and Figure 7 As shown in the figure, the emission source is located at a depth of 1000m from the horizontal plane. Considering the propagation distance is 75km, the value range of the sound field energy is 0-100dB. Figure 6 , Figure 7 It can be seen that the propagation loss is increasing with the increase of detection distance. At the same time, the sound propagation curve will also be affected by the submarine mountains. This also shows that the quality of sonar detection performance is closely related to the marine environment.

[0089] With the detection point as the center, azimuths are uniformly selected, and the number of azimuths increases continuously from 1. The detection distance of the sonar at the detection point along the selected azimuth is first calculated through the sonar detection capability analysis model, and then the median is calculated as the actual detection distance of the sonar at the detection point. The relationship between the number of selected azimuths and the actual detection distance of the sonar at the detection point is obtained, as shown in Figure 8 As shown. Figure 8 It can be seen that as the number of selected azimuth angles increases, the actual detection distance of the sonar at the detection point gradually tends to be stable, but the occupation of computing resources and the time spent on calculation also increase accordingly. Therefore, choosing the appropriate number of azimuth angles can effectively save computing resources and speed up the calculation.

[0090] Define a threshold to determine the number of azimuths at which the actual detection distance tends to be stable, so as to obtain the actual detection distance of each coordinate point in the undetermined search area and draw a distribution diagram of the actual detection distance of the sonar. Fig. 9 As shown. Fig. 9 It can be seen that the actual sonar detection distances obtained at different coordinate points in the undetermined search area are different, and the numerical fluctuation range is large, which also shows that the quality of sonar detection performance is closely related to the marine environment.

[0091] Although the present invention is described with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the features described in the various dependent claims and the present invention may be combined in a manner different from that described in the original claims. It should also be understood that the features described in conjunction with the individual embodiments may be used in other described embodiments.

Claims

1. A ship-aircraft collaborative detection method based on marine environmental influencing factors is characterized by: include: S1. Starting from the actual scenario, the ocean environment data of the pending search area is introduced, and an optimization method for sonar detection distance analysis is proposed to visualize the actual sonar detection distance of the pending search area; S2. Design a rectangular search algorithm for surface ships, propose a ship-aircraft collaborative search algorithm based on collaborative search, and establish a search platform motion model; S3. The ship-aircraft collaborative search algorithm based on collaborative search was optimized, and the key parameter adjustment strategy for the ship-aircraft collaborative search algorithm to achieve the maximum search probability under the collaborative search mode was obtained.

2. The ship-aircraft collaborative detection method based on marine environmental influencing factors according to claim 1 is characterized in that: In S1: Select a sea area as a pending search area, extract the marine environment data of the pending search area, and select a coordinate point in the pending search area as a sonar detection point; With the detection point as the center, azimuths are uniformly selected, and the number of azimuths increases continuously from 1. The detection distance of the sonar at the detection point along the selected azimuth is first calculated through the sonar detection capability analysis model, and then the median is calculated as the actual detection distance of the sonar at the detection point, and the relationship between the number of selected azimuths and the actual detection distance of the sonar at the detection point is obtained. The calculation formula of the sonar quality factor is: FOM=SL-NL-DT+DI+TS Where: SL represents the sound source level, NL represents the noise level, DT represents the detection threshold of the sonar equipment, and DI represents the receiving directivity index. The calculation formula of sonar detection probability can be expressed as: Where: SE represents the signal margin, erfc[x] is the complementary error function. The calculation formula of signal margin can be expressed as: SE=FOM-2TL Where: TL represents the propagation loss on the transmit path and the receive path. Define a threshold to determine the number of azimuths where the actual detection distance tends to be stable, so as to obtain the actual detection distance of each coordinate point in the undetermined search area and draw a distribution diagram of the actual detection distance of the sonar. The rate of change of the actual detection distance can be expressed as: Where: D N It represents the actual detection distance of the sonar calculated at N azimuth angles, and ΔD represents the rate of change of the actual detection distance of the sonar after adding M azimuth angles.

3. The ship-aircraft collaborative detection method based on marine environmental influencing factors according to claim 1 is characterized in that: In S2: A surface ship is set up, which starts from the surface ship initial position and travels in a direction close to the expected initial position of the submarine to the surface ship search starting position. The surface ship search starting position is determined by the search line width. The coordinates of the surface ship search starting position are located on a rectangular path with the expected initial position of the submarine as the center and the search line width as the side length. After the surface ship reaches the search starting position, it will start searching in a clockwise direction along the rectangular search path; Combining the rectangular search algorithm of surface ships and the spiral search algorithm of anti-submarine helicopters, a ship-aircraft collaborative search algorithm based on collaborative search is proposed. After the combat begins, the surface ship and the anti-submarine helicopter start searching according to their respective search algorithms starting from the initial position of the surface ship.

4. The ship-aircraft collaborative detection method based on marine environmental influencing factors according to claim 1 is characterized in that: In the S3: A certain combat environment is set, the key parameters of the ship-aircraft collaborative search algorithm are adjusted, the changes in the search probability under the collaborative search mode are observed, and a simulation experiment is designed to obtain the relationship between the key parameters and the search probability of the ship-aircraft collaborative search algorithm under the collaborative search mode. The key parameter values ​​of the rectangular search algorithm for surface ships and the spiral search algorithm for anti-submarine helicopters in the independent search mode are compared to obtain the key parameter adjustment strategy for achieving the maximum search probability using the ship-aircraft collaborative search algorithm in the collaborative search mode.

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