A radar target information fusion method and system for multi-unmanned aerial vehicle cooperative reconnaissance

Abstract

The application discloses a radar target information fusion method and system for multi-unmanned aerial vehicle cooperative reconnaissance, comprising the following steps: S1, constructing a multi-dimensional target model based on target identification information; S2, performing collective processing on multiple mode parameters of the target; through pairwise mode comparison, same mode judgment is completed, parameter fusion is performed on the same mode, and a non-redundant mode parameter set is output; S3, single-machine target fusion; same target judgment is performed on a target queue detected by a single unmanned aerial vehicle, repeated targets are fused, and parameters are completed; S4, multi-machine target fusion; cross-platform same target judgment is performed on a target queue detected by multiple unmanned aerial vehicles, the same targets are fused, and a global target queue is generated; S5, historical target fusion; the current global target queue is compared with a historical target queue, a target ID identifier is updated, a confidence degree is calculated, and a corrected historical target queue is output. The application provides a decision basis for threat assessment, positioning, tracking and the like of the target through comprehensive and multi-dimensional acquisition of target information.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of multi-unmanned aerial vehicle cooperative reconnaissance based on electromagnetic signals, and particularly relates to a radar target information fusion method and system for multi-unmanned aerial vehicle cooperative reconnaissance. BACKGROUND

[0002] In modern warfare, the battlefield environment is increasingly complex, and the timeliness, accuracy and comprehensiveness of intelligence are extremely high. A single reconnaissance platform cannot fully and accurately obtain target information in a complex battlefield environment. Multi-aircraft cooperative reconnaissance can use multiple aircraft to detect target areas from different angles and heights, increasing the comprehensiveness of information acquisition, and through information fusion technology, the accuracy of target identification, positioning and tracking can be improved, providing a more reliable basis for combat decision-making.

[0003] The continuous progress of sensor technology provides a foundation for multi-aircraft cooperative reconnaissance information fusion. Today, various high-precision, miniaturized, low-power sensors are emerging, such as high-resolution optical cameras, infrared thermal imagers, laser radars, etc. These sensors can be carried on different aircraft to obtain rich feature information of the target. Different types of sensors have their own advantages and limitations. Through information fusion, the information of these different sensors can be utilized comprehensively, complementing their shortcomings and improving the accuracy and reliability of target reconnaissance.

[0004] The development of communication technology makes it possible for information exchange and cooperative work between multiple aircraft. High-speed, reliable wireless communication networks can ensure fast data transmission between multiple aircraft and between aircraft and ground control stations, allowing each aircraft to share the information detected in real time. At the same time, advanced communication protocols and network architectures can support the transmission of large amounts of data and have the ability to resist interference and interception, ensuring the security and stability of information transmission, providing strong communication support for multi-aircraft cooperative reconnaissance information fusion.

[0005] With the rapid development of computer technology and signal processing technology, data processing capabilities have been greatly improved. In the face of large amounts of data generated by multi-aircraft cooperative reconnaissance, high-performance processors and advanced algorithms can quickly process, analyze and fuse the data, and then through information fusion technology, the position, state and other information of the target can be accurately estimated.

[0006] In summary, through target information fusion technology, multi-source (such as different sensors, reconnaissance equipment) data can be integrated, eliminating the errors or limitations of a single information source and improving information accuracy; multi-dimensional information is complementary, covering the detection blind area of a single platform (such as multi-aircraft reconnaissance from different angles), avoiding target omission and enhancing information integrity; real-time fusion of multi-aircraft dynamic data adapts to rapid target changes (such as mobile target tracking), providing immediate support for decision-making and ensuring information timeliness. SUMMARY

[0007] In view of the problems of the prior art, the present application aims to provide a radar target information fusion method for multi-unmanned aerial vehicle cooperative reconnaissance, which can more accurately, comprehensively and multi-dimensionally obtain target information to provide decision basis for further threat assessment, positioning and tracking of the target.

[0008] To achieve the above-mentioned purposes, the present application provides a radar target information fusion method for multi-unmanned aerial vehicle cooperative reconnaissance, comprising:

[0009] S1. Constructing a multi-dimensional target model based on target identification information;

[0010] S2. Collectively processing multiple mode parameters of the target: completing same mode judgment through pairwise mode comparison, parameter fusion on the same mode, and outputting a non-redundant mode parameter set;

[0011] S3. Single-machine target fusion: performing same target judgment on a target queue detected by a single unmanned aerial vehicle, fusing repeated targets and completing parameters;

[0012] S4. Multi-machine target fusion: performing cross-platform same target judgment on a target queue detected by multiple unmanned aerial vehicles, fusing the same targets and generating a global target queue;

[0013] S5. Historical target fusion: comparing the current global target queue with a historical target queue, updating target ID identification and calculating confidence, and outputting a corrected historical target queue.

[0014] Further, in the step S1, the model parameters include target ID, DOA, pose, pulse width, carrier frequency, PRI, position, speed and threat level.

[0015] Further, the same mode judgment of the step S2 comprises:

[0016] Pulse width parameter judgment: when the pulse width modulation types are consistent, the fixed type judges whether the pulse width values are equal within the error, the agile type compares the consistency of the agile intervals, and the uneven / group variable type judges whether the agile values are equal;

[0017] Carrier frequency parameter judgment: cross judgment of the fixed type and the uneven / group variable type is added, and the following conditions are met , it is determined that the modes are the same; , the carrier frequency is taken as , the carrier frequency is uneven, , the set carrier frequency error is set;

[0018] PRI parameter judgment: set correlation rules for fixed, sliding, jitter, and uneven / group variable types respectively. Uneven / group variable type needs to detect the equality, integer multiple relationship or sum correlation between values.

[0019] Further, the pulse width parameter fusion rule is:

[0020] Fixed type: take the average of the pulse width values in the two mode parameters;

[0021] Sliding type: take the union of the minimum-maximum value interval of the pulse width value;

[0022] Uneven / group variable type: take the geometric mean of all pulse width values that are equal within the error range after merging the values. After fusion, the modulation type is dynamically converted according to the number of values.

[0023] Further, the confidence level calculation of step S5 uses:

[0024] ;

[0025] wherein, is the confidence level, is the number of times a certain target is found, is the total number of times the ith target is found, and m is the total number of targets found, represents the degree of confidence that the target pointed to is found and is a true target, is the degree of confidence that a certain target among all discovered targets is found and is a true target.

[0026] Further, the same target judgment rule is:

[0027] If there is at least one same mode parameter between two targets, it is determined as the same target. When fused, the latest spatio-temporal data is preferentially retained, and the conflicting parameters are taken as weighted average values.

[0028] Further, the multi-machine target fusion of step S4 includes:

[0029] The known target queue and the unknown target queue are processed in layers. Unknown targets are associated across platforms through mode parameter similarity matching.

[0030] Further, when constructing the target model:

[0031] The parameter dimension is dynamically expanded according to the complexity of the electromagnetic environment, and signal modulation characteristic parameters, polarization modes, and pulse accumulation numbers are added.

[0032] Further, it further includes:

[0033] In a complex electromagnetic environment, an anti-interference mode is started. When communication is interrupted, local fusion is performed based on the historical target queue, and after communication is restored, the difference data is synchronized.

[0034] A radar target information fusion system for multi-UAV cooperative reconnaissance, comprising:

[0035] A modeling module: for constructing a multi-dimensional radar target model, dynamically configuring parameter dimensions;

[0036] An aggregation processing module: performing same-mode judgment and fusion of mode parameters, and built-in special fusion operators for pulse width, carrier frequency and PRI;

[0037] A fusion engine: containing a single-machine fusion subunit, a multi-machine fusion subunit and a historical fusion subunit, and performing target deduplication and parameter completion in layers;

[0038] A confidence calculation module: calculating dynamic confidence based on target occurrence frequency;

[0039] A communication adapter: supporting network interruption and data difference synchronization;

[0040] An anti-interference module: enabling inertial navigation data assisted positioning in GPS denial environment.

[0041] The beneficial effects of the present application are as follows:

[0042] The present application can effectively integrate information according to the target recognition result of electromagnetic signals, can solve the problem of data redundancy, can enhance the accuracy and reliability of the recognized target parameters, can improve the target recognition capability, and can quickly characterize the target in multiple dimensions, providing a basis for further task decision-making. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 A flowchart of the target information fusion method for multi-UAV cooperative reconnaissance according to the embodiment of the present application;

[0044] Figure 2 A framework diagram of target mode parameter aggregation in step two according to the embodiment of the present application;

[0045] Figure 3 A single-machine target fusion framework diagram in step three according to the embodiment of the present application;

[0046] Figure 4 A two-machine target fusion framework diagram in step four according to the embodiment of the present application;

[0047] Figure 5 A historical target fusion framework diagram in step five according to the embodiment of the present application;

[0048] Figure 6 A target fusion framework diagram for two-machine cooperative reconnaissance according to the embodiment of the present application;

[0049] Figure 7 This is a diagram illustrating the pulse width parameter judgment rule for step two of the present invention, based on an embodiment of the present invention.

[0050] Figure 8 This is a diagram illustrating the carrier frequency parameter determination rule for step two of the same-mode parameter determination according to an embodiment of the present invention.

[0051] Figure 9 This is a diagram illustrating the PRI parameter judgment rule for step two of the present invention, based on an embodiment of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0054] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0055] The following combination Figures 1-9 Specific embodiments of the present invention will be described in detail below. It should be understood that the specific embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the present invention.

[0056] In view of the complex electromagnetic environment scene of multi-machine cooperative combat, in order to make the reconnaissance result more accurate and effective, and obtain more comprehensive and complete information, the improvement of information fusion technology becomes an urgent demand. Based on this, the radar target information fusion method and system for multi-unmanned aerial vehicle cooperative reconnaissance, in view of the multi-machine cooperative reconnaissance scene, considers target multi-dimensional data information fusion, models the target in multiple dimensions, completes the target mode parameter set for the single reconnaissance platform data acquisition time batch processing caused by the same target parameter mode increment, and completes the same target merging information fusion processing for the target increment, and fuses the target information detected by the multi-machine reconnaissance platform, so that the real-time reconnaissance target information is more comprehensive and accurate. Finally, the cumulative multi-reconnaissance result forms a historical target queue, and each time the target queue is fused with the historical target queue. Compared with the historical target, the identification ID of each target and the fused target obtained by the multi-machine reconnaissance is given, the target can be distinguished as a newly discovered target or a persistent target according to the ID, and the identification execution degree of the true target can be improved to a certain extent in the process of continuously fusing the historical target queue, and the target can be more accurately identified.

[0057] As shown in Figure 1 for the multi-target scene in the complex electromagnetic environment, the target information fusion is completed for the multi-target reconnaissance information data, the target information fusion method for multi-unmanned aerial vehicle reconnaissance, the method implements the following steps:

[0058] S1. Constructing a target model based on target identification information. First, the target information needs to be parameterized designed, and the target needs to be modeled. The purpose is to fuse the multi-dimensional data of the target, so the multi-dimensional data modeling of the target is needed;

[0059] S2. Target mode parameter set. For the multi-functional radar target, the multi-mode parameter condition is ensured to realize the minimum informationization description of the identified target mode parameter, that is, to realize the target mode parameter set;

[0060] S3. Single machine target fusion. The targets detected by a single unmanned aerial vehicle are fused and processed to complete the information fusion of the same target, and it is ensured that each target in the target queue detected by it has uniqueness, that is, no repeated target;

[0061] S4. Multi-machine target fusion. The targets detected by multiple unmanned aerial vehicles complete information fusion. The same target detected by multiple machines completes data information fusion, and all different targets form a single reconnaissance target queue output;

[0062] S5. Historical target fusion. For the multi-reconnaissance target result, a historical target queue is formed, and the same target information fusion needs to be realized, different targets form a queue merging output, and then the target information fusion of the multi-reconnaissance target queue is completed.

[0063] The embodiment is directed to two-machine cooperative reconnaissance, and target information fusion is performed on real-time reconnaissance results of the two machines, as shown in the following figure. Figure 6 The figure is a target fusion framework diagram of two-machine cooperative reconnaissance of the embodiment.

[0064] First, in step S1, multi-dimensional modeling is performed on the identified targets for two-machine reconnaissance. Considering the target state recognition, positioning, tracking and other requirements after target information fusion, the target parameters can be designed according to the test requirements, so that the target parameter design can cover the reconnaissance task requirements.

[0065] In step S1, the target model is constructed based on the target recognition information. According to the test requirements in the multi-machine cooperative reconnaissance scene, a radiation source or a multi-functional radar target model is designed to realize parameterized description of the target. The parameter design needs to consider the purpose of target recognition, so as to take further countermeasures against the identified target. For example, the design target parameters include target ID (for distinguishing targets), target real-time DOA and aircraft pose (for positioning), mode parameters include pulse width, carrier frequency, PRI and other parameter information (for state recognition), target position, speed, etc. (for positioning results, target tracking, etc.), threat level (for threat level results of multi-parameter evaluation), etc.

[0066] Then, in step S2, the target parameter set of all targets detected by the two machines is set. In a complex electromagnetic signal environment, the model parameters of a multi-functional radar target are relatively complex, so the target mode parameter set is designed to realize accurate description of the target without losing target information and to complete de-redundancy of the identification information. For example, as shown in the following figure, it is a target mode parameter set framework diagram. Figure 2 The principle is to de-replicate multiple mode parameters contained in a certain target model and fuse the same mode parameter information. This step first needs to determine whether there is the same mode in the multiple modes of the target, mainly by comparing two mode parameters to determine the same mode. The method for determining the same two mode parameters is as follows:

[0067] According to the parameter design of the multi-dimensional modeling of the target in step S1, the related parameters of the two modes are obtained, mainly including pulse width, carrier frequency, PRI modulation parameters and state information. The pulse width, carrier frequency and PRI modulation parameters need to be judged from their modulation types and parameter values respectively.

[0068] 1. Whether the pulse width parameters of the two modes are the same, the judgment rule is as shown in the following figure. Figure 7 The figure is a pulse width parameter judgment rule for the same mode parameter judgment. Figure 7

[0069] ​If the two modes are of the same type of pulse width modulation, it is determined whether the pulse width values are equal or the change intervals are consistent. If so, the two pulse width parameters are considered the same, otherwise they are considered different. If the modulation types are different, they are considered different.

[0070] wherein the fixed, variable, uneven or group variable refers to the modulation type of the pulse width, the fixed type refers to a fixed pulse width value, the variable type refers to a random variable pulse width within a certain range, the uneven type refers to a random variable pulse width between several values, and the group variable type refers to a pulse width that appears in the form of several values in the same group.

[0071] (1) When the pulse width modulation types of the two modes are fixed, it is determined whether the pulse width values of the two modes are equal within an error. The equal condition is as follows:

[0072] ;

[0073] wherein , are the pulse width values corresponding to the two modes, is the set error.

[0074] (2) When the pulse width modulation types of the two modes are variable, it is determined whether the pulse width variable intervals of the two modes are consistent. The consistent condition is as follows:

[0075]

[0076] wherein , , , are the minimum and maximum pulse width variable values corresponding to the two modes, is the set pulse width variable error.

[0077] (3) When the pulse width modulation types of the two modes are uneven or group variable, it is determined whether the variable values of the two modes are equal. The condition is as follows:

[0078]

[0079] wherein , are the pulse width uneven or group variable values corresponding to the two modes, , is the number of pulse width values in mode 1, , is the number of pulse width values in mode 2, is the set pulse width error.

[0080] 2. Whether the carrier frequency parameters of the two modes are the same, the judgment rule is as follows:Figure 8 as shown, Figure 8 is a carrier frequency parameter judgment rule graph for same mode parameter judgment.

[0081] Wherein, fixed, sliding, quick change, uneven or group change refers to the modulation type of carrier frequency, the fixed type is that the carrier frequency value is fixed, the sliding type is that the carrier frequency value appears in the sliding interval, the quick change type is that the carrier frequency randomly changes in a certain range, the uneven type refers to the carrier frequency randomly changes between several values, and the group change type refers to the carrier frequency changes in the form of several values in the same group.

[0082] For example, Figure 8 The rule is that if the modulation types of the carrier frequencies of the two modes are consistent, it is judged whether there is an equal condition for the carrier frequency value, or the sliding interval and the interval are consistent when sliding, or the interval is consistent when quick changing, then it is considered that the two pulse width parameters are the same, otherwise it is considered different; If the modulation types of the carrier frequencies of the two modes are inconsistent, it is judged whether they belong to one of fixed, uneven or group change, if they belong to one of them, it is judged whether there is an equal condition for the carrier frequency value, if there is, it is considered that the two modes are the same, if not, they are different. If none of them is different.

[0083] (1) When the modulation types of the carrier frequencies of the two modes are fixed, it is judged whether the carrier frequency values of the two modes are equal within the error, and the equal judgment condition is as follows:

[0084] ;

[0085] Wherein, , are the carrier frequency values corresponding to the two modes respectively, is the set carrier frequency error.

[0086] (2) When the modulation types of the carrier frequencies of the two modes are sliding, it is judged whether the sliding intervals of the two modes are equal and the sliding intervals are consistent, and the equal judgment condition of the sliding interval is as follows:

[0087] ;

[0088] Wherein, , are the sliding intervals of the carrier frequencies of the two modes respectively, is the set carrier frequency error.

[0089] The judgment condition of the consistent sliding interval is as follows:

[0090] ;

[0091] Or: ;

[0092] Wherein, , , , are the minimum and maximum values of the carrier frequency ramping corresponding to the two modes respectively, is the set carrier frequency ramping or steering error.

[0093] (3) When the carrier frequency modulation type of the two modes is steering type, the consistent judgment condition of the steering interval is the same as that of the ramping interval.

[0094] (4) When the carrier frequency modulation type of the two modes is staggered or group variable type, it is necessary to judge whether the steering values of the two modes are equal, and the judgment condition is as follows:

[0095] ;

[0096] Among them, , is the carrier frequency staggered or group variable value corresponding to the two modes, , is the number of carrier frequency values in mode 1, , is the number of carrier frequency values in mode 2, is the set carrier frequency error.

[0097] When the scene signal aliasing is complex, there are many clutter signals, and the amount of collected data is not enough, etc. The identified carrier frequency value may appear one (fixed modulation) or multiple (staggered or group variable modulation). At this time, it is necessary to consider the judgment and fusion of the fixed and staggered or group variable carrier frequency values. If the fixed carrier frequency value of one mode is the same as the staggered or group variable value of another mode, it is reasonable to identify the error. The result will be the same mode. The condition is:

[0098] ;

[0099] Among them, is the carrier frequency value of the fixed modulation, is the carrier frequency value of the carrier frequency staggered or group variable modulation, , is the number of corresponding carrier frequency values, is the set carrier frequency error.

[0100] 3. Whether the PRI parameters of the two modes are the same, as shown in Figure 9 PRI parameter judgment rule diagram for same mode parameter judgment.

[0101] Wherein, the modulation type of fixed, sliding, jitter, uneven or group variable PRI, the fixed type is that the PRI value is fixed, the sliding type is that the PRI value slides in the interval according to the sliding interval, the jitter type is that the PRI randomly jitters in the interval above and below a certain value, the uneven type means that the PRI randomly appears between several values, and the group variable type means that the PRI appears in the form of several values in the same group.

[0102] If the PRI modulation types of the two modes are consistent, whether the parameters exist the same condition is judged according to the different modulation types, as follows:

[0103] (1) Fixed type, whether the PRI value exists the equal or multiple correlation condition is judged, the multiple correlation is that the PRI value of one mode is an integer multiple of the PRI value of the other mode, the multiple threshold cannot be too large, if it exists, it is considered the same. The judgment condition is as follows:

[0104] ;

[0105] Wherein, is the larger value of the PRI in the two modes, is the smaller value of the PRI in the two modes, Rn is / the multiple value after rounding, and Rn cannot exceed the set threshold, which can be set according to the actual collected data aliasing condition.

[0106] (2) Sliding type, whether the sliding interval and the interval are consistent in the error range is judged, if they are consistent, it is considered the same. The judgment condition is as follows:

[0107] ;

[0108] Wherein, , are the sliding intervals corresponding to the PRI of the two modes, is the set error.

[0109] The judgment condition of the consistent sliding interval is as follows:

[0110] ;

[0111] Wherein, , , , are the minimum and maximum values of the PRI sliding of the two modes, is the set PRI sliding or jitter error.

[0112] (3) Jitter type: Determine whether the jitter interval is consistent within the error range. If it is consistent, it is considered the same. The judgment condition is the same as the judgment condition for consistent slip interval.

[0113] (4) For uneven or grouped variation types, determine whether the PRI values ​​of the two modes are equal, have multiple correlations, or have additive correlations. If so, they are considered the same. First, merge all PRI values ​​of the two modes and sort them to obtain... , , This represents the total number of PRI values ​​after merging and sorting.

[0114] The conditions for determining equality are as follows:

[0115] ;

[0116] in, , These are any two PRI values ​​from all PRI values ​​after merging and sorting. The PRI error is set.

[0117] The conditions for determining multiple association are as follows:

[0118] ;

[0119] in, , Let be any two PRI values ​​from all PRI values ​​after merging and sorting, and For larger values, For smaller values, for / Multiples of the value after rounding, and need to The threshold value must not be exceeded. The threshold value can be set according to the actual aliasing situation of the collected data. The PRI error is set.

[0120] The conditions for determining additive association are as follows:

[0121] ;

[0122] in, The sum of multiple PRI values. To determine the number of PRI values ​​to be summed, a threshold is usually set, not exceeding 3. In most cases, the sum of 2 or 3 measured values ​​is the true PRI value. This is another PRI value, different from the sum of several values. The PRI error is set.

[0123] In the case of the same modulation type, if the PRI parameters are not associated with each other, it is considered that the PRI parameters are different.

[0124] In a complex scenario, in the case of analog carrier frequency parameter identification, considering the PRI identification error, the identification result of the uneven modulation or group variable modulation may be identified as fixed, or the PRI value of the uneven or group variable modulation is incomplete, and the same mode parameters are identified as fixed (uneven or group variable modulation). At this time, the fixed and uneven or group variable PRI values are considered to be fused. If the fixed PRI value of one mode is the same as the uneven or group variable value of another mode, the same mode is determined. That is, in the case of different modulation types, it is determined whether the modulation types of the two modes belong to one of fixed, uneven or group variable. If it belongs, it is determined whether there is PRI value equal, multiple association or sum association. If there is, it is considered that the two modes are the same, and if there is not, it is different. If none of the above conditions is met, it is considered to be different. The PRI value equal, multiple association and sum association condition is the same as the condition for determining whether the two modes are uneven or group variable.

[0125] It is directly determined whether the identification states of the two target modes are consistent.

[0126] If the pulse width, carrier frequency, PRI parameter and state of the two modes are consistent, it is considered that the parameters of the two modes are the same, and the parameters of the two modes are fused. The fusion method is as follows:

[0127] (1) Pulse width parameter fusion, different modulation types have different fusion methods, as follows:

[0128] Fixed type: take the average of the pulse width values in the two mode parameters;

[0129] Chang type: the pulse width values of the mode parameters are the maximum and minimum values, the minimum value of the fused pulse width is the smaller of the two minimum values, and the maximum value of the pulse width is the larger of the two maximum values.

[0130] Uneven type: merge the pulse width values of the two modes, and then take the geometric mean of all pulse width values within the error range.

[0131] Group variable type: merge the pulse width values of the two modes, and then take the geometric mean of all pulse width values within the error range.

[0132] For the fusion of uneven or group variable, it is determined whether the number of pulse width values of the fused chang is equal to 1. If yes, the pulse width modulation type after fusion is set to fixed, and the value is the only pulse width value after fusion. Otherwise, according to the merging condition, the fusion of fixed and uneven takes uneven, the fusion of fixed and group variable takes group variable, and the fusion of uneven and group variable takes uneven.

[0133] (2) Carrier frequency parameter fusion: The fusion method varies for different modulation types, as detailed below:

[0134] Fixed type: Take the average of the carrier frequency values ​​from the two mode parameters;

[0135] Slip type: The mode parameters are the slip interval and the maximum and minimum values. The fused slip interval is the average of the slip intervals of the two mode parameters. The fused minimum carrier frequency is the smaller of the two minimum values. The maximum carrier frequency is the larger of the two maximum values.

[0136] Agile type: The mode parameter values ​​are the maximum and minimum values ​​of the carrier frequency. The minimum value of the fused carrier frequency is the smaller of the two minimum values, and the maximum value of the carrier frequency is the larger of the two maximum values.

[0137] Staggered type: Combine all carrier frequency values ​​of the two modes, and then take the geometric mean of all carrier frequency values ​​that are equal within the error range.

[0138] Group variation type: Combine all carrier frequency values ​​of the two modes, and then take the geometric mean of all carrier frequency values ​​that are equal within the error range.

[0139] When the carrier frequency modulation types are inconsistent, all carrier frequency values ​​of the two modes are merged and sorted, and then the geometric mean is calculated for all carrier frequency values ​​that are equal within the error range.

[0140] After merging, determine if the number of carrier frequency values ​​of the agile transformer is equal to 1. If so, set the carrier frequency modulation type of the merged transformer to fixed, and take the unique carrier frequency value after merging. Otherwise, depending on the merging situation, if fixed and staggered transformers are merged, take the staggered value; if fixed and group transformers are merged, take the group transformer value; if staggered transformers are merged, take the staggered value.

[0141] (3) PRI parameter fusion: The fusion method varies for different modulation types, as detailed below:

[0142] Fixed type: Take the geometric mean of the larger and smaller PRI values ​​of the two mode parameters, i.e.

[0143] ;

[0144] in, The larger of the two modes is PRI. The smaller of the two modes is PRI. for / The multiples of the value after rounding.

[0145] Slip type: mode parameters take the slip interval and maximum and minimum values, the fused slip interval takes the average of the slip intervals of the two mode parameters, the fused PRI minimum value takes the smaller of the two minimum values, and the PRI maximum value takes the larger of the two maximum values.

[0146] Jitter type: mode parameters take the PRI value, PRI maximum and minimum value, the fused jitter PRI value takes the average of the PRI values of the two mode parameters, the PRI minimum value takes the smaller of the two minimum values, and the PRI maximum value takes the larger of the two maximum values.

[0147] Stagger type: all PRI values of the two modes are merged, and fusion is performed according to numerical equality, multiple correlation, and sum correlation in turn;

[0148] (1) Numerical equality fusion: geometric mean value is obtained for multiple PRI values that are equal within the PRI error range;

[0149] (2) Multiple correlation fusion: the PRI values after equal fusion are arranged in ascending order, and it is judged whether the other PRI values greater than a certain PRI value are integer multiples of the certain PRI value, which needs to meet the upper limit of the multiple correlation and the integer multiple proportion error, and the geometric mean value is taken as the fused RPI, that is, multiple PRI addition / multiple addition;

[0150] (3) Sum correlation fusion: the PRI values after multiple correlation fusion are arranged in ascending order, and it is judged whether the sum of a plurality of values (here, the plurality cannot exceed the upper limit of the sum correlation) is equal to a certain larger value within the error range, and if so, the geometric mean value is taken as the fused PRI, that is, (sum of multiple values + larger value) / 2.

[0151] Group variable type: the fusion method is the same as stagger.

[0152] In the case where the PRI modulation types are inconsistent, all the PRI values of the two modes are merged and sorted in ascending order, and then the fusion method of stagger is used to complete the PRI parameter fusion;

[0153] After fusion, it is judged whether the number of variable PRI values is equal to 1, if so, the fused PRI modulation type is set to fixed, and the value is the only fused PRI value. Otherwise, according to the merging condition, fixed and stagger fusion take stagger, fixed and group variable fusion take group variable, and stagger and group variable take stagger.

[0154] Based on the above two mode judgment and fusion methods, the mode parameter set of all target mode parameters is set, the modes belonging to the same mode parameter are fused, and are merged into a single mode output, so that there is no repeated mode in the final output of each target mode parameter, and the information is more complete.

[0155] The single machine target fusion in step S3 fuses the targets detected by a single unmanned aerial vehicle, ensures that each target in the target queue detected by the unmanned aerial vehicle is unique, that is, there is no repeated target; this case is mainly for repeatedly detecting and identifying targets according to the signal data collection rhythm within a single detection time, which may lead to multiple identification of the same target, and the information covered by the same target in different identifications may be different. Through single machine target fusion, the same target detection information is merged and completed, the target information detected by the single machine is ensured to be more detailed and complete, and each target in the output target queue has uniqueness.

[0156] As shown in Figure 3 , it is a single machine target fusion framework diagram. The single machine target queue fusion mainly fuses the target information detected by a single task machine, fuses the target parameters belonging to the same target, and merges them into a single target output. This step involves the same target judgment and the same target parameter fusion. The judgment method of two targets as the same target is as follows: obtain all mode parameters of two targets, then traverse, if a certain mode parameter of target 1 and a certain mode parameter of target 2 belong to the same mode, then the two targets are considered as the same target, and the target parameter fusion of the two targets is performed.

[0157] The target parameter fusion method of two targets as the same target is as follows:

[0158] (1) Obtain all mode parameters of two targets, take target 1 and target 2;

[0159] (2) Traverse all mode parameters of target 1, take each mode parameter of target 2 and perform the same mode parameter judgment, if it is the same mode parameter, then perform the same mode parameter fusion, and update the mode of target 1 to the fused mode parameter. If it is not the same as all modes of target 1, then the mode parameter of target 2 is supplemented to the mode parameters of target 1; until the traversal of all parameters of target 1 is completed;

[0160] (3) Finally, target 1 after the traversal mode parameter fusion is the target after the fusion of the two targets.

[0161] The multi-machine target fusion in step S4 aims to complete the same target information fusion of the target queues detected by multiple unmanned aerial vehicles, and merge different targets into the same queue, that is, to realize the information merging and target summarizing operation of the targets detected by the unmanned aerial vehicles.

[0162] As shown in Figure 4 , it is a two-machine target fusion framework diagram. The two-machine target queue information fusion mainly realizes the same target judgment of all targets detected by two task machines, merges the same target data information, covers all different target data information, and forms a fused target queue.

[0163] The two-aircraft target fusion method is as follows:

[0164] Fusion is performed separately for known and unknown targets. First, known targets are retrieved from the target queues of both aircraft. The uniqueness of the target ID within the known target queue is used to determine if they are the same target; if so, the same-target information fusion is completed. The fusion method is the same as that used in single-aircraft target fusion. Then, unknown targets are retrieved from the target queues of both aircraft. Iteration is performed to determine if any two targets detected by the two aircraft are the same target; if so, the same-target information fusion is completed. The same-target determination and fusion method is the same as that used in single-aircraft target queue fusion.

[0165] Furthermore, when fusing the DOA and aircraft pose of two aircraft targeting the same target, the most recent aircraft DOA and pose are used as the fused information based on the real-time nature of the target discovery time.

[0166] The historical target fusion described in step S5 is to perform target fusion operation on the multi-aircraft target fusion results obtained from multiple reconnaissances. That is, each time the reconnaissance is completed, the multi-aircraft fused target is merged with the historical target queue. By merging with the historical target queue, the uniqueness of the target is distinguished, and the individual identifier and target ID of the reconnaissance target in the multi-aircraft target queue are assigned. The target ID identifier of the target queue obtained by a single aircraft is also assigned. At the same time, the historical target queue is updated.

[0167] like Figure 5 The diagram shows the framework for historical target fusion. Historical target queue information fusion primarily involves fusing the multi-aircraft fusion target queue detected each time with all previously detected target queues, assigning an individual identifier (target ID or radar ID) to each target detected in the current reconnaissance. Each historical target queue information fusion update updates the historical target queue. Furthermore, during continuous reconnaissance, it is necessary to determine the true targets from the reconnaissance results and correct target parameters. Historical target fusion compares the current reconnaissance target with historical targets. Based on the comparison results, a unique target identifier can be assigned to each target, and parameters can be corrected during the fusion process for the same target, thus identifying targets as accurately as possible. A target identification confidence level is proposed based on the frequency of target detection. The confidence level is calculated as follows:

[0168]

[0169] in, For confidence level, The number of times a target has been discovered. This represents the total number of times all targets were detected. This confidence calculation method is suitable for scenarios with a small number of targets and low electromagnetic environment complexity.

[0170] Or improve the degree of execution of the target frequently found, then it can be calculated in another way, the formula is as follows:

[0171] ;

[0172] Wherein, The confidence degree, The number of times a certain target is found, The total number of times the ith target is found, and m is the total number of found targets. Indicates the degree of confidence that the target pointed to is found and is a true target, The confidence degree that a certain target is found and is a true target among all found targets. This confidence calculation method is suitable for the case where the number of targets in the scene is large, and there are many interference targets, the electromagnetic environment is complex, and it is difficult to distinguish true targets and interference targets. Through this way of calculating confidence to assist the decision-making after target recognition, in general, the confidence is also calculated by selecting this calculation method to improve the confidence of frequently found targets.

[0173] In this way, with the increase of the number of reconnaissance times, the confidence degree of a certain true target will be higher and higher, and then the true target can be distinguished.

[0174] The same target information fusion in steps S3, S4 and S5 needs to complete the information fusion of the same target multi-mode parameters, and the signal modulation type is considered. The same mode parameter fusion is performed for the mode parameters with the same modulation type and the same parameters, and the different mode parameters are output in the mode set parameter queue.

[0175] The application also provides a radar target information fusion system for multi-unmanned aerial vehicle cooperative reconnaissance, comprising:

[0176] The modeling module is used for constructing a multi-dimensional radar target model and dynamically configuring parameter dimensions.

[0177] The set processing module is used for performing same mode judgment and fusion of mode parameters, and the special fusion operators of pulse width, carrier frequency and PRI are built-in.

[0178] The fusion engine comprises a single machine fusion subunit, a multi-machine fusion subunit and a historical fusion subunit, and performs target deduplication and parameter completion in layers.

[0179] The confidence calculation module is used for calculating dynamic confidence based on target appearance frequency.

[0180] The communication adapter supports network interruption and data difference synchronization.

[0181] The anti-interference module is used for enabling inertial navigation data assisted positioning in GPS denial environment.

[0182] Any procedural or methodological descriptions in flowcharts of the present application or otherwise described herein can be understood as representing modules, segments or portions of code that include executable instructions for implementing the specific logic functions or steps, which can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus or device, which can be any of a variety of electronic devices including a read-only memory, a magnetic or optical disk, or the like.

[0183] In the description of the present specification, the description referring to the terms "embodiment", "example", and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms are not necessarily directed to the same embodiment or example. In addition, a person skilled in the art can combine or combine the different embodiments or examples described in the present specification and the features thereof without producing a contradiction.

[0184] Although the above has shown and described the embodiments of the present application, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application, and a person skilled in the art can make changes, modifications, replacements and modifications of the above embodiments within the scope of the present application.

Claims

1. A radar target information fusion method for multi-UAV collaborative reconnaissance, characterized in that, The method includes: S1. Construct a multi-dimensional target model based on target recognition information; S2. Collectively process multiple mode parameters of the target: determine the same mode by comparing each pair of modes, fuse the parameters of the same mode, and output a set of mode parameters without redundancy. S3. Single-UAV Target Fusion: Perform target matching on a single UAV reconnaissance target queue, fuse duplicate targets and complete parameters; S4. Multi-UAV Target Fusion: Perform cross-platform target identification on target queues from multiple UAV reconnaissance missions, merge identical targets, and generate a global target queue; S5. Historical Target Fusion: Compare the current global target queue with the historical target queue, update the target ID, calculate the confidence level, and output the corrected historical target queue; The parameter fusion in step S2 varies depending on the modulation type, as detailed below: Fixed type: Take the geometric mean of the larger and smaller PRI values ​​of the two mode parameters, i.e. ； in, The larger of the two modes is PRI. The smaller of the two modes is PRI. for / Multiples of the value after rounding; Slip type: The model parameters are the slip interval and the maximum and minimum values. The fused slip interval is the average of the slip intervals of the two model parameters. The fused minimum PRI value is the smaller of the two minimum values. The maximum PRI value is the larger of the two maximum values. Jitter type: The mode parameter is taken as PRI value, maximum and minimum PRI value. The jitter PRI value after fusion is the average of the PRI values ​​of the two mode parameters. The minimum PRI value is the smaller of the two minimum values. The maximum PRI value is the larger of the two maximum values. Staggered types: All PRI values ​​from both modes are merged, successively based on numerical equality, multiple correlation, and additive correlation, including: (1) Numerical equality fusion: calculate the geometric mean of all PRI values ​​that are equal within the PRI error range; (2) Multiple association fusion: Arrange the PRI values ​​after fusion in ascending order, and iterate to determine whether other PRI values ​​greater than a certain PRI value are integer multiples of it. It is necessary to satisfy the condition that within the maximum multiple limit of multiple association and within the proportional error of integer multiples, take the geometric mean as the fused PRI, that is, the sum of multiple PRI / multiples; (3) Addition and correlation fusion: Arrange the PRI values ​​after multiple correlation fusion in ascending order, and determine whether the sum of multiple values ​​is equal to a larger value within the error range. If they are equal, take their geometric mean as the fused PRI. Group variation type: All PRI values ​​of the two modes are merged, and fusion is performed in turn according to numerical equality, multiple correlation, and summation correlation. The geometric mean is calculated for all carrier frequencies that are equal within the error range. When the modulation types are inconsistent, all PRIs of the two modes are merged and sorted in ascending order, and then the PRI parameters are fused using the same fusion method. After fusion, determine whether the number of PRI values ​​of the agile transformer is equal to 1. If yes, set the PRI modulation type of the fused transformer to fixed and take the unique PRI value after fusion. If no, depending on the fusion situation, take the non-non ...

2. The radar target information fusion method for multi-UAV cooperative reconnaissance according to claim 1, characterized in that, In step S1, the model parameters include target ID, DOA, pose, pulse width, carrier frequency, PRI, position, velocity, and threat level.

3. The radar target information fusion method for multi-UAV cooperative reconnaissance according to claim 1, characterized in that, The same pattern determination in step S2 includes: Pulse width parameter judgment: When the pulse width modulation type is consistent, the fixed type judges whether the pulse width value is equal within the error range, the agile type compares the consistency of the agile interval, and the staggered / group variable type judges whether the agile value is equal. Carrier frequency parameter judgment: Add cross-judgment of fixed type and staggered / grouped type to meet the requirements. When the time comes, it is determined to be the same pattern; For the carrier frequency value, Due to carrier frequency variation, The carrier frequency error is set. PRI parameter judgment: Set association rules for fixed, slip, jitter, and staggered / grouped types respectively. For staggered / grouped types, it is necessary to detect the equality, integer multiple relationship or summation relationship between values.

4. The radar target information fusion method for multi-UAV cooperative reconnaissance according to claim 3, characterized in that, The pulse width parameter fusion rule is as follows: Fixed type: Take the average of the pulse width values ​​from the two mode parameters; Agile type: Take the union of the minimum and maximum pulse width values; Staggered / Group Variation Type: After merging the values, take the geometric mean of all pulse width values ​​that are equal within the error range, and dynamically change the modulation type according to the number of values ​​after fusion.

5. The radar target information fusion method for multi-UAV cooperative reconnaissance according to claim 1, characterized in that, The confidence level calculation in step S5 uses the following method: ； in, For confidence level, The number of times a target has been discovered. For the first The total number of times each target was discovered. The total number of targets discovered. This indicates the degree of confidence that the target being calculated has been discovered and is a genuine target. The degree of confidence that a particular target has been found and is a true target among all the discovered targets.

6. The radar target information fusion method for multi-UAV cooperative reconnaissance according to claim 1, characterized in that, The same target determination rule is as follows: Two targets are considered to be the same target if they have at least one identical mode parameter. During fusion, the latest spatiotemporal data is retained first, and the conflicting parameters are weighted averaged.

7. The radar target information fusion method for multi-UAV cooperative reconnaissance according to claim 1, characterized in that, The multi-machine target fusion in step S4 includes: The system handles known target queues and unknown target queues in a hierarchical manner, and unknown targets are associated across platforms through pattern parameter similarity matching.

8. The radar target information fusion method for multi-UAV cooperative reconnaissance according to claim 1, characterized in that, When the target model is constructed: The parameter dimensions are dynamically expanded based on the complexity of the electromagnetic environment, adding signal modulation characteristic parameters, polarization mode, and pulse accumulation number.

9. The radar target information fusion method for multi-UAV cooperative reconnaissance according to any one of claims 1-8, characterized in that, The method initiates an anti-interference mode in complex electromagnetic environments, performs local fusion based on historical target queues when communication is interrupted, and synchronizes the difference data after communication is restored.

10. A radar target information fusion system for multi-UAV collaborative reconnaissance, used to implement the radar target information fusion method for multi-UAV collaborative reconnaissance according to any one of claims 1-8, characterized in that, The system includes: Modeling module: Used to build multi-dimensional radar target models and dynamically configure parameter dimensions; The aggregation processing module performs same-mode judgment and fusion of execution mode parameters, and has built-in dedicated fusion operators for pulse width, carrier frequency, and PRI. Fusion Engine: Includes single-machine fusion subunit, multi-machine fusion subunit, and historical fusion subunit, performing target deduplication and parameter completion in a layered manner; Confidence calculation module: Calculates dynamic confidence based on the frequency of target occurrence; Communication adapter: Supports resume transmission after network outage and data differential synchronization; Anti-interference module: Enables inertial navigation data-assisted positioning in GPS-denied environments.

Images