Multi-radar resource scheduling method for cluster target detection

Abstract

The invention provides a multi-radar resource scheduling method for cluster target detection, and the method is used for carrying out resource scheduling on a radar network, and the radar network comprises a plurality of radars. The method comprises the steps that detection information of each radar is acquired, and for any radar, the detection information comprises track data of a target tracked by the radar and the working state of the radar; for any radar, based on the detection information of the radar, the detection income of the radar to each tracked target is determined; based on the detection income of each radar to each target tracked by the radar net, an objective function of the total detection income of the radar net is constructed, and for any radar, the detection income of the radar to untracked targets is 0; solving the target function by taking the maximization of the total detection income as a target to obtain a target distribution scheme; and issuing a resource scheduling instruction to each radar in the radar network according to the target allocation scheme. According to the scheme, the resource utilization efficiency of the radar network and the capability of coping with the cluster target can be greatly improved.

Description

Technical Field

[0001] This invention relates to the field of radar control technology, and more specifically to a multi-radar resource scheduling method for cluster target detection. Background Technology

[0002] In the field of target reconnaissance, targets are increasingly exhibiting characteristics of high density, high speed, and multi-batch clustering, such as drone swarm attacks. Traditional single-unit phased array radars are limited by their own transmit power, beam dwell time, and data processing capabilities, leading to rapid saturation of detection and tracking resources when dealing with such clustered targets. Specifically, this manifests as: decreased data rate: the radar needs to allocate tracking beams to more targets, resulting in longer data update cycles for individual targets, and decreased tracking accuracy and continuity; track loss: when the number of targets exceeds the radar's maximum capacity, newly emerging targets cannot be batched and tracked in a timely manner, while already tracked targets may be lost due to resource preemption; detection blind spots: to maintain tracking of high-risk targets, the radar has to abandon searches of certain airspace, creating defensive vulnerabilities.

[0003] To address the resource limitations of single radars, multi-radar networked collaborative detection has become a significant development trend. However, most existing network systems remain at the information fusion level, where each radar operates independently, sending its detection results to a central node for fusion processing. In this model, the lack of coordinated scheduling among radars easily leads to resource waste, such as multiple radars simultaneously tracking the same target while other targets are left untracked. Especially within the shared coverage area of ​​networked radars, how to achieve efficient "division of labor and cooperation," avoid redundant detection, and maximize the overall utilization efficiency of detection resources is a pressing technical challenge in the current radar network technology field.

[0004] In view of this, the present invention is hereby proposed. Summary of the Invention

[0005] The present invention is proposed in view of the above-mentioned problems. According to one aspect of the present invention, a multi-radar resource scheduling method for cluster target detection is provided, for scheduling resources of a radar network, the radar network comprising multiple radars; the method includes: Acquire the detection information of each of the radars, wherein for any radar, the detection information includes the trajectory data of each target tracked by the radar and the radar's operating status; For any of the radars, based on the radar's detection information, the detection gain of the radar for each tracked target is determined; Based on the detection gain of each radar for each target tracked by the radar network, an objective function for the total detection gain of the radar network is constructed, wherein for any radar, the detection gain of the radar for untracked targets is 0. With the goal of maximizing total detection gains, the objective function is solved to obtain the objective allocation scheme; According to the target allocation scheme, resource scheduling instructions are issued to each radar in the radar network.

[0006] For example, determining the detection gain of the radar for each tracked target based on the radar's detection information includes: For any target tracked by this radar, The threat level of a target should be determined based at least on its speed, heading, and radar cross-section. Based on the radar's signal-to-noise ratio, tracking accuracy, and data rate for the target, the tracking quality of the radar for the target is determined; The resource consumption of the radar in tracking the target is determined by the ratio of the beam resources used by the radar to the total beam resources of the radar. The radar's detection gain against the target is determined by calculating a weighted sum of the threat level, the tracking quality, and the reciprocal of the resource consumption. The trajectory data includes the speed, heading, and radar cross-section of each target tracked by the radar; the operating status includes the beam resources of each target tracked by the radar; and the detection information also includes the radar's detection signal-to-noise ratio, tracking accuracy, and data rate for each target tracked.

[0007] For example, before calculating the weighted sum of the threat level, the tracking quality, and the reciprocal of the resource consumption, the method further includes: Based on user instructions, determine the weights corresponding to the threat level, the tracking quality, and the reciprocal of the resource consumption; or, Based on the region where the target is located, a target weight combination is determined from multiple weight combinations, wherein each weight combination includes the weights corresponding to the threat level, the tracking quality, and the reciprocal of the resource consumption.

[0008] For example, determining the radar's tracking quality of the target based on the radar's detection signal-to-noise ratio, tracking accuracy, and data rate includes: Based on the preset maximum and preset minimum values ​​of the detection signal-to-noise ratio, the tracking accuracy, and the data rate, the detection signal-to-noise ratio, the tracking accuracy, and the data rate are normalized respectively. The detection signal-to-noise ratio is positively correlated with the corresponding normalized value, the tracking accuracy is negatively correlated with the corresponding normalized value, and the data rate is positively correlated with the corresponding normalized value. The weighted sum of the detection signal-to-noise ratio, the tracking accuracy, and the data rate is calculated to determine the tracking quality of the radar for the target.

[0009] For example, solving the objective function with the goal of maximizing the total detection gain includes: Based on the objective constraints, the objective function is solved with the goal of maximizing the total detection gain. The target constraints include a unique tracking constraint and a resource capacity constraint. The unique tracking constraint is as follows: The resource capacity constraint is: ; in, Indicates the first i Radar for the first j The tracking status of each target. Indicates tracking, Indicates no tracking; Indicates the first i Radar tracking j The beam resources consumed by each tracked target; Indicates the first i Total beam resources of the radar.

[0010] Exemplarily, before determining the detection gain of the radar for each tracked target, the method further includes: For any given target, if at least two radars in the radar network are tracking the target, the target shall be assigned to the radar with the lowest current workload or the radar with the shortest detection range among the at least two radars.

[0011] Exemplarily, the method further includes: The track data of each of the multiple radars are converted to a geocentric coordinate system; Based on the theoretical range measurement error, azimuth measurement error, and elevation measurement error of each radar, a global nearest neighbor association fusion algorithm is used to associate and fuse the track data of multiple radars to determine all targets tracked by the multiple radars.

[0012] According to another aspect of the present invention, a multi-radar resource scheduling system is provided, which is used to schedule the resources of a radar network, the radar network including multiple radars; the system includes a scheduling center, which is used to implement the above-described method.

[0013] According to another aspect of the present invention, an electronic device is provided, including a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the method as described above.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program / instructions that, when executed by a processor, implement the method described above.

[0015] In the aforementioned technical solution, an objective function for the total detection benefit of the radar network is constructed based on each radar for each target it tracks, and the solution is obtained by maximizing this benefit. On the one hand, this enables global-level resource optimization, maximizing the overall detection efficiency of the radar network and effectively avoiding problems such as idle radar resources, overloaded radars, or insufficient detection of high-value targets. It ensures that the tracking resources of the network radars are not wasted on targets already being stably tracked by other radars. Simultaneously, redundant radar resources can be released to track newly emerging or higher-threat targets, thereby improving the overall detection and tracking capacity of the entire radar network for cluster targets. On the other hand, this dynamic resource scheduling method enables distributed collaboration. Even if some radars are damaged or overloaded, other radars can dynamically adjust their task allocation to maintain continuous tracking of key targets, which helps enhance system resilience. Furthermore, by dynamically adjusting the array pointing and task allocation of each radar with the goal of optimizing the total detection benefit of the entire radar network, the overall perception capability of cluster targets can be maximized, achieving maximum detection benefit. In summary, this scheme can greatly improve the resource utilization efficiency of radar networks, significantly enhance the ability to respond to cluster targets, achieve dynamic optimal allocation of detection resources, and improve the robustness and reliability of the system. It is especially suitable for cluster target tracking in complex electromagnetic environments or multi-target dense scenarios.

[0016] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0017] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0018] Figure 1 A schematic flowchart illustrating a multi-radar resource scheduling method for cluster target detection according to an embodiment of the present invention is shown. Figure 2 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of the present invention.

[0020] According to one aspect of the present invention, a multi-radar resource scheduling method for cluster target detection is provided. This method is used to schedule resources of a radar network, which includes multiple radars. The number of radars in the radar network can be determined based on actual operating conditions, and this document does not impose any limitations on this.

[0021] Figure 1 A schematic flowchart illustrating a multi-radar resource scheduling method for cluster target detection according to an embodiment of the present invention is shown. Figure 1 As shown, the method may include steps S110, S120, S130, S140 and S150.

[0022] In step S110, the detection information of each radar is acquired. For any radar, the detection information includes the trajectory data of each target tracked by the radar and the radar's operating status.

[0023] After being powered on, the radars in the radar network can search within their respective responsibility sectors and report the detection information in real time to the scheduling center used to execute the methods described in this paper. The detection information of each radar can include the trajectory data of each target tracked by that radar and the radar's operational status. The trajectory data refers to the collection of motion state information of the target in space, such as position, velocity, and heading, used to characterize the target's trajectory. This data may include, but is not limited to, the target's position, velocity, acceleration, and radar cross-section (RCS). The radar's operational status can include the radar's current task load, resource availability, and the beam resources consumed by the radar in tracking each target. Of course, the detection information can also include performance evaluation indicators such as detection signal-to-noise ratio, tracking accuracy, and data rate, which will not be elaborated upon here.

[0024] In step S120, for any radar, based on the radar's detection information, the detection gain of the radar for each tracked target is determined.

[0025] It is understandable that the detection information includes parameters related to each target tracked by the radar, such as target speed, heading, RCS, detection signal-to-noise ratio, tracking accuracy, and data rate. Based on these real-time parameters, the detection benefit of each radar tracking each target can be evaluated in real time. This allows for the precise quantification of the detection value of different radars for different cluster targets, breaking away from the blindness of traditional scheduling methods such as "average allocation" or "experience-based allocation," and providing a more reliable data basis for subsequent radar resource scheduling.

[0026] In step S130, an objective function for the total detection gain of the radar network is constructed based on the detection gain of each radar for each target tracked by the radar network, wherein for any radar, the detection gain of the radar for untracked targets is 0.

[0027] After obtaining the detection gains of each radar for each target, the sum of the detection gains of each radar for each target can be taken as the total detection gain, thus obtaining the objective function. This objective function can be expressed as: ,in, W Indicates the total detection benefit; Indicates the first i Radar for the first j The detection gains of each tracked target; Indicates the first i Radar for the first j The tracking status of each target. Indicates tracking, This indicates no tracking. Specifically, for any radar, the detection gain for a tracked target can be calculated in step S120, while the detection gain for an untracked target can be directly determined as 0.

[0028] In step S140, with the goal of maximizing the total detection revenue, the objective function is solved to obtain the objective allocation scheme.

[0029] After obtaining the objective function, the optimal solution, i.e., the objective allocation scheme, can be obtained by solving the objective function with the goal of maximizing the total detection reward. This solution method includes, but is not limited to, the Hungarian algorithm and simulated annealing algorithm, etc., which are not limited in this paper.

[0030] In step S150, resource scheduling instructions are issued to each radar in the radar network according to the target allocation scheme.

[0031] After obtaining the target allocation scheme, it can be interpreted into a set of scheduling instructions executable by each radar. This set of instructions can include task change instructions and pointing control instructions. Task change instructions explicitly inform the radar which target IDs need to be "added to track" and which target IDs need to be "abandoned." Pointing control instructions, for targets to be added to track, first calculate the azimuth and elevation values ​​of all targets relative to the radar, then obtain the azimuth and elevation ranges of these targets in the radar's measurement coordinate system. Based on this, the center values ​​of the azimuth and elevation ranges are taken as the radar's array pointing azimuth and elevation angles, and corresponding wavefront scheduling instructions are generated. The scheduling center distributes these instructions to the corresponding radars. Upon receiving the scheduling instructions, each radar updates its task queue and resource scheduling plan, dynamically adjusts its array pointing, and executes the new tracking task. The detection results and status changes after execution are continuously reported to the scheduling center, forming a closed-loop control system of "perception-decision-execution."

[0032] In the aforementioned technical solution, an objective function for the total detection benefit of the radar network is constructed based on each radar for each target it tracks, and the solution is solved with the goal of maximizing the benefit. On the one hand, this enables global-level resource optimization, maximizing the overall detection efficiency of the radar network and effectively avoiding problems such as idle radar resources, overloaded radars, or insufficient detection of high-value targets. It ensures that the tracking resources of the network radars are not wasted on targets that have been stably tracked by other radars. At the same time, redundant radar resources can be released to track newly emerging or higher-threat targets, thereby improving the overall detection and tracking capacity of the entire radar network for cluster targets. On the other hand, this dynamic resource scheduling method enables distributed collaboration. Even if some radars are damaged or overloaded, other radars can dynamically adjust their task distribution to maintain continuous tracking of key targets, which helps to enhance system resilience. Furthermore, by dynamically adjusting the array pointing and task allocation of each radar with the goal of optimizing the total detection benefit of the entire radar network, the overall perception capability of cluster targets can be maximized, thereby maximizing the detection benefit. In summary, this scheme can greatly improve the resource utilization efficiency of radar networks, significantly enhance the ability to respond to cluster targets, achieve dynamic optimal allocation of detection resources, and improve the robustness and reliability of the system. It is especially suitable for cluster target tracking in complex electromagnetic environments or multi-target dense scenarios.

[0033] For example, the method further includes: converting the track data of each of the multiple radars into a geocentric coordinate system; and using a global nearest neighbor association fusion algorithm to perform association fusion on the track data of each of the multiple radars based on the theoretical range measurement error, azimuth measurement error, and elevation measurement error of each radar, so as to determine all targets tracked by the multiple radars.

[0034] In this example, the track data from N radars are first converted to a geocentric coordinate system. Based on the theoretical range measurement error, azimuth measurement error, and elevation measurement error of each radar, a global nearest neighbor association fusion algorithm is used to associate and fuse the tracks reported by each radar, forming a unified global target situation table. This global target situation table includes all targets tracked by the radar network. By associating and fusing track data, the correspondence between different radar track data can be accurately identified, and the association and fusion of multi-source tracks can be efficiently completed. Ultimately, accurate positioning and complete identification of all targets tracked by multiple radars can be achieved, significantly improving the accuracy, completeness, and reliability of target tracking in multi-radar cooperative detection scenarios, and effectively reducing the problems of target omission and misjudgment caused by single radar measurement errors or multi-source data mismatch.

[0035] For example, before determining the detection benefit of the radar for each tracked target, the method further includes: for any target, when at least two radars in the radar network are tracking the target, assigning the target to the radar with a lower current mission load or the radar with a shorter detection range among the at least two radars.

[0036] In this example, at the initial stage of the mission, when a target is successfully acquired by radar, to ensure stable tracking of the target and rapid situational awareness of the dispatch center, a coarse allocation can be performed using either the "proximity principle" or the "load balancing principle." For example, while continuing to assign targets to radars that have successfully acquired and tracked them, additional consideration can be given to assigning them to radars with shorter detection ranges or lighter current loads. This ensures that each target is tracked by at least one radar while maintaining balanced radar network resource usage and ensuring the accuracy of target tracking data.

[0037] For example, step S120, which determines the detection gain of the radar for each tracked target based on the radar's detection information, may specifically include the following steps S121, S122, S123, and S124.

[0038] In step S121, for any target tracked by the radar, the threat level of the target is determined based at least on the target's speed, heading, and radar cross-section.

[0039] In step S122, the tracking quality of the radar on the target is determined based on the radar's detection signal-to-noise ratio, tracking accuracy, and data rate.

[0040] In step S123, the resource consumption of the radar in tracking the target is determined based on the ratio of the beam resources used by the radar to the total beam resources of the radar.

[0041] In step S124, a weighted sum of the threat level, tracking quality, and the reciprocal of resource consumption is calculated to determine the radar's detection gain for the target; wherein, the track data includes the speed, heading, and radar cross-section of each target tracked by the radar; the operating status includes the beam resources of each target tracked by the radar; and the detection information also includes the radar's detection signal-to-noise ratio, tracking accuracy, and data rate for each target tracked.

[0042] In some implementations of this example, a Gaussian fuzzy membership function can be constructed based on information such as the target's speed, heading, and RCS (radar cross-section). A threat assessment algorithm based on the Takagi-Sugeno fuzzy system is then used for real-time fuzzy inference to obtain the specific numerical value of the threat level. Specifically, the specific numerical value of the threat level can be obtained through the following steps: Step 1: Construct Gaussian fuzzy membership functions. This step specifically includes: determining fuzzy subsets: dividing the target input parameters (velocity, heading, RCS) into fuzzy subsets; defining Gaussian function parameters: setting the center value and width of the Gaussian membership function for each fuzzy subset; calculating membership: substituting the preprocessed input parameters into the corresponding Gaussian function to obtain the membership value of each parameter in each fuzzy subset.

[0043] Step 2: Construction of the rule base for the Takagi-Sugeno fuzzy system. This step specifically includes: designing fuzzy rules: establishing rules based on domain experience or sample training; setting rule consequents: the consequents of the rules in the TS system are linear combinations of input parameters, and the linear coefficients are determined through sample fitting.

[0044] Step 3: Real-time Fuzzy Inference and Threat Level Calculation. This step specifically includes: Rule Matching and Activation Calculation: Calculate the activation of each fuzzy rule based on the membership degree of each input parameter; Weighted Fusion of Rule Consequences: Use the activation of each rule as a weight to perform a weighted summation of the linear combination results of the rule consequences; Output Threat Level: Perform inverse normalization on the fusion result to obtain the specific threat level value.

[0045] As described above, the resource consumption for radar target tracking is determined by the ratio of the radar's beam resources for tracking that target to the radar's total beam resources. The beam resources used for tracking can be obtained based on the target's position relative to the radar and the radar's beam dwell time schedule; the total radar beam resources can be manually configured with reference to radar design specifications, and will not be elaborated further.

[0046] In this example, the approach first quantifies the threat level, radar tracking quality, and resource consumption of each target, and then calculates the detection gain based on these factors. Targets with higher threat levels have higher weights and gains. Better detection conditions and higher tracking quality result in greater gains. Lower resource consumption leads to higher gains.

[0047] In some embodiments, the first i Radar for the first j The detection gain for each tracked target can be expressed as: ; in, w 1. w 2 and w 3 represents the weight values ​​of the reciprocals of threat level, tracking quality, and resource consumption, respectively; Indicates the first j Threat level of each tracked target; Indicates the first i Radar for the first j The tracking quality of each target; Indicates the first i Radar tracking j Resource consumption of each tracking target.

[0048] In the aforementioned scheme, the threat level determination stage focuses on core trajectory data such as target speed, heading, and radar cross-section, enabling accurate identification of high-threat targets and providing crucial information for subsequent resource allocation. This effectively avoids excessive focus on low-threat targets. Furthermore, the tracking quality assessment dimension, constructed by combining detection signal-to-noise ratio, tracking accuracy, and data rate, objectively reflects the radar's actual target detection effectiveness, ensuring the accuracy of the "effectiveness dimension" in the benefit calculation. Simultaneously, introducing beam resource proportion as a resource consumption metric clearly quantifies the cost of radar target tracking, achieving a synergistic consideration of "benefit-cost." Finally, by weighting and calculating the reciprocals of threat level, tracking quality, and resource consumption, the scheme integrates target threat level, detection effectiveness, and resource utilization. This not only provides clear data support for the radar's detection benefits for each tracked target but also offers precise quantitative information for subsequent radar beam resource allocation and tracking priority adjustments. This significantly improves radar resource utilization efficiency and the targeting and effectiveness of target tracking, avoiding resource waste and the oversight of high-value targets.

[0049] For example, before calculating the weighted sum of the inverses of threat level, tracking quality, and resource consumption, the method further includes: determining the weights corresponding to the inverses of threat level, tracking quality, and resource consumption according to user instructions; or, determining a target weight combination from multiple weight combinations based on the region where the target is located, wherein each weight combination includes the weights corresponding to the inverses of threat level, tracking quality, and resource consumption.

[0050] In some implementations, a weighted sum of the inverses of threat level, tracking quality, and resource consumption can be calculated based on pre-input weight values ​​or weight combinations selected by the user from multiple weight combinations. This method of determining weights based on user instructions can directly respond to personalized needs in different scenarios in practical applications (such as prioritizing the accuracy of threat level assessment or reducing resource consumption in specific tasks), ensuring that the weighted calculation results are highly aligned with the user's core requirements.

[0051] In other implementation schemes, a target weight combination is determined from multiple weight combinations based on the target's location. For example, the region can include a security zone, a warning zone, and a core zone, with the threat level weights of the security zone, warning zone, and core zone increasing sequentially. This avoids regional threat assessment bias caused by using uniform weights, allowing the low-threat characteristics of the security zone to be reasonably reflected with lower weights, preventing over-assessment and resource waste. Furthermore, it highlights the higher potential threats in the core zone and warning zone through increasing weights, helping the system prioritize high-risk areas, improving the accuracy and targeting of threat identification, and optimizing resource allocation efficiency. This ensures that when addressing different regional security needs, an assessment system more closely aligned with actual threat scenarios can be formed, providing a more scientific and reliable weighting basis for subsequent risk prevention and control decisions.

[0052] For example, determining the radar's tracking quality for the target based on its detection signal-to-noise ratio, tracking accuracy, and data rate includes: normalizing the detection signal-to-noise ratio, tracking accuracy, and data rate according to their respective preset maximum and minimum values, wherein the detection signal-to-noise ratio is positively correlated with the corresponding normalized value, the tracking accuracy is negatively correlated with the corresponding normalized value, and the data rate is positively correlated with the corresponding normalized value; and calculating a weighted sum of the detection signal-to-noise ratio, tracking accuracy, and data rate to determine the radar's tracking quality for the target.

[0053] For example, for the detection signal-to-noise ratio (SNR), a preset minimum value corresponds to 0, and a preset maximum value corresponds to 1. When the detection SNR is less than or equal to the preset minimum value, it can be normalized to 0. When the detection SNR is greater than or equal to the preset maximum value, it can be normalized to 1. When the detection SNR is between the preset minimum and the preset maximum value, the ratio of the difference between the detection SNR and the preset minimum value to the difference between the preset maximum value and the preset minimum value can be calculated for normalization.

[0054] For tracking accuracy, the preset minimum value corresponds to 1, and the preset maximum value corresponds to 0. When the tracking accuracy is less than or equal to the preset minimum value, it can be normalized to 1. When the tracking accuracy is greater than or equal to the preset maximum value, it can be normalized to 0. When the tracking accuracy is between the preset minimum and the preset maximum value, the ratio of the difference between the preset maximum value and the tracking accuracy to the difference between the preset maximum value and the preset minimum value can be calculated for normalization.

[0055] For the data rate, the preset minimum value corresponds to 0, and the preset maximum value corresponds to 1. When the data rate is less than or equal to the preset minimum value, it can be normalized to 0. When the data rate is greater than or equal to the preset maximum value, it can be normalized to 1. When the data rate is between the preset minimum and the preset maximum value, the ratio of the difference between the data rate and the preset minimum value to the difference between the preset maximum value and the preset minimum value can be calculated for normalization.

[0056] The above-mentioned scheme determines tracking quality by combining the core performance indicators of radar target detection (detection signal-to-noise ratio, tracking accuracy, and data rate), achieving scientific integration and precise quantification of multi-dimensional indicators. On the one hand, it normalizes each indicator based on its preset maximum and minimum values, and sets a reasonable normalization mapping relationship according to the actual impact logic of the indicators on tracking quality (detection signal-to-noise ratio and data rate are positively correlated with tracking quality, while tracking accuracy is negatively correlated with tracking quality). This effectively eliminates interference caused by differences in the dimensions of different indicators, ensuring that each indicator is comparable under the same evaluation dimension. On the other hand, by calculating the weighted sum of each normalized indicator, the final tracking quality result is obtained. This not only highlights the differences in the importance of different indicators in the tracking scenario (e.g., increasing the weight of data rate in high-dynamic target scenarios and increasing the weight of detection signal-to-noise ratio in long-range target scenarios), but also achieves a comprehensive and objective evaluation of radar tracking capabilities, avoiding the one-sidedness of single-indicator evaluation. Ultimately, it provides accurate and reliable quantitative basis for subsequent decisions such as optimizing and adjusting radar tracking performance and determining target priority, significantly improving the scientificity and practicality of radar target tracking evaluation.

[0057] For example, solving the objective function with the goal of maximizing total detection revenue includes: solving the objective function based on objective constraints, with the goal of maximizing total detection revenue; the objective constraints include a unique tracking constraint and a resource capacity constraint, and the unique tracking constraint is: Resource capacity constraints are: ;in, Indicates the first i Radar for the first j The tracking status of each target. Indicates tracking, Indicates no tracking; Indicates the first i Radar tracking j The beam resources consumed by each tracked target; Indicates the first i Total beam resources of the radar.

[0058] In this example scheme, a unique tracking constraint can guarantee that any target... j At least one radar is responsible for tracking. Resource capacity constraints ensure that the resources required by each radar do not exceed its resource limit. In this example, multiple radars are allowed to track a target simultaneously. Specifically, when the number of radars is much greater than the number of targets, the track quality of a target tracked by multiple radars simultaneously is higher, and the detection gains are greater.

[0059] In the above technical solution, by constructing and solving the objective function with the maximization of total detection benefits as the core objective, and combining the unique tracking constraint and resource capacity constraint to form a scientific optimization framework, the efficient allocation and precise scheduling of radar detection resources can be achieved. Under the premise of satisfying the physical constraints of radar resources and the logical rules of target tracking, the overall detection benefits are maximized, significantly improving the radar system's tracking efficiency for multiple targets, resource utilization efficiency and reliability of detection tasks, and providing a practical and feasible technical path for radar detection optimization in complex scenarios.

[0060] In some embodiments, different constraints can be set according to the different areas where the target is located. For example, when the area includes a safe zone, a warning zone, and a core zone, the target constraint conditions can also include: for a target in the safe zone, the number of radars responsible for tracking the target is one. This can further optimize the resource scheduling effect of the radar network.

[0061] According to another aspect of the present invention, a multi-radar resource scheduling system is provided. The system is used to schedule the resources of a radar network, which includes multiple radars. The system includes a scheduling center for implementing the above-described method.

[0062] According to another aspect of the present invention, an electronic device is also provided. Figure 2 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. Figure 2 As shown, the electronic device 200 includes a processor 210 and a memory 220. The memory 220 stores a computer program, which the processor 210 executes to implement the method described above.

[0063] According to another aspect of the present invention, a computer-readable storage medium is also provided. The storage medium stores a computer program / instructions that, when executed by a processor, implement the method described above. The storage medium may, for example, include a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0064] Those skilled in the art will readily understand the implementation structure, working principle, and beneficial effects of the system, electronic device, and computer-readable storage medium by reading the above methods. For the sake of brevity, further details will not be elaborated upon here.

[0065] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of the invention. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the invention as claimed in the appended claims.

[0066] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0067] In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0068] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0069] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention. However, this approach should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with fewer features than all of those in a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0070] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or elements of any method or apparatus so disclosed may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0071] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0072] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules in the electronic device according to embodiments of the present invention. The present invention can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing some or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0073] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0074] The above description is merely a specific embodiment of the present invention or an explanation of that embodiment. The scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-radar resource scheduling method for cluster target detection, characterized in that, The method is used to schedule resources in a radar network, which includes multiple radars; the method includes: Acquire the detection information of each of the radars, wherein for any radar, the detection information includes the trajectory data of each target tracked by the radar and the radar's operating status; For any of the radars, based on the radar's detection information, the detection gain of the radar for each tracked target is determined; Based on the detection gain of each radar for each target tracked by the radar network, an objective function for the total detection gain of the radar network is constructed, wherein for any radar, the detection gain of the radar for untracked targets is 0. With the goal of maximizing total detection gains, the objective function is solved to obtain the objective allocation scheme; According to the target allocation scheme, resource scheduling instructions are issued to each radar in the radar network.

2. The method according to claim 1, characterized in that, The determination of the radar's detection gain for each tracked target based on the radar's detection information includes: For any target tracked by this radar, The threat level of a target should be determined based at least on its speed, heading, and radar cross-section. Based on the radar's signal-to-noise ratio, tracking accuracy, and data rate for the target, the tracking quality of the radar for the target is determined; The resource consumption of the radar in tracking the target is determined by the ratio of the beam resources used by the radar to the total beam resources of the radar. The radar's detection gain against the target is determined by calculating a weighted sum of the threat level, the tracking quality, and the reciprocal of the resource consumption. The trajectory data includes the speed, heading, and radar cross-section of each target tracked by the radar; the operating status includes the beam resources of each target tracked by the radar; and the detection information also includes the radar's detection signal-to-noise ratio, tracking accuracy, and data rate for each target tracked.

3. The method according to claim 2, characterized in that, Before calculating the weighted sum of the threat level, the tracking quality, and the reciprocal of the resource consumption, the method further includes: Based on user instructions, determine the weights corresponding to the threat level, the tracking quality, and the reciprocal of the resource consumption; or, Based on the region where the target is located, a target weight combination is determined from multiple weight combinations, wherein each weight combination includes the weights corresponding to the threat level, the tracking quality, and the reciprocal of the resource consumption.

4. The method according to claim 2, characterized in that, The determination of the radar's tracking quality for the target based on the radar's detection signal-to-noise ratio, tracking accuracy, and data rate includes: Based on the preset maximum and preset minimum values ​​of the detection signal-to-noise ratio, the tracking accuracy, and the data rate, the detection signal-to-noise ratio, the tracking accuracy, and the data rate are normalized respectively. The detection signal-to-noise ratio is positively correlated with the corresponding normalized value, the tracking accuracy is negatively correlated with the corresponding normalized value, and the data rate is positively correlated with the corresponding normalized value. The weighted sum of the detection signal-to-noise ratio, the tracking accuracy, and the data rate is calculated to determine the tracking quality of the radar for the target.

5. The method according to claim 1, characterized in that, The process of solving the objective function, with the goal of maximizing total detection gains, includes: Based on the objective constraints, the objective function is solved with the goal of maximizing the total detection gain. The target constraints include a unique tracking constraint and a resource capacity constraint. The unique tracking constraint is as follows: The resource capacity constraint is: ; in, Indicates the first i Radar for the first j The tracking status of each target. Indicates tracking, Indicates no tracking; Indicates the first i Radar tracking j The beam resources consumed by each tracked target; Indicates the first i Total beam resources of the radar.

6. The method according to any one of claims 1-5, characterized in that, Before determining the detection gain of the radar for each tracked target, the method further includes: For any given target, if at least two radars in the radar network are tracking the target, the target shall be assigned to the radar with the lowest current workload or the radar with the shortest detection range among the at least two radars.

7. The method according to any one of claims 1-5, characterized in that, The method further includes: The track data of each of the multiple radars are converted to a geocentric coordinate system; Based on the theoretical range measurement error, azimuth measurement error, and elevation measurement error of each radar, a global nearest neighbor association fusion algorithm is used to associate and fuse the track data of multiple radars to determine all targets tracked by the multiple radars.

8. A multi-radar resource scheduling system, characterized in that, The system is used for scheduling resources of a radar network, which includes multiple radars; the system includes a scheduling center for implementing the method described in any one of claims 1-7.

9. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the method as claimed in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The system stores a computer program / instructions that, when executed by a processor, implement the method as described in any one of claims 1-7.

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