A multi-target collaborative tracking method in synaesthesia integrated network

Abstract

Since the communication and perception modes of the base station share resources, the resources allocated to the perception mode are relatively limited under the premise of meeting the communication requirements. At the same time, although the dense deployment of base stations meets the demand for full coverage of low-altitude airspace, it also allows low-altitude targets such as drones to be perceived and tracked by multiple base stations at the same time, resulting in a waste of resources caused by multiple base stations tracking the same target. In order to overcome the shortcomings of the prior art, the present invention proposes a multi-target collaborative tracking method for the interawareness integrated network, which realizes the effective collaborative optimization of base station tracking resources under the base station perception working mode, achieves a reasonable match between the base station and the tracking target, and greatly improves the overall tracking efficiency of the network.

Description

Technical Field

[0001] The present invention belongs to the field of network communications, and in particular relates to a multi-target collaborative tracking method in a synaesthesia integrated network. Background Art

[0002] With the advancement of low-altitude open-air policies and the development of the drone industry, the effective utilization of low-altitude airspace has enormous application potential and socioeconomic value. However, the current lack of effective tracking and control of low-altitude drones makes it difficult to ensure their normal operation, severely restricting the effective utilization of low-altitude airspace. Deploying sensing equipment to achieve full coverage of low-altitude airspace requires extremely high deployment and operating costs. With the vigorous development of mobile communications, target perception, and information technology, future mobile communication systems will integrate multi-dimensional capabilities such as communication and perception. Base stations, as the most important nodes in mobile communication networks, can be integrated with sensing capabilities by adding sensing capabilities to existing base stations. This not only improves communication performance but also enables real-time perception and tracking of low-altitude targets. Effective low-altitude airspace control and management is also possible based on target perception information.

[0003] Because the communication and perception modes of a base station share resources, the resources allocated to the perception mode are relatively limited while meeting communication requirements. Furthermore, while dense base station deployment meets the need for full coverage of low-altitude airspace, it also allows low-altitude targets such as drones to be sensed and tracked simultaneously by multiple base stations, resulting in resource waste caused by multiple base stations tracking the same target. Therefore, it is necessary for the network to collaboratively optimize the tracking targets of multiple integrated sensing base stations, maximizing the overall tracking efficiency of the network within the constraints of limited sensing resources, in order to achieve effective target tracking and low-altitude airspace management and control.

[0004] At present, there is no research on the design of multi-target collaborative tracking methods for synaesthesia integrated networks. Summary of the Invention

[0005] To overcome the shortcomings of the existing technology, this paper proposes a multi-target collaborative tracking method for the interawareness integrated network. This method realizes the effective collaborative optimization of base station tracking resources in the base station sensing working mode, achieves a reasonable match between the base station and the tracking target, and greatly improves the overall tracking efficiency of the network. Specifically, it includes the following steps:

[0006] (10) Base station communication perception resource allocation: The base station perceives the environment and divides resources based on the number and information of end users. A portion of the resources is used to meet communication service needs, and the other portion is used for target perception. The allocated resources are reported to the central controller.

[0007] (20) The central controller sets the base station tracking strategy: The central controller sets the multi-target collaborative tracking plan between base stations based on the location of the UAV target uploaded by the base station and the tracking resources allocated by each base station, and sends the plan to each base station;

[0008] (30) Each base station performs communication and perception: On the basis of reusing the same hardware resources, the base station performs communication and perception services based on the allocated synaesthesia resources. Target perception includes two modes: search and tracking. In the search mode, the base station performs periodic searches on the airspace it covers. In the tracking mode, the base station tracks the corresponding UAV target according to the tracking plan issued by the central controller, and uploads the target's perception information to the central controller for centralized processing.

[0009] The beneficial effects of the present invention are

[0010] Compared with the existing technology, the present invention has the following significant advantages: based on the different tracking values ​​of different targets and the conflicts caused by different base stations tracking the same target, the collaborative tracking solution design between base stations effectively solves the problem of resource waste caused by multiple base stations tracking the same target, realizes the effective tracking of high-value targets by base stations, effectively improves the overall tracking efficiency of the network, and enhances the control capability of low-altitude airspace. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flowchart of the overall working process of the present invention.

[0012] Figure 2 It is a schematic diagram of the network system model of the present invention.

[0013] Figure 3 This is the algorithm flow chart of the step central controller designing base station tracking strategy.

[0014] Figure 4 This is a performance comparison chart of the method of the present invention and two heuristic methods under different perception ranges of the base station. DETAILED DESCRIPTION

[0015] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0016] like Figure 1 As shown, the overall process of the present invention includes the following steps:

[0017] (10) Communication perception resource allocation of base stations: The base station divides time and other resources based on information such as the number of terminal users through environmental perception, with one part used to meet communication service needs and the other part used for target perception; the allocated resources are reported to the central controller. The network system model is shown in the figure below. Figure 2 Specifically:

[0018] (11) Each base station m in the network senses the environment and allocates corresponding resources to the communication service based on the number of terminals and service requirements, which is recorded as T m,c The resources contained in one synaptic cycle of base station m are recorded as T m , then the resources used for perception are: T m -T m,c .

[0019] (12) In the sensing mode, the base station adopts the Track and Search (TAS) scheme, where the resources required for the base station m to search are denoted as T m,s , the resources that can be used to track the target are: T m,t =T m -T m,c -T m,s .

[0020] (13) Base station m uses its available resources T to track the target m,t Upload to the central controller.

[0021] (20) The central controller sets the base station tracking strategy: The central controller designs a multi-target collaborative tracking plan between base stations based on the location of the drone target uploaded by the base station and the tracking resources allocated to each base station, and sends the plan to each base station. Specifically:

[0022] (21) The connection matrix between the UAV target set {1,2,...U} acquired by the central controller and the base station set {1,2,...M} in the region in the previous synaesthesia cycle is defined as: C∈{0,1} M×U , where element c m,u =1 means that the drone u is within the sensing range of the base station m, c m,u =0 means that the human machine u is not within the sensing range of the base station m.

[0023] (22) Different drones have different tracking values, where p u Represents the tracking value of UAV u. At the same time, different UAVs have different tracking data rate requirements, so the perception resources consumed are also different, where w u It represents the sensing resources consumed to track the UAV u in one synaesthesia cycle.

[0024] (23) The central controller needs to maximize the overall tracking efficiency of the network by designing and optimizing the collaborative tracking strategy under the constraints of base station tracking resources. This optimization problem can be expressed as follows:

[0025]

[0026]

[0027]

[0028] where a m,u is the tracking scheme of base station m for drone u, a m,u =1 means base station m tracks drone u, a m,u = 0 means that base station m does not track drone u. The objective function of the optimization problem P1 is the overall tracking performance of the network, and the second constraint is the tracking resource constraint of base station m.

[0029] (24) The central controller obtains the collaborative sensing solution by solving the optimization problem P1. The flow chart is as follows: Figure 3 The specific steps are as follows:

[0030] (241) The central controller initializes its own tracking scheme {a m,u}:

[0031] (242) For each UAV target u that can be tracked by base station m, the ratio of tracking value to tracking resources is That is, the unit tracking values ​​are sorted in descending order.

[0032] (243) For all base stations and drones, if the base station sensing resources are sufficient, the maximum unit tracking value is selected. Match the base station and the drone, that is, select the one that satisfies c m,u =1,a m,u =0 and The largest base station-UAV pair (m,u), let base station m track UAV u, that is, a m,u =1, and update the remaining sensing resources of base station m and the network's sensing scheme {a m,u}.

[0033] (244) The central controller repeats step (243) until the sensing resources of all base stations are insufficient to track any target that has not been tracked, and obtains the final tracking solution {a m,u}.

[0034] (30) Each base station performs communication and perception: The base station performs communication and perception services based on the allocated synaesthesia resources on the basis of reusing the same hardware resources. Among them, target perception includes two modes: search and tracking. In the search mode, the base station performs periodic searches on the airspace it covers; in the tracking mode, it tracks the corresponding drone target according to the tracking plan issued by the central controller. The perception information of the target is uploaded to the central controller for centralized processing. Specifically:

[0035] (31) The base station provides communication services and perception services based on the allocated communication resources and perception resources.

[0036] (32) In the sensing mode, the base station performs signal processing and data processing on the received echo signal in the traditional target sensing mode to obtain target information, such as location information.

[0037] (33) The base station uploads the target information it senses to the central controller, which then integrates and processes the information uploaded by the base station to form a basic situation of the nearby airspace and conducts corresponding airspace control.

[0038] Compared with random tracking and distributed tracking methods, the multi-target collaborative tracking method proposed in this paper can greatly improve the overall tracking efficiency of the network and increase the utilization of sensing resources. The random tracking method is that the base station randomly selects drones within its coverage area for tracking, while the distributed tracking method is that the base station selects the drone with the highest cumulative tracking value within its coverage area for tracking.

[0039] Figure 4 The performance of the multi-target collaborative tracking method proposed in this invention was compared with that of two other heuristic tracking algorithms under different base station perception ranges. It can be seen that the multi-target collaborative tracking method proposed in this invention consistently outperforms the other two tracking methods. In particular, the performance of the multi-target collaborative tracking method proposed in this invention far outperforms the other two tracking methods when the perception range increases. This is because as the base station perception range increases, the overlap in the perception airspace between base stations increases, and the coupling becomes stronger. In this case, the collaborative tracking method can avoid conflicts and improve tracking resource utilization, resulting in far superior performance to the other two methods.

[0040] The present invention is not limited to the above specific embodiments, and various modifications and variations are possible. Any modification, equivalent replacement, improvement, etc. made to the above embodiments based on the technical essence of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A method for collaborative tracking of multiple targets in a synaesthesia-integrated network, characterized by: The steps include: (10) Base station communication perception resource allocation: The base station perceives the environment and divides resources based on the number and information of end users. A portion of the resources is used to meet communication service needs, and the other portion is used for target perception. The allocated resources are reported to the central controller. (20) The central controller sets the base station tracking strategy: The central controller sets the multi-target collaborative tracking plan between base stations based on the location of the UAV target uploaded by the base station and the tracking resources allocated by each base station, and sends the plan to each base station; (30) Each base station performs communication and perception: On the basis of reusing the same hardware resources, the base station performs communication and perception services based on the allocated synaesthesia resources. Target perception includes two modes: search and tracking. In the search mode, the base station performs periodic searches of the airspace it covers. In the tracking mode, the base station tracks the corresponding UAV target according to the tracking plan issued by the central controller, and uploads the target's perception information to the central controller for centralized processing. The communication sensing resource allocation of the base station in step (10) specifically includes: (11) Each base station m in the network senses the environment and allocates corresponding resources to the communication service according to the number of terminals, business requirements and information, which is recorded as T m,c , the resources contained in one synaesthesia cycle of base station m are recorded as T m , then the resources used for perception are: T m -T m,c ; (12) In the sensing mode, the base station adopts the tracking and searching method, where the resources required by the base station m are recorded as T m,s , the resources that can be used to track the target are: T m,t =T m -T m,c -T m,s ; (13) Base station m uses its available resources T to track the target m,t Upload to the central controller; The step (20) wherein the central controller sets the base station tracking strategy specifically includes: (21) The connection matrix between the UAV target set {1,2,...U} acquired by the central controller and the base station set {1,2,...M} in the region in the previous synaesthesia cycle is defined as: C∈{0,1} M×U , where element c m,u =1 means that the drone u is within the sensing range of the base station m, c m,u =0 means that the human machine u is not within the sensing range of the base station m; (22) Different drones have different tracking values, p u represents the tracking value of UAV u. Different UAVs have different tracking data rate requirements, so the perception resources consumed are also different. u It represents the sensing resources consumed to track the UAV u in one synaesthesia cycle; (23) Under the constraints of base station tracking resources, the central controller optimizes the collaborative tracking strategy to maximize the overall tracking efficiency of the network: where a m,u is the tracking scheme of base station m for drone u, a m,u =1 means base station m tracks drone u, a m,u =0 means that base station m does not track UAV u; the objective function of the optimization problem P1 is the overall tracking efficiency of the network, and the second constraint is the tracking resource constraint of base station m; (24) The central controller obtains the collaborative sensing solution by solving the optimization problem P1.

2. The multi-target collaborative tracking method in a synaesthesia integrated network according to claim 1, characterized in that: The base station communication sensing time division work in step (30) specifically includes: (31) The base station provides communication services and sensing services based on the allocated communication resources and sensing resources respectively; (32) In the sensing mode, the base station performs signal processing and data processing on the received echo signal in the traditional target sensing mode to obtain target information; (33) The base station uploads the target information it senses to the central controller, which then integrates and processes the information uploaded by the base station to form a basic situation of the nearby airspace and conducts corresponding airspace control.

3. The multi-target collaborative tracking method in a synaesthesia integrated network according to claim 1, characterized in that: In step (24), the central controller obtains a collaborative sensing solution by solving the optimization problem P1, which specifically includes: (241) The central controller initializes its own tracking scheme {a m,u }:a m,u =0, (242) For each UAV target u that can be tracked by base station m, the ratio of tracking value to tracking resources is That is, the unit tracking value is sorted in descending order; (243) For all base stations and drones, if the base station sensing resources are sufficient, the maximum unit tracking value is selected. Match the base station and the drone, that is, select the one that satisfies c m,u =1,a m,u =0 and The largest base station-UAV pair (m,u), let base station m track UAV u, that is, a m,u =1, and update the remaining sensing resources of base station m and the network's sensing scheme {a m,u }; (244) The central controller repeats step (243) until the sensing resources of all base stations are insufficient to track any target that has not been tracked, and obtains the final tracking solution {a m,u }.

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