Self-adaptive risk awareness multi-target tracking method with network repair

Abstract

The invention discloses a self-adaptive risk-aware multi-target tracking method with network repair, and relates to the technical field of unmanned aerial vehicle trajectory planning. The method has the beneficial effects that the method is a two-stage strategy: in the first stage, whether the whole team needs to be repaired or not is checked; if necessary, quantizing the observability of the team by using the trace of the Grammian matrix, and giving a new network structure meeting the observability of one hop; and in the second stage, the safety and the accuracy are quantified, the sensing margin is used as a dynamic weight, and the coordinates of the unmanned aerial vehicle at the next moment are given. The method is not only used for track generation after self-adaptive tradeoff between accuracy and safety, but also has a function of automatically repairing a communication network.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle trajectory planning, and in particular to an adaptive risk-aware multi-target tracking method with network repair. Background Art

[0002] Target tracking technology refers to the use of various sensors to detect targets and obtain their status, including but not limited to position and speed. This technology is widely used in civil and military fields. However, with the diversification and complexity of application scenarios, the traditional target tracking theory based on the single-point target model no longer meets people's requirements for tasks. Therefore, people have begun to study the theory of multi-target tracking. Among them, in order to save costs and improve resource utilization, it is mostly applied based on heterogeneous drone teams.

[0003] Considering the complexity of multi-target tracking tasks and scenarios, the multi-robot system performing the task will inevitably be subject to external interference or even high-intensity attacks. These external interferences and attacks may come from harsh environments, such as fire scenes, magnetic fields, or unavoidable problems in the measurement process, such as angular glint, limited sensing area, or tracking targets with attack capabilities. In order to deal with the above situations, research is usually carried out starting from three indicators: robustness, resilience, and flexibility.

[0004] Risk-aware target tracking (also known as safety-aware target tracking) usually requires modeling and estimating known or unknown hazards. Hazards may come from adversarial tracking objects, complex task execution environments, interference within a multi-robot team, or unknown external interference. In these tasks, one of the most basic requirements is to avoid collisions, which includes three types of constraints: within the tracker, between the tracker and the environment, and between the tracker and the target. One approach is to use a control obstacle function to constrain the control strategy of the robot team using its forward invariance to achieve collision avoidance. However, the control obstacle function has its limitations in collision avoidance control, including dependence on the precise model of the system, high computational complexity, conservative strategy, difficulty in parameter adjustment, compatibility issues with other control methods, insufficient robustness, and the possibility of falling into local minima. These shortcomings may affect the collision avoidance effect and efficiency in practical applications.

[0005] Another approach is to use artificial potential fields to achieve safe behavior. In practical applications, risk-aware target tracking needs to consider other goals such as observation accuracy, safety, and energy consumption in addition to meeting collision avoidance conditions. It should be noted that these goals may conflict or even contradict each other. In order to balance multiple goals, a large number of scientists have conducted research in related fields. A cascade control strategy is adopted: first, the perception performance and energy consumption are balanced when generating trajectories; second, when generating control strategies, it balances the degree of closeness to the nominal control strategy and the response speed. In order to solve the above problems, one method is to linearly combine the two objective functions and optimize them uniformly. A cascade control strategy can also be used to optimize the two objectives separately in turn to obtain the final control strategy. However, this type of method also has the disadvantages of local minimum problem, target unreachability problem, oscillation problem, difficulty in parameter adjustment, and high computational complexity, which limits its effectiveness and efficiency in practical applications.

[0006] In addition, reinforcement learning based on composite artificial potential fields can be used to obtain equilibrium control strategies. However, using reinforcement learning for decision-making has problems such as long training time, high consumption of computing resources, strong environmental dependence, difficult to explain decision-making process, low sample efficiency and stability during training. Summary of the invention

[0007] The purpose of the present invention is to provide an adaptive risk-aware multi-target tracking method with network repair in view of the deficiencies and defects in the prior art. The method is a two-stage strategy: in the first stage, it will be checked whether the entire team needs to be repaired. If necessary, the observability of the team is quantified by the trace of the Grammian matrix, and a new network structure that satisfies one-hop observability is given; and in the second stage, the coordinates of the drone at the next moment are given by quantifying security and accuracy, using the sensor margin as a dynamic weight. This method is not only used for trajectory generation after adaptively balancing accuracy and security, but also has the function of automatically repairing the communication network.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: an adaptive risk-aware multi-target tracking method with network repair, a heterogeneous drone team tracks a group of dynamic targets, and the motion of each target can be expressed by the following state space equation: Where Z is the robot coordinate, A is the state transfer matrix, B is the input matrix, u is the input control variable, and w is an independent Gaussian noise matrix with zero mean and covariance matrix Q; the formula written in compact form is as follows: Although each drone tracker only tracks its own target, it still observes all targets. Therefore, for each drone, there is the following linear observation model: Among them, y is the observed value of the target state, H is the output matrix, and v is the observation noise; considering the one-hop community where the drone is located in the communication network Therefore, the linear observation model is as follows: in, The community-based measurement noise takes the following form: in, Based on the above observation model, the Gaussian function danger field of the jth tracking target is defined as follows. It can be seen that it is exponentially related to the square of the distance; Correspondingly, the safety field function is defined as follows: Then, for the target state measured by the sensor, Kalman filtering is used for data fusion, which is divided into prediction step and update step: In order to quantify the task performance, an evaluation index is given, and then a one-hop observable Gramian matrix is ​​defined. The specific formula is as follows: Among them, T o is a pre-selected time interval, Π i,t is a positive definite matrix used to quantify the security of closed community i. Based on the previously defined security field, the security weight Π i,t The definition is as follows: Among them, π k (x j,t ) represents the safety field between UAV j and target k; sensor margin is selected as another indicator to evaluate mission performance. A distributed UAV is observable within its community, which means that its is observable, corresponding to The rank of is φ, φ = Md, and the sensing margin under distributed conditions is defined as follows: First, based on the above dynamic model, observation model and two performance indicators, the following optimization-based adaptive risk-aware target tracking algorithm is proposed:

[0009] When the drone is subjected to a tolerable attack, the above solution is no longer effective. Therefore, an edge repair strategy is introduced. When performing edge repair, it is not necessary to consider the observation performance, but only the one-hop observable Gram matrix. The observable Gram matrix of sensor j is as follows:

[0010] The one-hop observable Gram matrix of drone i is as follows:

[0011] in, is a closed connection matrix, and the compressed form of the observable Gram matrix of the entire team is as follows: Then use vector To record which drone individuals do not meet one-hop observability: For individuals that are not satisfied, the repair weight is increased, and the updated weight is as follows: W = I + Diag (l). Based on the above content, the following repair algorithm is given:

[0012]

[0013] in, Then, seven drones track seven targets with attack intentions. When resources are sufficient at the beginning, the drones will track the targets closely but keep a distance. After being attacked, the distance will be widened. When irreparable resources are encountered, the communication map will be repaired by adding edges.

[0014] Furthermore, the sensor is one-dimensional, so the sensor output matrix is ​​as follows Among them, h is the output vector of the sensor, and is selected As a combination of the column indices of the non-empty entries in the i-th row of the resource matrix Γ, for a given resource matrix and sensor output matrix, the output matrix of drone i for target j is as follows:

[0015] Furthermore, the measurement noise is defined as the covariance R ij,t The zero-mean and independent Gaussian noise is a distance-dependent function. As the observation distance increases, the observation noise increases exponentially: in: The λ s and w s is the shape parameter of the Gaussian distribution.

[0016] Furthermore, considering that a heterogeneous drone team may be attacked when performing distributed tracking of multiple targets, the attacks are divided into the following three categories based on the changes in observability status before and after the attack: 1. Minor attack (the individuals before and after the attack satisfy one-hop observability); 2. Tolerable attack (one-hop observability is satisfied before the attack, but not after the attack, but one-hop observability can be restored by acquiring the sensor resources of new members); 3. Intolerable attack (one-hop observability is satisfied before the attack, but not after the attack, but one-hop observability can be restored by acquiring the sensor resources of new members and one-hop observability cannot be re-satisfied by sharing resources).

[0017] Furthermore, the In the algorithm, the first constraint is the dynamic constraint of the UAV, which limits the displacement of a single iteration. The second constraint is the collision avoidance constraint between different UAVs, which limits the minimum distance between UAVs. The third constraint is the scaling of the UAV measurement accuracy. The fourth constraint is the scaling of the UAV safety. The objective function is a linear combination of the scaling of the two indicators based on the sensor margin. In the observation task, when observability is met, the accuracy of tracking observation is improved as much as possible. When the UAV makes a slight attack and observability decreases, the focus is first on improving safety and then optimizing tracking accuracy.

[0018] Furthermore, the In the above example, the decision variable L represents the new Laplacian graph, where the first constraint ensures the properties of the Laplacian graph; the second constraint ensures that the graph is connected; the sixth constraint strictly defines that each diagonal element of L is greater than 0; the seventh constraint ensures that the off-diagonal elements in L are the opposite values ​​of the corresponding elements in Π; the ninth constraint determines that at least one edge is added; the third constraint strictly defines the form of the one-hop observable Gram matrix; the fourth and fifth elements ensure the property of Π as a closed connection matrix; the eighth constraint limits the maximum number of added edges.

[0019] After adopting the above technical solution, the beneficial effects of the present invention are as follows: This method is a two-stage strategy: In the first stage, it will be checked whether the entire team needs to be repaired. If necessary, the observability of the team is quantified by the trace of the Grammian matrix, and a new network structure that satisfies one-hop observability is given; and in the second stage, the coordinates of the drone at the next moment are given by quantifying security and accuracy, using the sensor margin as a dynamic weight. This method is not only used for trajectory generation after adaptively balancing accuracy and security, but also has the function of automatically repairing the communication network. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 It is a schematic diagram of the process of the present invention.

[0022] Figure 2 This is a schematic diagram of 7 to 7 multi-target tracking in the present invention. Figure 1 .

[0023] Figure 3 This is a schematic diagram of 7 to 7 multi-target tracking in the present invention. Figure 2 .

[0024] Figure 4 This is a schematic diagram of 7 to 7 multi-target tracking in the present invention. Figure 3 .

[0025] Figure 5 This is a schematic diagram of 7 to 7 multi-target tracking in the present invention. Figure 4 .

[0026] Figure 6 This is a schematic diagram of 7 to 7 multi-target tracking in the present invention. Figure 5 .

[0027] Figure 7 This is a schematic diagram of 7 to 7 multi-target tracking in the present invention. Figure 6 .

[0028] Figure 8 This is a schematic diagram of 7 to 7 multi-target tracking in the present invention. Figure 7 .

[0029] Fig. 9 This is a schematic diagram of 7 to 7 multi-target tracking in the present invention. Figure 8 .

[0030] Fig.10 This is a schematic diagram of 7 to 7 multi-target tracking in the present invention. Figure 9 . DETAILED DESCRIPTION

[0031] See also Figure 1 As shown, the technical solution adopted in this specific implementation is: a heterogeneous drone team tracks a group of dynamic targets, and the movement of each target can be expressed by the following state space equation:

[0032]

[0033] Where Z is the robot coordinate, A is the state transfer matrix, B is the input matrix, u is the input control quantity, and w is an independent Gaussian noise matrix with zero mean and covariance matrix Q;

[0034] The formula written in compact form is as follows:

[0035]

[0036] Although each drone tracker only tracks its own target, it still observes all targets. Therefore, for each drone, there is the following linear observation model:

[0037]

[0038] Where y is the observed value of the target state, H is the output matrix, and v is the observation noise;

[0039] The sensor is one-dimensional, so the sensor output matrix is ​​as follows Among them, h is the output vector of the sensor, and is selected As a combination of the column indices of the non-empty entries in the i-th row of the resource matrix Γ, for a given resource matrix and sensor output matrix, the output matrix of drone i for target j is as follows:

[0040]

[0041] The measurement noise is defined as the covariance R ij,t The zero-mean and independent Gaussian noise is a distance-dependent function. As the observation distance increases, the observation noise increases exponentially:

[0042]

[0043] in:

[0044]

[0045] The λ s and w s is the shape parameter of the Gaussian distribution.

[0046] Considering the one-hop community of the drone in the communication network Therefore, the linear observation model is as follows:

[0047]

[0048] in,

[0049] The community-based measurement noise takes the following form:

[0050] in,

[0051] Based on the above observation model, the Gaussian function danger field of the jth tracking target is defined as follows, which can be seen to be exponentially related to the square of the distance:

[0052]

[0053] Correspondingly, the safety field function is defined as follows:

[0054]

[0055] Then, for the target state measured by the sensor, Kalman filtering is used for data fusion, which is divided into prediction step and update step:

[0056]

[0057] In order to quantify the task performance, an evaluation index is given, and then a one-hop observable Gramian matrix is ​​defined. The specific formula is as follows:

[0058]

[0059] Among them, T o is a pre-selected time interval, Π i,t is a positive definite matrix used to quantify the security of closed community i. Based on the previously defined security field, the security weight Π i,t The definition is as follows:

[0060]

[0061] Among them, π k (x j,t ) represents the safety field between UAV j and target k;

[0062] Sensor margin is selected as another metric to evaluate mission performance. A distributed UAV is observable within its community, which means its is observable, corresponding to The rank of is φ, φ = Md, and the sensing margin under distributed conditions is defined as follows:

[0063]

[0064] Considering that a heterogeneous drone team may be attacked when performing distributed tracking of multiple targets, the attacks are divided into the following three categories based on the changes in the observability status before and after the attack:

[0065] 1) Minor attack (both individuals before and after the attack satisfy one-hop observability);

[0066] 2) Tolerable attacks (one-hop observability is satisfied before the attack, but not after the attack, but one-hop observability can be restored by acquiring the sensor resources of new members);

[0067] 3) Intolerable attacks (one-hop observability is satisfied before the attack, but not after the attack. However, one-hop observability can be restored by acquiring the sensor resources of new members and one-hop observability cannot be satisfied again by sharing resources).

[0068] First, based on the above dynamic model, observation model and two performance indicators, the following optimization-based adaptive risk-aware target tracking algorithm is proposed:

[0069]

[0070] Among them, the first constraint is the dynamic constraint of the UAV, which limits the displacement of a single iteration. The second constraint is the collision avoidance constraint between different UAVs, which limits the minimum distance between UAVs. The third constraint is the scaling of the UAV measurement accuracy. The fourth constraint is the scaling of the UAV safety. The objective function is a linear combination of the scaling of the two indicators based on the sensor margin. In the observation task, when observability is met, the accuracy of tracking observation is improved as much as possible. When the UAV makes a slight attack and observability decreases, the focus is first on improving safety, and then optimizing tracking accuracy.

[0071] When the drone is subjected to a tolerable attack, the above solution is no longer effective. Therefore, an edge repair strategy is introduced. When performing edge repair, it is not necessary to consider the observation performance, but only the one-hop observable Gram matrix. The observable Gram matrix of sensor j is as follows:

[0072]

[0073] The one-hop observable Gram matrix of drone i is as follows:

[0074]

[0075] in, is a closed connection matrix, and the compressed form of the observable Gram matrix of the entire team is as follows:

[0076]

[0077] Then use vector To record which drone individuals do not meet one-hop observability:

[0078]

[0079] For the unsatisfied individuals, the repair weight is increased, and the updated weight is as follows:

[0080] W=I+Diag(l) (17)

[0081] Based on the above content, the following repair algorithm is given:

[0082]

[0083] in, The decision variable L represents the new Laplacian graph, where the first constraint ensures the properties of the Laplacian graph; the second constraint ensures that the graph is connected; the sixth constraint strictly defines that each diagonal element of L is greater than 0; the seventh constraint ensures that the off-diagonal elements in L are the opposite values ​​of the corresponding elements in Π; the ninth constraint determines that at least one edge is added; the third constraint strictly defines the form of the one-hop observable Gram matrix; the fourth and fifth elements ensure the property of Π as a closed connection matrix; the eighth constraint limits the maximum number of added edges

[0084] See also Figure 2-Figure 10 As shown, 7 drones then track 7 targets with attack intentions. At the beginning, when resources are sufficient, the drones will track the targets closely but keep a distance. After being attacked, the distance will be widened. When irreparable resources are encountered, the communication diagram will be repaired by adding edges.

[0085] The above description is only used to illustrate the technical solution of the present invention rather than to limit it. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in this field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. An adaptive risk-aware multi-target tracking method with network repair, characterized by: A heterogeneous fleet of drones is tracking a set of dynamic targets, and the motion of each target can be represented by the following state-space equation: Where Z is the robot coordinate, A is the state transfer matrix, B is the input matrix, u is the input control quantity, and w is an independent Gaussian noise matrix with zero mean and covariance matrix Q; The formula written in compact form is as follows: Although each drone tracker only tracks its own target, it still observes all targets. Therefore, for each drone, there is the following linear observation model: Where y is the observed value of the target state, H is the output matrix, and v is the observation noise; Considering the one-hop community of the drone in the communication network Therefore, the linear observation model is as follows: in, The community-based measurement noise takes the following form: in, Based on the above observation model, the Gaussian function danger field of the jth tracking target is defined as follows. It can be seen that it is exponentially related to the square of the distance; Correspondingly, the safety field function is defined as follows: Then, the target state measured by the sensor is fused using Kalman filtering. It is divided into prediction step and update step: In order to quantify the task performance, an evaluation index is given, and then a one-hop observable Gramian matrix is ​​defined. The specific formula is as follows: Among them, T o is a pre-selected time interval, Π i,t is a positive definite matrix used to quantify the security of closed community i. Based on the previously defined security field, the security weight Π i,t The definition is as follows: Among them, π k (x j,t ) represents the safety field between UAV j and target k; Sensor margin is selected as another metric to evaluate mission performance. A distributed UAV is observable within its community, which means its is observable, corresponding to The rank of is φ, φ = Md, and the sensing margin under distributed conditions is defined as follows: First, based on the above dynamic model, observation model and two performance indicators, the following optimization-based adaptive risk-aware target tracking algorithm is proposed: When the drone is subjected to a tolerable attack, the above solution is no longer effective. Therefore, an edge repair strategy is introduced. When performing edge repair, it is not necessary to consider the observation performance, but only the one-hop observable Gram matrix. The observable Gram matrix of sensor j is as follows: The one-hop observable Gram matrix of drone i is as follows: in, is a closed connection matrix, and the compressed form of the observable Gram matrix of the entire team is as follows: Then use vector To record which drone individuals do not meet one-hop observability: For the unsatisfied individuals, the repair weight is increased, and the updated weight is as follows: W=I+Diag(l) (17) Based on the above content, the following repair algorithm is given: in, Then, seven drones track seven targets with attack intentions. When resources are sufficient at the beginning, the drones will track the targets closely but keep a distance. After being attacked, the distance will be widened. When irreparable resources are encountered, the communication map will be repaired by adding edges.

2. The adaptive risk-aware multi-target tracking method with network repair according to claim 1 is characterized by: The sensor is one-dimensional, so the sensor output matrix is ​​as follows Among them, h is the output vector of the sensor, and is selected As a combination of the column indices of the non-empty entries in the i-th row of the resource matrix Γ, for a given resource matrix and sensor output matrix, the output matrix of drone i for target j is as follows:

3. The adaptive risk-aware multi-target tracking method with network repair according to claim 1 is characterized by: The measurement noise is defined as the covariance R ij,t The zero-mean and independent Gaussian noise is a distance-dependent function. As the observation distance increases, the observation noise increases exponentially: in: The λ s and w s is the shape parameter of the Gaussian distribution.

4. The adaptive risk-aware multi-target tracking method with network repair according to claim 1 is characterized by: Considering that a heterogeneous drone team may be attacked when performing distributed tracking of multiple targets, the attacks are divided into the following three categories based on the changes in the observability status before and after the attack: 1) Minor attack (both individuals before and after the attack satisfy one-hop observability); 2) Tolerable attacks (one-hop observability is satisfied before the attack, but not after the attack, but one-hop observability can be restored by acquiring the sensor resources of new members); 3) Intolerable attacks (one-hop observability is satisfied before the attack, but not after the attack. However, one-hop observability can be restored by acquiring the sensor resources of new members and one-hop observability cannot be satisfied again by sharing resources).

5. The adaptive risk-aware multi-target tracking method with network repair according to claim 1 is characterized in that: In Formula 12, the first constraint is the dynamic constraint of the UAV, which limits the displacement of a single iteration. The second constraint is the collision avoidance constraint between different UAVs, which limits the minimum distance between UAVs. The third constraint is the scaling of the UAV measurement accuracy. The fourth constraint is the scaling of the UAV safety. The objective function is a linear combination of the scaling amounts of the two indicators based on the sensor margin. In the observation task, when observability is met, the accuracy of tracking observation is improved as much as possible. When the UAV makes a slight attack and observability decreases, the focus is first on improving safety and then optimizing tracking accuracy.

6. The adaptive risk-aware multi-target tracking method with network repair according to claim 1 is characterized by: In Formula 18, the decision variable L represents the new Laplacian graph, wherein the first constraint ensures the properties of the Laplacian graph; the second constraint ensures that the graph is connected; the sixth constraint strictly defines that each diagonal element of L is greater than 0; the seventh constraint ensures that the non-diagonal elements in L are the opposite values ​​of the corresponding elements in Π; the ninth constraint determines that at least one edge is added; the third constraint strictly defines the form of the one-hop observable Gram matrix; the fourth and fifth elements ensure the property of Π as a closed connection matrix; and the eighth constraint limits the maximum number of added edges.

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