Multi-satellite cooperative moving target continuous tracking method based on improved Kalman filtering

Abstract

The invention relates to a multi-satellite cooperative moving target continuous tracking method based on improved Kalman filtering, and the method comprises the following steps: calculating a multi-satellite cooperative task region, and uploading the cooperative task region to a first satellite; after the first satellite detects a target, a Kalman filtering model is established, and guidance information is sent to the next satellite; the next satellite predicts a target position by using an improved Kalman filtering algorithm based on the received guidance information, and executes a detection task; and after the satellite detects the target, the Kalman filtering coefficient is updated, and the next satellite is guided to track in a relay manner, so that continuous tracking of the moving target is realized. According to the method, the cooperative guidance success rate is effectively improved through key methods such as multi-satellite cooperative task area calculation, Kalman filtering prediction improvement and cooperative guidance information design, and the method has important application value in the field of navigation safety.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-satellite collaborative target tracking, and particularly relates to a multi-satellite collaborative moving target continuous tracking method based on improved Kalman filtering. Background Art

[0002] In the process of modern technological development, the demand for tracking and monitoring dynamic targets is becoming increasingly urgent. Taking the marine field as an example, with the booming development of global trade, the marine transportation industry is busy, the number of ships has increased sharply, and it is crucial to master the position, speed, course and other information of ships in the ocean in real time. This not only helps ship operators plan the optimal route, reduce transportation costs, but also can effectively avoid dangerous areas such as storm areas and areas with frequent pirate activities, and ensure navigation safety.

[0003] However, a single satellite has many limitations in target tracking. Its field of view is limited and it is difficult to cover a vast ocean area, resulting in some targets being in the monitoring blind area for a long time; the revisit period is long and it is impossible to continuously monitor the target. When the target changes its position during the interval between two satellite observations, tracking interruption may occur. In addition, moving targets themselves have characteristics such as strong mobility and high speed, and their movement trajectories are complex and changeable. Conventional single-satellite monitoring means are difficult to meet the tracking requirements of such targets, and the targets are likely to escape the monitoring range of a single satellite.

[0004] Although technologies such as satellite communication and satellite links have made remarkable progress, providing communication guarantees for efficient information interaction between multiple satellites, there are still some deficiencies in the existing multi-satellite collaborative tracking technologies. In the prior art, when determining the multi-satellite collaborative task area, the visible window time and spatial conditions of satellites are not fully considered, resulting in low collaborative efficiency; in target prediction, traditional linear models are mostly used, and the non-linearity and uncertainty of the movement of moving targets are not fully considered, and the tracking accuracy is difficult to meet the actual requirements; the transmission of guiding information between satellites is not perfect enough, and the advantages of advanced algorithms such as Kalman filtering cannot be fully utilized to ensure the continuity and robustness of tracking.

[0005] Therefore, it is an urgent problem to develop a multi-satellite collaborative moving target continuous tracking method that can effectively overcome the above problems. Summary of the Invention

[0006] In view of the above technical problems, the present invention proposes a multi-satellite collaborative moving target continuous tracking method based on improved Kalman filtering, which realizes the relay tracking of moving targets in fields such as the ocean, improves the accuracy, continuity and reliability of tracking, and provides strong technical support for relevant application scenarios.

[0007] To achieve the object of the present invention, the present invention provides a multi-satellite collaborative moving target continuous tracking method based on improved Kalman filtering, including the following steps:

[0008] Step S1: Calculate the multi-satellite collaborative mission area and upload the collaborative mission area to the first satellite.

[0009] Step S2: After the first satellite detects a target, establish a Kalman filter model and send the guidance information to the next satellite.

[0010] Step S3: The next satellite predicts the target position using the improved Kalman filter algorithm based on the received guidance information and performs the detection task.

[0011] Step S4: After the satellite detects a target, update the Kalman filter coefficients and guide the next satellite to perform relay tracking to achieve continuous tracking of the moving target.

[0012] According to a technical solution of the present invention, in the step S1, the calculation method of the multi-satellite collaborative mission area includes:

[0013] Step S11: Define the visible window of the satellite, denoted as sat(s,t s ,t e ,a), where s is the satellite number, t s 、t e are the start time and end time of the satellite observing the target area, a is the visible field of view range of the satellite in the target area, and at the same time, let the guidance time threshold be t d ;

[0014] Step S12: Sort all visible windows according to the observation start time to form a set {sat 1 ,sat 2 …sat i ,sat i+1 …sat n};

[0015] Step S13: Dynamically filter visible windows using a stack structure. The stack-in condition is that the visible field of view of the current window and the top window of the stack have an intersection, and the time interval is less than the guidance time threshold t d ;

[0016] Step S14: When the satellite numbers in the stack cover all satellites, record the current field of view intersection as the collaborative mission area.

[0017] According to a technical solution of the present invention, in the step S13, the stack-in condition is expressed as:

[0018]

[0019] Where s c is the set of satellite numbers in the stack, t is is the start time of the current window, tpe is the end time of the top window of the stack, t d is the boot time threshold, a i is the current area window, a c is the visible field of view of the top window of the stack.

[0020] According to a technical solution of the present invention, in the step S2, the guiding information includes the state variables of the target and the Kalman filter parameters, specifically including: the target detection time t, the target detection position and speed X 4 , the state transition coefficient A, the measurement coefficient C, the variance P, the state transition noise v m , the observation noise v 0 .

[0021] According to a technical solution of the present invention, in the step S2, the state variable X of the Kalman filter model 4 = [x, y, v x , v y , includes the target position and speed; when calculating the x-axis direction of the target, the state vector is expressed as X = [x, v x , and the state equation is expressed as: X t+1 = AX t + v m , where T is the measurement time interval, and v m is the state transition noise;

[0022] Establish a uniform motion model, then the state transition matrix A = [1, T; 0, 1], which follows a Gaussian distribution with a mean of 0 and a variance of σ m , and the measurement equation is: Z t+1 = CZ t + v 0 , where the measured value Z = x, C = 1; v 0 is the observation noise, which follows a Gaussian distribution with a mean of 0 and a variance of σ 0 .

[0023] According to a technical solution of the present invention, in the step S2, the Kalman filtering steps are as follows:

[0024] Determine the initial values of the state variables and covariance of the system from the observed values x1 and x2 at t = 1 and t = 2:

[0025] X 2 = [x 2 , (x 2 - x 1 ) / T]

[0026] P 2 = diag(0.1, 0.1) (1)

[0027] Substitute the initial values of the state variables and covariance into the state transition equation of the system to obtain the prior estimate and variance of the system:

[0028] X t = AX t-1

[0029] P t = AP t A T + v m (2)

[0030] Then the Kalman gain K of the system is:

[0031] K = P t C T [R + CP t C T -1 (3)

[0032] Where: R = CP t C T + σ 0

[0033] Posterior estimate and posterior variance are respectively:

[0034]

[0035] Use the same method to complete the Kalman filtering of the position and velocity on the y-axis.

[0036] According to a technical solution of the present invention, in the step S3, the improved Kalman filtering algorithm includes:

[0037] After the next satellite receives the guidance information, substitute the guidance information into Equation (2) to calculate the target predicted position X n1 , and expand 8 prediction points in the four directions of up, down, left, and right of X n1 , and the distance between each point is determined by the satellite field of view range.

[0038] According to a technical solution of the present invention, in the step S4, when the satellite detects the target, calculate the posterior estimate and posterior variance of the target according to Equation (4), update the guidance information, and send the state variables and Kalman filtering parameters of the target to the next satellite.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] ​According to the concept of the present invention, a multi-satellite collaborative moving target continuous tracking method based on improved Kalman filtering is proposed to achieve relay tracking of moving targets in fields such as the ocean, improve the accuracy, continuity, and reliability of tracking, and provide strong technical support for related application scenarios.

[0041] In the present invention, a multi-satellite collaborative mission area calculation method is proposed. It comprehensively considers the time window and regional window of satellites, conducts a depth search of the satellite visibility window through a stack method, avoids complex function call overhead, effectively optimizes memory usage, improves the time and space utilization rate of multi-satellite collaboration, enables multi-satellites to work more efficiently in collaboration, and enhances the overall tracking efficiency.

[0042] In the present invention, the target state variables and Kalman filter parameters are passed as guiding information to the next satellite, enabling the next satellite to avoid re-establishing the Kalman filter equation, saving computational resources and time, and ensuring the continuity and robustness of the Kalman filter. This makes the tracking process more stable and reliable, and can effectively cope with various interferences and uncertainties during the target movement process.

[0043] In the present invention, considering the non-linearity and uncertainty of the moving target movement, an improved Kalman filter prediction method is proposed. Based on the Kalman filter, the predicted point positions are added. By adding multiple predicted points around the Kalman filter position estimate, the satellite can observe in a wider range, greatly increasing the probability of the target being observed, thereby enhancing the guidance success rate and strengthening the tracking ability of the moving target. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Schematically showing the flowchart of the multi-satellite collaborative moving target continuous tracking method based on improved Kalman filtering according to an embodiment of the present invention;

[0045] Figure 2 Schematically showing the flowchart of calculating the multi-satellite collaborative mission area in the embodiment of the present invention;

[0046] Figure 3 Schematically showing the schematic diagram of the improved Kalman filter method in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0048] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments cannot be enumerated one by one here, but the embodiments of the present invention are not limited to the following embodiments.

[0049] As Figure 1 shown, a multi-satellite collaborative moving target continuous tracking method based on improved Kalman filtering of the present invention includes the following steps:

[0050] Step S1, calculate the multi-satellite collaborative task area and upload the collaborative task area to the first satellite;

[0051] As Figure 2 shown, the visible window of the satellite includes a time window and a region window, denoted as sat(s, t s , t e , a), where s is the satellite number, t s , t e are the start time and end time of the satellite observing the target area, a is the visible field of view range of the satellite in the target area, and at the same time, the guidance time threshold is set as t d ;

[0052] Sort all the visible windows in ascending order according to the observation start time to obtain the set {sat 1 , sat 2 … sat i , sat i+1 … sat n}, push the first visible window sat 1 onto the stack, record the union of the satellite numbers in the stack as s c = {s 1}, the intersection of the visible fields of view in the stack is a c = a 1 , and the visible window at the top of the stack is sat p = sat 1 ;

[0053] Traverse the next visible window sat i The conditions for pushing onto the stack are:

[0054]

[0055] where s c is the set of satellite numbers in the stack, t is is the start time of the current window, t pe is the end time of the window at the top of the stack, t d is the guidance time threshold, a i is the current region window, and a c is the visible field of view of the window at the top of the stack.

[0056] The visible window sati Update s after pushing onto the stack c , a c and sat p , then there is:

[0057]

[0058] When s c contains all satellites, record a c as a collaborative mission area, and pop the visible window sat p at the top of the stack;

[0059] When traversing to the last visible window, if the last window is pushed onto the stack, perform two pop operations. If the last window is not pushed onto the stack, perform one pop operation;

[0060] Update s c , a c and sat p after the visible window sati is popped, and then traverse the next visible window sat i+1 , until only the last visible window sat n remains in the stack and stop.

[0061] Step S2: After the first satellite detects the target, establish a Kalman filter model and send the guidance information to the next satellite;

[0062] Let the state variable X 4 of the target position = [x, y, v x , v y , including the target position and speed. Taking the x-axis as an example, its state vector is X = [x, v x , and the state equation is:

[0063] X t+1 = AX t + v m

[0064] Establish a uniform motion model, and its state transition matrix A = [1, T; 0, 1], where T is the measurement time interval, and v m is the state transition noise, which follows a Gaussian distribution with a mean of 0 and a variance of σ m . The measurement equation is:

[0065] Z t+1 = CZ t + v 0

[0066] In the formula, the measurement value Z = x, C = 1; v 0 is the observation noise, which follows a Gaussian distribution with a mean of 0 and a variance of σ 0 . The Kalman filter steps are as follows:

[0067] 1) Determine the initial values of the state variables and covariance of the system from the observed values x1 and x2 at times t = 1 and t = 2:

[0068] X 2 =[x 2 ,(x 2 -x 1 ) / T]

[0069] P 2 =diag(0.1,0.1) (1)

[0070] 2) Substitute the initial values of the state variables and covariance into the state transition equation of the system to obtain the prior estimate and variance of the system:

[0071] X t =AX t-1

[0072] P t =AP t A T +v m (2)

[0073] 3) Then the Kalman gain K of the system is:

[0074] K = P t C T [R + CP t C T -1 (3)

[0075] Where: R = CP t C T + σ 0

[0076] 4) Posterior estimate and posterior variance are respectively:

[0077]

[0078] 5) Complete the Kalman filtering of the position and velocity on the y-axis in the same way.

[0079] Send the guidance information to the next satellite. The guidance information is divided into state variables and Kalman filter parameters, and the specific content is shown in Table 1.

[0080] Serial number Variable name Variable description 1 t Target acquisition time 2 <![CDATA[X 4 > Target acquisition position and speed 3 A State transition coefficient 4 C Measurement coefficient 5 P Variance 6 <![CDATA[v m > State transition noise 7 <![CDATA[b 0 > Observation noise

[0081] Table 1

[0082] ​Step S3: Based on the received guidance information, the next satellite predicts the target position using the improved Kalman filtering algorithm and performs the detection task;

[0083] After receiving the guidance information, the next satellite substitutes the X 4 and P in the guidance information into formula (2) to obtain the Kalman filter position estimate X n1 . Considering the factors of the target's non-linear motion, eight estimated positions are added in the four directions of up, down, left, and right of X n1 . The satellite performs the observation task centered on these positions, and the distance between each position is determined by the satellite's field of view, as shown in Figure 3 .

[0084] Step S4: After the satellite detects the target, it updates the Kalman filter coefficients and guides the next satellite to perform relay tracking to achieve continuous tracking of the moving target.

[0085] When the satellite detects the target, it calculates the posterior estimate of the target and the posterior variance according to formula (4) to update the guidance content, and sends the state variables and Kalman filter parameters of the target to the next satellite.

[0086] Repeatedly execute Step S3 and Step S4 to complete the continuous tracking of the moving target.

[0087] In summary, the present invention proposes a multi-satellite collaborative moving target continuous tracking method based on improved Kalman filtering, including: Step S1: Calculate the multi-satellite collaborative task area and upload the collaborative task area to the first satellite; Step S2: After the first satellite detects the target, establish a Kalman filter model and send the guidance information to the next satellite; Step S3: Based on the received guidance information, the next satellite predicts the target position using the improved Kalman filtering algorithm and performs the detection task; Step S4: After the satellite detects the target, it updates the Kalman filter coefficients and guides the next satellite to perform relay tracking to achieve continuous tracking of the moving target, realizing the relay tracking of moving targets in fields such as the ocean, improving the accuracy, continuity, and reliability of tracking, and providing strong technical support for related application scenarios.

[0088] It should be noted that in this article, the terms "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article, or terminal device. Without further limitations, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article, or terminal device including the said element.

[0089] In addition, the above is the preferred embodiment of the present invention. It should be noted that although the preferred embodiments of the present invention have been described, for those skilled in the art of this technology, once the basic creative concept of the present invention is known, several improvements and refinements can be made without departing from the principles described in the present invention, and these improvements and refinements should also be regarded as within the protection scope of the present invention. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A method for continuous tracking of moving targets by multi-satellite coordination based on improved Kalman filtering, comprising the following steps: Step S1, calculating the multi-satellite collaborative mission area, and adding the collaborative mission area to the first satellite; Step S2: After the first satellite detects the target, a Kalman filter model is established and guidance information is sent to the next satellite; Step S3: The next satellite predicts the target position based on the received guidance information using the improved Kalman filter algorithm and performs the detection mission; Step S4: After the satellite detects the target, it updates the Kalman filter coefficient and guides the next satellite to relay tracking to achieve continuous tracking of the moving target.

2. The method according to claim 1, characterized in that In step S1, the calculation method of the multi-satellite collaborative mission area includes: Step S11: define the visible window of the satellite, denoted as sat(s,t s ,t e ,a), where s is the satellite number, t s ,t e is the start and end time of the satellite observation target area, a is the visible field of view of the satellite in the target area, and the guidance time threshold is t d ; Step S12: sort all visible windows by observation start time to form a set {sat1, sat2…sat i ,sat i+1 …sat n }; Step S13: Use a stack structure to dynamically filter visible windows. The stacking condition is that the visible fields of the current window and the top window of the stack intersect, and the time interval is less than the guidance time threshold t d ; Step S14: When the satellite numbers in the stack cover all satellites, the current field of view intersection is recorded as the collaborative mission area.

3. The method according to claim 2, characterized in that In step S13, the stacking condition is expressed as: Among them, s c is the satellite number set in the stack, t is is the start time of the current window, t pe is the end time of the top window, t d is the boot time threshold, a i is the current area window, a c The visible field of view of the window at the top of the stack.

4. The method according to claim 1, characterized in that In step S2, the guidance information includes the state variables and Kalman filter parameters of the target, specifically including: target detection time t, target detection position and speed x 4 , state transfer coefficient A, measurement coefficient C, variance P, state transfer noise v m , observation noise v0.

5. The method according to claim 1, characterized in that In step S2, the state variable X of the Kalman filter model 4 =[x,y,v x ,v y ], including the target position and speed; when calculating the x-axis direction of the target, the state vector is expressed as X = [x, v x ], the state equation is expressed as: X t+1 =AX t +v m , where T is the measurement time interval, v m is the state transition noise; Establish a uniform motion model, then the state transfer matrix A = [1, T; 0, 1], with a mean of 0 and a variance of σ m Gaussian distribution, the measurement equation is: Z t+1 =CZ t +v0, where the measured value Z=x, C=1; v0 is the observation noise, which obeys a Gaussian distribution with a mean of 0 and a variance of σ0.

6. The method according to claim 5, characterized in that In step S2, the Kalman filtering steps are as follows: The initial values ​​of the system's state variables and covariance are determined by the observed values ​​x1 and x2 at t=1 and t=2: X2=[x2,(x2-x1) / T] P2=diag(0.1,0.1) (1) Substitute the initial values ​​of the state variables and covariance into the state transfer equation of the system to obtain the prior estimate and variance of the system: X t =AX t-1 P t =AP t A T +v m (2) Then the Kalman gain K of the system is: K=P t C T [R+CP t C T ] -1 (3) Where: R = CP t C T +σ0 Posterior Estimation and the posterior variance They are: The Kalman filter of the position and velocity on the y-axis is completed in the same way.

7. The method according to claim 6, characterized in that In step S3, improving the Kalman filter algorithm includes: After the next satellite receives the guidance information, it substitutes the guidance information into equation (2) to calculate the target predicted position X n1 , and in X n1 Eight prediction points are extended in the four directions of up, down, left and right, and the distance between each point is determined by the satellite field of view.

8. The method according to claim 6, characterized in that In step S4, when the satellite detects the target, the posterior estimation and posterior variance of the target are calculated according to formula (4), the guidance information is updated, and the state variable and Kalman filter parameters of the target are sent to the next satellite.

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