Multi-satellite cooperative moving target persistent tracking method based on improved kalman filter

Abstract

The application relates to a multi-satellite cooperative moving target continuous tracking method based on improved Kalman filtering, which comprises the following steps: calculating a multi-satellite cooperative task area, and loading the cooperative task area to a first satellite; after the first satellite detects a target, a Kalman filtering model is established, and guide information is sent to a next satellite; the next satellite predicts a target position by using an improved Kalman filtering algorithm based on the received guide information, and performs a detection task; after the satellite detects the target, Kalman filtering coefficients are updated, and the next satellite is guided to relay tracking, so that continuous tracking of the moving target is realized. According to the application, the cooperative guide success rate is effectively improved through the key methods of calculating a multi-satellite cooperative task area, improving Kalman filtering prediction and designing cooperative guide information, and the application has important application value in the field of navigation safety.

Description

Technical Field

[0001] This invention relates to the field of multi-satellite cooperative target tracking technology, and specifically to a method for continuous tracking of multi-satellite cooperative targets based on an improved Kalman filter. Background Technology

[0002] In the course of modern technological development, the need for tracking and monitoring dynamic targets is becoming increasingly urgent. Taking the maritime sector as an example, with the booming development of global trade, the maritime transport industry is bustling, and the number of ships has increased dramatically. Real-time information on the position, speed, and course of ships at sea is crucial. This not only helps ship operators plan optimal routes and reduce transportation costs, but also effectively avoids dangerous areas such as storm zones and areas with frequent piracy, ensuring maritime safety.

[0003] However, single-satellite monitoring has many limitations in target tracking. Its field of view is limited, making it difficult to cover vast ocean areas, resulting in some targets potentially remaining in monitoring blind spots for extended periods. The long revisit cycle prevents continuous, uninterrupted monitoring of targets, and tracking may be interrupted if the target changes position between two observations. Furthermore, moving targets are characterized by high maneuverability and speed, with complex and variable trajectories, making conventional single-satellite monitoring methods insufficient for tracking such targets, allowing them to easily escape the monitoring range of a single satellite.

[0004] Despite significant advancements in satellite communication and satellite link technologies, which have provided communication guarantees for efficient information exchange between multiple satellites, current multi-satellite cooperative tracking technologies still have some shortcomings. In existing technologies, the visible window time and spatial conditions of satellites are not fully considered when determining the multi-satellite cooperative mission area, leading to low cooperative efficiency. In target prediction, traditional linear models are often used, failing to fully consider the nonlinearity and uncertainty of moving target motion, making it difficult to meet practical requirements for tracking accuracy. Furthermore, the transmission of guidance information between satellites is not perfect, failing to fully utilize the advantages of advanced algorithms such as Kalman filtering to ensure the continuity and robustness of tracking.

[0005] Therefore, developing a method for continuous tracking of multi-satellite cooperative targets that can effectively overcome the above problems is an urgent issue to be addressed. Summary of the Invention

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

[0007] To achieve the objective of this invention, this invention provides a method for continuous tracking of multi-satellite cooperative targets based on improved Kalman filtering, comprising the following steps:

[0008] Step S1: Calculate the multi-satellite collaborative mission area and inject the collaborative mission area onto the first satellite;

[0009] Step S2: After the first satellite detects the target, it establishes a Kalman filter model and sends the guidance information to the next satellite;

[0010] Step S3: The next satellite, based on the received guidance information, uses an improved Kalman filter algorithm to predict the target position and performs a detection mission;

[0011] Step S4: After the satellite detects the target, it updates the Kalman filter coefficients and guides the next satellite to take over the tracking, thus achieving continuous tracking of the moving target.

[0012] According to a technical solution of the present invention, the calculation method for the multi-satellite cooperative mission area in step S1 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 Let t be the start and end time of satellite observation of the target area, 'a' be the visible field of view of the satellite in the target area, and let t be the guidance time threshold. d ;

[0014] Step S12: Sort all visible windows by observation start time to form a set {sat1,sat2,…sat…} i ,sat i+1 …sat n};

[0015] Step S13: Dynamically filter visible windows using a stack structure. The conditions for pushing a window onto the stack are 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 ;

[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 step S13, the push condition is expressed as:

[0018]

[0019] Among them, s c Let t be the set of satellite numbers in the stack. is t represents the start time of the current window. pe t represents the end time of the top window of the stack.d As the guiding time threshold, a i For the current region window, a c The visible field of view of the window at the top of the stack.

[0020] According to one technical solution of the present invention, in step S2, the guidance information includes the target's state variables and Kalman filter parameters, specifically including: target detection time t, target detection position, and velocity X. 4 State transition coefficient A, measurement coefficient C, variance P, state transition noise v m Observation noise v0.

[0021] According to a technical solution of the present invention, in step S2, the state variable X of the Kalman filter model 4 =[x,y,v x ,v y The state vector includes the target's position and velocity; when calculating the target's x-axis direction, the state vector is represented 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 This is state transition noise;

[0022] If a uniform motion model is established, then the state transition matrix A = [1,T; 0,1] follows a mean of 0 and a variance of σ. m The Gaussian distribution is measured by the equation: Z t+1 =CZ t +v0, where the measured value Z = x, C = 1; v0 is the observation noise, which follows a Gaussian distribution with a mean of 0 and a variance of σ0.

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

[0024] The initial values ​​of the system's state variables and covariance are determined from the observed values ​​x1 and x2 at times t=1 and t=2.

[0025] X2 = [x2, (x2 - x1) / T]

[0026] P2 = diag(0.1, 0.1) (1)

[0027] Substituting the initial values ​​of the state variables and covariance into the system's state transition equation, we obtain the system's prior estimates and variance:

[0028] X t =AX t-1

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

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

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

[0032] In the formula: R = CP t C T +σ0

[0033] Posterior estimation With posterior variance They are respectively:

[0034]

[0035] The same method is used to perform Kalman filtering on the position and velocity along the y-axis.

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

[0037] After the next satellite receives the guidance information, it substitutes the guidance information into equation (2) to calculate the predicted target 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's field of view.

[0038] According to one technical solution of the present invention, in step S4, when the satellite detects the target, the posterior estimate and posterior variance of the target are calculated according to equation (4), the guidance information is updated, and the target's state variables and Kalman filter parameters are sent to the next satellite.

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

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

[0041] This invention proposes a multi-satellite collaborative mission area calculation method that comprehensively considers the satellite's time window and regional window. By using a stack method to perform a depth search of the satellite's visible window, it avoids complex function call overhead, effectively optimizes memory usage, and improves the time and space utilization of multi-satellite collaboration. This enables multiple satellites to work together more efficiently and improves the overall tracking efficiency.

[0042] This invention transmits the target state variables and Kalman filter parameters as guidance information to the next satellite, eliminating the need for the next satellite to re-establish the Kalman filter equation. This saves computational resources and time, while also ensuring the continuity and robustness of the Kalman filter, making the tracking process more stable and reliable, and effectively addressing various disturbances and uncertainties during the target's motion.

[0043] This invention, considering the nonlinearity and uncertainty of moving target motion, proposes an improved Kalman filter prediction method by adding prediction point locations to the existing Kalman filter. By adding multiple prediction points around the Kalman filter position estimate, the satellite can observe over a wider range, significantly increasing the probability of target observation, thereby improving guidance success rate and enhancing the tracking capability of moving targets. Attached Figure Description

[0044] Figure 1 The schematic diagram illustrates a process flow of a multi-star cooperative target continuous tracking method based on an improved Kalman filter according to an embodiment of the present invention.

[0045] Figure 2 This schematic diagram illustrates a flowchart for calculating a multi-satellite cooperative mission region according to an embodiment of the present invention;

[0046] Figure 3 The schematic diagram illustrates the improved Kalman filtering method according to an embodiment of the present invention. Detailed Implementation

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

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

[0049] like Figure 1As shown, the present invention provides a multi-satellite cooperative target continuous tracking method based on improved Kalman filtering, comprising the following steps:

[0050] Step S1: Calculate the multi-satellite collaborative mission area and inject the collaborative mission area onto the first satellite;

[0051] like Figure 2 As shown, the visible window of a satellite includes a time window and a regional window, denoted as sat(s,t). s ,t e a), where s is the satellite number, t s t e Let t be the start and end time of satellite observation of the target area, 'a' be the visible field of view of the satellite in the target area, and let t be the guidance time threshold. d ;

[0052] Sort all visible windows in ascending order of observation start time to obtain the set {sat1,sat2,…sat…} i ,sat i+1 …sat n Push the first visible window, sat1, onto the stack, and record the union of the satellite numbers in the stack as s. c ={s1}, the visible field intersection in the stack is a c =a1, the visible window at the top of the stack is sat. p =sat1;

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

[0054]

[0055] Among them, s c Let t be the set of satellite numbers in the stack. is t represents the start time of the current window. pe t represents the end time of the top window of the stack. d As the guiding time threshold, a i For the current region window, a c The visible field of view of the window at the top of the stack.

[0056] Visible window sat i Update s after pushing onto the stack c a c and sat p Then we have:

[0057]

[0058] When s c When including all satellites, let a be the denoting factor. cFor a collaborative task area, set the visible window at the top of the stack to 'sat'. p Pop from 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] It is visible that the window is popped from the stack and then updated. c a c and sat p Then iterate through the next visible window. i+1 until only the last visible window, sat, remains in the stack. n Stop when the time comes.

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

[0062] Let the state variable X be the target position. 4 =[x,y,v x ,v y The state vector, including the target position and velocity, is X = [x, v], taking the x-axis as an example. x The state equation is:

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

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

[0065] Z t+1 =CZ t +v0

[0066] In the formula, the measured value Z = x, C = 1; v0 is the observation noise, which follows a Gaussian distribution with a mean of 0 and a variance of σ0. The Kalman filtering steps are as follows:

[0067] 1) Determine the initial values ​​of the system's state variables and covariance from the observed values ​​x1 and x2 at times t=1 and t=2:

[0068] X2 = [x2, (x2 - x1) / T]

[0069] P2 = diag(0.1, 0.1) (1)

[0070] 2) Substituting the initial values ​​of the state variables and covariance into the system's state transition equation, we obtain the system's prior estimates and variance:

[0071] X t =AX t-1

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

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

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

[0075] In the formula: R = CP t C T +σ0

[0076] 4) Posterior estimation With posterior variance They are respectively:

[0077]

[0078] 5) Use the same method to perform Kalman filtering on the position and velocity along the y-axis.

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

[0080] Serial Number Variable name Variable description 1 t Target detection time 2 <![CDATA[X 4 ]]> Target location and speed 3 A State transition coefficients 4 C Measurement coefficient 5 P variance 6 <![CDATA[v m ]]> State transition noise 7 <![CDATA[b0]]> Observation noise

[0081] Table 1

[0082] Step S3: The next satellite, based on the received guidance information, uses an improved Kalman filter algorithm to predict the target position and performs a detection mission;

[0083] After receiving the guidance information, the next satellite will transmit the X from the guidance information. 4 Substituting P into formula (2), we obtain the Kalman filter position estimate X. n1 Considering the nonlinear motion of the target, in X n1 Eight new estimated positions have been added in the four directions of up, down, left, and right. The satellite will perform its observation mission using these positions as the center. The distance between each position is determined by the satellite's field of view. Figure 3 As shown.

[0084] Step S4: After the satellite detects the target, it updates the Kalman filter coefficients and guides the next satellite to take over the tracking, thus achieving continuous tracking of the moving target.

[0085] When the satellite detects the target, the posterior estimate of the target is calculated according to formula (4). With posterior variance Update the guidance content and send the target's state variables and Kalman filter parameters to the next satellite.

[0086] By repeating steps S3 and S4, continuous tracking of the moving target can be completed.

[0087] In summary, this invention proposes a multi-satellite cooperative target continuous tracking method based on an improved Kalman filter, comprising: Step S1, calculating the multi-satellite cooperative task area and injecting the cooperative task area onto the first satellite; Step S2, after the first satellite detects the target, establishing a Kalman filter model and sending guidance information to the next satellite; Step S3, based on the received guidance information, the next satellite uses the improved Kalman filter algorithm to predict the target position and executes the detection task; Step S4, after the satellite detects the target, updating the Kalman filter coefficients and guiding the next satellite to relay the tracking, thereby achieving continuous tracking of moving targets. This method enables relay tracking of moving targets in fields such as oceanography, 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 document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0089] Furthermore, the above description represents preferred embodiments of the present invention. It should be noted that although preferred embodiments of the present invention have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A multi-satellite cooperative moving target continuous tracking method based on improved Kalman filtering, comprising the following steps: Step S1, calculating a multi-satellite cooperative task area, and inputting the cooperative task area to a first satellite, the calculation method of the multi-satellite cooperative task area comprising: Step S11, define the visible window of the satellite, denoted as sat(s, t s ,t e ,a), wherein s is the satellite number, t s ,t e is the start time and end time of the satellite observation target area, and a is the visible field of view range of the satellite on the target area, and the guide time threshold is t d ; Step S12, sort all visible windows by observation start time, form a set {sat1, sat2... sat i , sat i+1 …sat n} Step S13, dynamically screening the visible window by using the stack structure, the in-stacking condition being that the current window and the top window of the stack have intersection of the visible field of view, and the time interval is less than the guide time threshold t d , the in-stacking condition being expressed as: where s c is a 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 top window, t d is a lead time threshold, a i is the current area window, a c is the visible field of view of the top window; Step S14, when the satellite numbers in the stack cover all the satellites, recording the current field of view intersection as the cooperative task area; Step S2, after the first satellite detects the target, establishing a Kalman filtering model, and sending guiding information to the next satellite; The guiding information includes state variables and Kalman filtering parameters of the target, specifically including: target detection time t, target detection position and speed X 4 , state transition coefficient A, measurement coefficient C, variance P, state transition noise v m , observation noise v0; State variable X of Kalman filter model 4 = [x, y, v x , v y ], including target position and velocity; in calculating x-axis direction of target, state vector is expressed as X = [x, v x ], and 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; The uniform motion model is established, and a state transition matrix A = [1, T; 0, 1] is obtained, which is subject to a Gaussian distribution with a mean of 0 and a variance of σ m The measurement equation is Z t+1 = CZ t + v0, wherein a measurement value Z = x, C = 1; v0 is observation noise, which is subject to a Gaussian distribution with a mean of 0 and a variance of σ0. The Kalman filtering step is as follows: The initial values of the state variables and the covariance of the system are determined by the observation values x1 and x2 at t=1 and t=2: X2=[x2,(x2-x1) / T] P2=diag(0.1,0.1) (1) The initial values of the state variables and the covariance are substituted into the state transition equation of the system to obtain the prior estimation and the variance of the system: X t = AX t-1 P t = AP t A T + v m (2) 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 estimate with posterior variance respectively. In the same way, the Kalman filtering of the position and the speed on the y-axis is completed; Step S3, the next satellite predicts the target position based on the received guiding information, and performs a detection task by using the improved Kalman filtering algorithm; the improved Kalman filtering algorithm comprising: The next satellite receives the guidance information, substitutes the guidance information into formula (2), and calculates the target predicted position X n1 , and extends 8 predicted points in the upper, lower, left and right directions of X n1 , and the interval between each point is determined by the satellite field of view range. Step S4, after the satellite detects the target, updating the Kalman filtering coefficient, and guiding the next satellite to relay the tracking, so as to realize the continuous tracking of the moving target.

2. The method of claim 1, wherein, In the step S4, when the satellite detects the target, the posterior estimation and the posterior variance of the target are calculated according to the formula (4), the guiding information is updated, and the state variables of the target and the Kalman filtering parameters are sent to the next satellite.

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