Method for near range measurement and tracking of space large targets by space based radar

Abstract

This invention discloses a method for short-range measurement and tracking of large space targets by space-based radar. Addressing the angular scintillation effect caused by the large local RCS of large and complex space targets during short-range measurement and tracking, the method extracts target body shape feature information by constructing a target measurement time window, performs data correlation filtering on multiple measurements to eliminate interference from the target's local RCS impact response points, and provides an offset tracking scheme for situations with large local RCS interference, ensuring the radar beam always points towards the target body, effectively improving the accuracy and stability of radar tracking of the target body under local RCS interference.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and in particular to a method for short-range measurement and tracking of large space targets by space-based radar. Background Technology

[0002] With the rapid development of aerospace technology, the application of space-based radar has expanded from the detection, early warning, and surveillance of ground (sea) and air targets to the detection, identification, and surveillance (tracking) of space targets. In the detection, identification, and tracking of space targets such as spacecraft, the target's RCS (Radar Cross Section) characteristics are an important input to the tracking and aiming system. For large and complex space targets, the target's RCS characteristics not only change with the target's direction of radar detection, but also the target's RCS distribution characteristics are more complex. In close-range measurement and tracking, there are interference from local RCS alluvial response points (singularities) of the target, and even local RCS values ​​remain relatively large relative to the target body for a long time, which has a significant impact on the accuracy of close-range target measurement and tracking.

[0003] Therefore, it is necessary to propose a method for short-range measurement and tracking of large space targets using space-based radar. Summary of the Invention

[0004] This invention provides a method for close-range measurement and tracking of large space targets by space-based radar. To address the angular scintillation effect caused by the large local RCS of large and complex space targets during close-range measurement and tracking, this method extracts the target's body shape feature information by constructing a target measurement time window and performs data correlation filtering and tracking on multiple measurements to eliminate interference from the target's local RCS impact response points.

[0005] To achieve the aforementioned objectives, the present invention provides a method for short-range measurement and tracking of large space targets using space-based radar, comprising:

[0006] A uniform motion model of the target and a two-dimensional radar observation model are constructed; a Kalman filter is used to perform iterative calculations and tracking of the target;

[0007] The target airspace is searched and scanned. When the target enters the radar beam range, the center of the radar beam points to the target and the target's tracking filter is initialized.

[0008] A FIFO-shaped time window is constructed for the target echo measurement. The angular deviation is calculated for the two adjacent measurements with the largest polarization in the measurement set at each moment. The angular deviation is used as the target's body shape feature for tracking. Based on the target's body shape feature and target motion angular velocity at the current moment, it is determined whether the target's body shape feature is valid.

[0009] If the target's body shape characteristics are valid, then the target echo measurement values ​​within the time window are processed for data association and tracking: through the cumulative measurement in the time window, the association probability of all candidate echoes falling within the target echo is calculated, and the weighted average of all candidate echoes is calculated by calculating the probability that each echo comes from the target body to obtain the equivalent echo of the target at the current moment.

[0010] Preferably, the construction of the target uniform motion model and the radar two-dimensional observation model includes:

[0011]

[0012] F(k) is the system state transition matrix, X(k+1) is the target state vector, w(k) is the zero-mean Gaussian system noise with covariance Q; H is the observation matrix, Y(k) is the target observation value at time k, and v(k) is the Gaussian observation noise with mean and covariance R.

[0013] The one-step prediction of the state is:

[0014] The one-step prediction of covariance is: P(k+1|k)=F(k)P(k|k)F'(k)+Q(k)

[0015] The covariance of the measurement information is: S(k+1)=H(k+1)P(k+1|k)H(k+1)+R(k+1)

[0016] Kalman gain calculation: K(k+1)=P(k+1|k)H'(k+1)S -1 (k+1)

[0017] Status Update: Where Z represents the measurement information of the target;

[0018] Finally, perform a covariance update: P(k+1|k+1)=[IK(k+1)H(k+1)]P(k+1|k)

[0019] I is the identity matrix of the same dimension as P(k+1|k).

[0020] Preferably, determining the validity of the target's body shape characteristics based on the target's current body shape characteristics and angular velocity includes:

[0021] like Then the target's physical characteristics are valid, among which, Let ω be the angular velocity of the target motion, p be the sensitivity coefficient for target feature recognition, and Δα(k) be the angular deviation.

[0022] Preferably, the data correlation and tracking processing of the target echo measurement values ​​within the time window includes:

[0023] Assume the cumulative measurement set in the time window at time k is Z. k Z(k) is defined as the set of candidate echoes that fall within the target tracking gate during the time window at time k.

[0024] Where, m k The target candidate echo number at this moment;

[0025] Define the event:

[0026] θ i (k)={z i (k) is the actual measurement of the target entity, i = 1, 2, ..., m k ;

[0027] θ0(k) = {At time k, there is no actual measurement of the target body};

[0028] The i-th measurement z i (k) The conditional probability originating from the target is: β i (k)=Pr{θi i (k)|Z k},

[0029] Calculate the correlation probability of each measurement:

[0030]

[0031] in

[0032] Among them, V i Let S(k) be the innovation of the i-th measurement, S(k) be the measurement innovation covariance, λ be the clutter spatial density, and P be the density of the clutter. G To measure the probability that the target body falls within the gate, P D The probability of target detection;

[0033] The update equation for the target state under the Kalman filter is:

[0034]

[0035] in, Based on event θ i (k) is the conditional target state update estimate, v i (k) represents the new information corresponding to this measurement value;

[0036] The covariance update equation for the target is:

[0037] Where P c(k|k)=[IK(k)H(k)]P(k|k-1),

[0038] Preferably, after performing data correlation and tracking processing on the target echo measurement values ​​within the time window, the method further includes:

[0039] By analyzing the non-uniform distribution characteristics of the target's RCS, the target's tracking trajectory, and the set of past measurements within the time window, the relative region where the target is located is determined.

[0040] The offset tracking angle is calculated based on the measurement angle with the largest polarization in the current measurement set and the target's current angle estimate, as well as the angle deviation between the two adjacent measurements with the largest polarization in the current measurement set.

[0041] When the offset tracking angle is greater than the tracking angle error threshold, the radar beam center is deviated from the target tracking estimate by the offset tracking angle and pointed to the location of the target body, so that the radar maintains tracking of the target body.

[0042] Preferably, the step of calculating the offset tracking angle based on the measurement angle with the largest polarization relative to the area where the subject is located in the measurement set at the current moment, the estimated angle of the target at the current moment, and the angular deviation between the two adjacent measurements with the largest polarization in the measurement set at the current moment includes:

[0043] Based on the formula: Calculate the offset tracking angle, where δ is the measurement angle with the largest polarization relative to the target body region in the current measurement set. This is the estimated angle of the target at the current moment.

[0044] The present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the method for short-range measurement and tracking of large space targets by space-based radar as described above.

[0045] The present invention also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the method described above for short-range measurement and tracking of large space targets by space-based radar.

[0046] Compared with the prior art, the technical solution of the present invention has at least the following beneficial effects:

[0047] 1. This invention takes into account the RCS distribution characteristics of large and complex space targets. By setting measurement time windows and multi-measurement data correlation filtering, it reduces the interference of local RCS alluvial response points (singular points) of large and complex space targets and eliminates the angular scintillation phenomenon that may occur during close-range tracking by space-based radar.

[0048] 2. This invention takes into account that the measurement information during close-range tracking of large and complex spatial targets is not typical point target information, but extended target information with contour features. The body features of the target are identified and tracked by analyzing past measurement sets within a time window.

[0049] 3. This invention takes into account the strong interference from the non-uniformly distributed local RCS of the target during close-range tracking of irregular large space targets (the local RCS value of the target remains relatively large relative to the body for a long time). By performing offset tracking on the target, the radar can maintain tracking of the target body and prevent the reduction of tracking accuracy of the target body due to strong interference from the target's local RCS.

[0050] 4. The processing of this invention has low complexity and low overall computational load. Therefore, it has low requirements for hardware equipment and is easy to implement in engineering. Attached Figure Description

[0051] Figure 1 This is a flowchart of a method for short-range measurement and tracking of large space targets using space-based radar according to an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the data processing time window of a method for short-range measurement and tracking of large space targets by space-based radar according to an embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of the offset tracking method for short-range measurement and tracking of large space targets by space-based radar according to an embodiment of the present invention;

[0054] Figure 4 This is a schematic diagram of the structure of a large and complex space target according to an embodiment of the present invention;

[0055] Figure 5(a) is a schematic diagram of the target tracking trajectory of a method for short-range measurement and tracking of large space targets using a space-based radar according to an embodiment of the present invention;

[0056] Figure 5(b) is a schematic diagram of the target X-axis tracking accuracy deviation of a method for close-range measurement and tracking of large space targets using a space-based radar according to an embodiment of the present invention;

[0057] Figure 5(c) is a schematic diagram of the target y-axis tracking accuracy deviation of a method for close-range measurement and tracking of large space targets using a space-based radar according to an embodiment of the present invention.

[0058] Figure 5(d) is a schematic diagram of the target X-axis velocity tracking accuracy deviation using a space-based radar close-range measurement and tracking method for large space targets according to an embodiment of the present invention;

[0059] Figure 5(e) is a schematic diagram of the target y-axis velocity tracking accuracy deviation of a method for close-range measurement and tracking of large space targets using a space-based radar according to an embodiment of the present invention. Detailed Implementation

[0060] The method for short-range measurement and tracking of large space targets using space-based radar, as proposed in this invention, will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of this invention will become clearer from the following description. It should be noted that the accompanying drawings are in a very simplified form and use non-precise proportions, used only to facilitate and clearly illustrate the embodiments of this invention. Please refer to the accompanying drawings to make the objectives, features, and advantages of this invention more apparent and understandable. It should be understood that the structures, proportions, sizes, etc., depicted in the accompanying drawings are only for illustrative purposes to aid those skilled in the art and are not intended to limit the implementation conditions of this invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to the size, without affecting the effects and objectives achieved by this invention, should still fall within the scope of the technical content disclosed in this invention.

[0061] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0062] It should be noted that the apparatus and methods disclosed in the embodiments herein can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments herein. In this regard, each block in a flowchart or block diagram may represent a module, program, or part of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system to perform the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0063] In addition, the functional modules in the various embodiments of this article can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0064] To address the angular scintillation effect caused by the large local RCS of large and complex space targets during close-range measurement and tracking, this embodiment provides a method for space-based radar to measure and track large space targets at close range. This method extracts target body shape feature information by constructing a target measurement time window, performs data correlation filtering on multiple measurements to eliminate interference from the target's local RCS impingement response points, and provides an offset tracking scheme for situations with large local RCS interference, ensuring the radar beam always points towards the target body, effectively improving the accuracy and stability of radar tracking of the target body under local RCS interference. (The method involves constructing a target measurement time window to extract target body feature information, performing data correlation filtering on multiple measurements to eliminate interference from the target's local RCS impingement response points, and providing an offset tracking scheme for situations with large local RCS interference, ensuring the radar beam always points towards the target body, effectively improving the accuracy and stability of radar tracking of the target body under such interference.) Figure 4 As shown, its external structure is mainly composed of the spacecraft body and the spacecraft local structure, which are distributed vertically. In most cases, the RCS of the target body is greater than that of the target local structure. When the radar incident wave direction is nearly perpendicular to the target local structure, the target local structure has a large reflective surface and a strong RCS.

[0065] Please see Figure 1 , Figure 1 This is a flowchart of a method for close-range measurement and tracking of large space targets using space-based radar according to an embodiment of the present invention. The method includes:

[0066] A target uniform motion model and a radar two-dimensional observation model are constructed; a Kalman filter is used to perform iterative calculations to track the target; specifically, the constructed target uniform motion model and radar two-dimensional observation model are as follows:

[0067]

[0068] F(k) is the system state transition matrix, X(k+1) is the target state at time k, w(k) is the Gaussian system noise with zero mean and covariance Q; H is the observation matrix, Y(k) is the target observation value at time k, and v(k) is the Gaussian observation noise with mean and covariance R.

[0069] Where X(k) is the target state vector, and F(k) is the target state transition matrix, with the following values:

[0070]

[0071] Let w(k) be the Gaussian system noise with zero mean and covariance Q. Let w(k) be the variance, then the system process noise covariance matrix is:

[0072]

[0073] H is the observation matrix, and Y(k) is the target observation value at time k, with the following values:

[0074] Y(k) = [x, y] T

[0075]

[0076] v(k) is Gaussian observation noise with mean and covariance R; where...

[0077]

[0078] The target is tracked using iterative calculations using a Kalman filter.

[0079] The one-step prediction of the state is:

[0080] The one-step prediction of covariance is: P(k+1|k)=F(k)P(k|k)F'(k)+Q(k)

[0081] The covariance of the measurement information is: S(k+1)=H(k+1)P(k+1|k)H(k+1)+R(k+1)

[0082] Kalman gain calculation: K(k+1)=P(k+1|k)H'(k+1)S -1 (k+1)

[0083] Status Update: Where Z represents the target's measurement information, which generally includes the target's spatial coordinates.

[0084] Finally, perform a covariance update: P(k+1|k+1)=[IK(k+1)H(k+1)]P(k+1|k)

[0085] I is the identity matrix of the same dimension as P(k+1|k).

[0086] When a large and complex space target is close to a space-based radar, the target is basically outside the radar beam range. The radar searches and scans the target's airspace. When the target enters the radar beam range, the center of the radar beam points to the target and initializes the target's tracking filter.

[0087] Due to limitations in the radial and angular resolution of space-based radar, during close-range tracking of this target, the target's RCS distribution is concentrated vertically. In this case, the target measurement information is not typical point target information, but rather extended target information with contour features. Therefore, a FIFO (First In, First Out) time window is constructed for the target echo measurement, such as... Figure 2 As shown, in this embodiment, N=4.

[0088] Based on the historical measurement set Z in the time window k The system uses k = 1, 2, ... to identify and track the target's body features. For each moment in the measurement set, the angular deviation Δα between the two adjacent measurements with the largest polarization is calculated and used as the target's body feature for tracking. If the target's body feature Δα(k) at the current moment is greater than the target's angular velocity, the target's body feature is considered valid.

[0089] in, denoted as angular velocity of the target motion, and p is the sensitivity coefficient for target feature recognition.

[0090] If the target's body features are valid, the target is treated as an extended target for data association tracking. Otherwise, the target is treated as a point target for filtered tracking.

[0091] The target's RCS distribution is concentrated at both the top and bottom, which will produce strong angular scintillation. To eliminate the interference from local RCS impact response points (singularities) on the target, data correlation and tracking processing is performed on the target echo measurements within the time window:

[0092] Assume the cumulative measurement set in the time window at time k is Z. k Z(k) is defined as the set of candidate echoes that fall within the target tracking gate during the time window at time k.

[0093] In the formula, m k This represents the number of candidate echoes for the target at that moment. Since the target's angular velocity is very small, the radar can set a small angular error as the tracking gate threshold.

[0094] Define the event:

[0095] θ i (k)={z i (k) is the actual measurement of the target entity, i = 1, 2, ..., m k ;

[0096] θ0(k) = {At time k, there is no actual measurement of the target body}.

[0097] The i-th measurement z i (k) The conditional probability originating from the target is: β i (k)=Pr{θ i (k)|Z k},

[0098] Calculate the correlation probability of each measurement:

[0099]

[0100] in,

[0101]

[0102] In the formula, V i Let S(k) be the innovation of the i-th measurement, S(k) be the measurement innovation covariance, λ be the clutter spatial density, and P be the density of the clutter. G To measure the probability that the target body falls within the gate, P D denoted as the target detection probability.

[0103] The update equation for the target state under the Kalman filter is:

[0104]

[0105] In the formula, Based on event θ i (k) is the conditional target state update estimate, v i (k) represents the new information corresponding to this measurement value.

[0106] The covariance update equation for the target is:

[0107]

[0108] in,

[0109] P c(k|k)=[IK(k)H(k)]P(k|k-1)

[0110]

[0111] By accumulating measurements within a time window, the correlation probability is calculated for all candidate echoes falling within the target's relevant gate. The weighted average of all candidate echoes, calculated based on the probability that each echo originates from the target body, is used as the equivalent echo of the target at the current moment. This reduces the interference of the target's local RCS alluvial response point on the tracking accuracy of the space-based radar. The target tracking effect is shown in Figure 5. Figure 5(a) is a schematic diagram of the target tracking trajectory using a space-based radar close-range measurement and tracking method for large space targets according to an embodiment of the present invention; Figure 5(b) is a schematic diagram of the target X-axis tracking accuracy deviation using a space-based radar close-range measurement and tracking method for large space targets according to an embodiment of the present invention; Figure 5(c) is a schematic diagram of the target y-axis tracking accuracy deviation using a space-based radar close-range measurement and tracking method for large space targets according to an embodiment of the present invention; Figure 5(d) is a schematic diagram of the target X-axis velocity tracking accuracy deviation using a space-based radar close-range measurement and tracking method for large space targets according to an embodiment of the present invention; Figure 5(e) is a schematic diagram of the target y-axis velocity tracking accuracy deviation using a space-based radar close-range measurement and tracking method for large space targets according to an embodiment of the present invention.

[0112] When the target tracking process is strongly disturbed by the non-uniformly distributed local RCS of the target, or even when the local RCS value of the target remains relatively large relative to the body for a long time, the tracking of the target body will deviate significantly. In this case, it is necessary to perform offset tracking on the target.

[0113] By analyzing the non-uniform distribution characteristics of the target's RCS, the target's tracking trajectory, and past measurement sets within the time window, the relative region where the target body is located was determined to be the lower region. Offset tracking was then performed on this region under strong interference in the target's local RCS. A schematic diagram of the offset tracking is shown below. Figure 3 The radar offset tracking target's current state estimate. (Dashed arrow) When the target's body features are valid, calculate the offset tracking angle:

[0114]

[0115] In the formula, δ is the measurement angle with the largest polarization relative to the target body region in the measurement set at the current moment. This is the estimated angle of the target at the current moment.

[0116] When the offset tracking angle is greater than the tracking angle error threshold β0, the radar beam center is deviated from the target tracking estimate by the offset tracking angle β and pointed to the location of the target body (solid arrow), so that the radar maintains tracking of the target body and prevents the tracking accuracy of the target body from being reduced due to strong interference from the local RCS of the target.

[0117] This embodiment provides a method for short-range measurement and tracking of large space targets using space-based radar. It considers the RCS distribution characteristics of large and complex space targets, and reduces interference from local RCS alluvial response points (singularities) of large and complex space targets by setting measurement time windows and multi-measurement data correlation filtering, thus eliminating angular flicker that may occur during short-range tracking by space-based radar. It also considers that the measurement information during short-range tracking of large and complex space targets is not typical point target information, but rather extended target information with contour features. The method identifies and tracks the target's body shape features by analyzing past measurement sets within the time window. Furthermore, it considers the strong interference from the non-uniformly distributed local RCS of irregular large space targets during short-range tracking (where the local RCS value remains relatively large relative to the target body for a long time). By performing offset tracking, the radar maintains tracking of the target body, preventing a decrease in tracking accuracy due to strong interference from the target's local RCS. This invention has low processing complexity and low overall computational load. Therefore, it has low hardware requirements and is easy to implement in engineering.

[0118] This embodiment also provides an electronic device, including a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, it implements any of the above-described methods for short-range measurement and tracking of large space targets by space-based radar.

[0119] This embodiment also provides a readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-described methods for short-range measurement and tracking of large space targets by space-based radar.

[0120] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for short-range measurement and tracking of large space targets using space-based radar, characterized in that, include: Construct a target uniform motion model and a radar two-dimensional observation model; The target is tracked using iterative calculations using a Kalman filter; The target airspace is searched and scanned. When the target enters the radar beam range, the center of the radar beam points to the target and the target's tracking filter is initialized. A FIFO-shaped time window is constructed for the target echo measurement. The angular deviation is calculated for the two adjacent measurements with the largest polarization in the measurement set at each moment. The angular deviation is used as the target's body shape feature for tracking. Based on the target's body shape feature and target motion angular velocity at the current moment, it is determined whether the target's body shape feature is valid. If the target's body shape characteristics are valid, then the target echo measurement values ​​within the time window are processed for data association and tracking: through the cumulative measurement in the time window, the association probability of all candidate echoes falling within the target echo is calculated, and the weighted average of all candidate echoes is calculated by calculating the probability that each echo comes from the target body to obtain the equivalent echo of the target at the current moment.

2. The method for close-range tracking and measurement of large space targets by space-based radar as described in claim 1, characterized in that, The construction of the target uniform motion model and the radar two-dimensional observation model includes: F(k) is the system state transition matrix, X(k+1) is the target state vector, w(k) is the zero-mean Gaussian system noise with covariance Q; H is the observation matrix, Y(k) is the target observation value at time k, and v(k) is the Gaussian observation noise with mean and covariance R. The one-step prediction of the state is: The one-step prediction of covariance is: P(k+1|k)=F(k)P(k|k)F'(k)+Q(k) The covariance of the measurement information is: S(k+1)=H(k+1)P(k+1|k)H(k+1)+R(k+1) Kalman gain calculation: K(k+1)=P(k+1|k)H'(k+1)S -1 (k+1) Status Update: Where Z represents the measurement information of the target; Finally, perform a covariance update: P(k+1|k+1)=[IK(k+1)H(k+1)]P(k+1|k) I is the identity matrix of the same dimension as P(k+1|k).

3. The method for short-range measurement and tracking of large space targets by space-based radar as described in claim 2, characterized in that, The determination of the validity of the target's body features based on the target's current body features and angular velocity includes: like Then the target's physical characteristics are valid, among which, denoted as angular velocity of the target, p is the target feature recognition sensitivity coefficient, and ×α(k) is the angular deviation.

4. The method for short-range measurement and tracking of large space targets by space-based radar as described in claim 3, characterized in that, The data correlation and tracking processing of target echo measurements within the time window includes: Assume the cumulative measurement set in the time window at time k is Z. k Z(k) is defined as the set of candidate echoes that fall within the target tracking gate during the time window at time k. Where, m k The target candidate echo number at this moment; Define the event: θ i (k)={z i (k) is the actual measurement of the target entity, i = 1, 2, ..., m k ; θ0(k) = {At time k, there is no actual measurement of the target body}; The i-th measurement z i (k) The conditional probability originating from the target is: β i (k)=Pr{θ i (k)|Z k }, Calculate the correlation probability of each measurement: in Among them, V i Let S(k) be the innovation of the i-th measurement, S(k) be the measurement innovation covariance, λ be the clutter spatial density, and P be the density of the clutter. G To measure the probability that the target body falls within the gate, P D The probability of target detection; The update equation for the target state under the Kalman filter is: in, Based on event θ i (k) is the conditional target state update estimate, v i (k) represents the new information corresponding to this measurement value; The covariance update equation for the target is: Among themP c (k|k)=[IK(k)H(k)]P(k|k-1), 5. The method for short-range measurement and tracking of spatially sized targets by space-based radar as described in claim 4, characterized in that, After performing data correlation and tracking processing on the target echo measurement values ​​within the time window, the method further includes: By analyzing the non-uniform distribution characteristics of the target's RCS, the target's tracking trajectory, and the set of past measurements within the time window, the relative region where the target is located is determined. The offset tracking angle is calculated based on the measurement angle with the largest polarization in the current measurement set and the target's current angle estimate, as well as the angle deviation between the two adjacent measurements with the largest polarization in the current measurement set. When the offset tracking angle is greater than the tracking angle error threshold, the radar beam center is deviated from the target tracking estimate by the offset tracking angle and pointed to the location of the target body, so that the radar maintains tracking of the target body.

6. The method for short-range measurement and tracking of large space targets by space-based radar as described in claim 5, characterized in that, The offset tracking angle is calculated based on the measurement angle with the largest polarization relative to the area where the target is located in the measurement set at the current moment, the estimated angle of the target at the current moment, and the angular deviation between the two adjacent measurements with the largest polarization in the measurement set at the current moment, including: Based on the formula: Calculate the offset tracking angle, where δ is the measurement angle with the largest polarization relative to the target body region in the current measurement set. This is the estimated angle of the target at the current moment.

7. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, which, when executed by the processor, implements the method of any one of claims 1 to 6.

8. A readable storage medium, characterized in that, The readable storage medium stores a computer program, which, when executed by a processor, implements the method of any one of claims 1 to 6.

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