Phase correction space-based radar target tracking method, device and equipment

Abstract

The invention provides a phase correction space-based radar target tracking method, device and equipment. The method comprises the following steps: acquiring first target data under a ground-based radar coordinate system, and converting the first target data into a space-based radar coordinate system to obtain second target data; obtaining continuous target initial phase information based on the second target data; a Kalman state transition equation is constructed based on the target initial phase information, Kalman filtering processing and phase inversion processing are sequentially carried out by adopting the Kalman state transition equation to obtain inversion data, and the inversion data are combined with the target measurement data to obtain final target data; whether a dynamic curvature compensation strategy is executed or not is determined through the final target data; when the dynamic curvature compensation strategy is executed, a preset curvature compensation item is obtained, and through the final target data and the curvature compensation item, an IMM algorithm is adopted for calculation to obtain a target movement track. Therefore, the overall tracking precision and reliability of the space-based radar system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of radar target tracking, and particularly relates to a space-based radar target tracking method with phase correction. Background Art

[0002] In the fields of space remote sensing and electronic detection, with the breakthrough of space platforms and microwave sensing technologies, spaceborne radars have become the core monitoring means of modern security systems. Their advantages of wide coverage, strong survivability, and outstanding continuous monitoring capabilities have made the demand for precise tracking of airborne targets increasingly urgent. However, in practical applications, target tracking faces two challenges: First, in complex electromagnetic environments or electronic countermeasure scenarios, radar echo signals are vulnerable to non-Gaussian noise and multiple interferences, resulting in a decline in the performance of traditional algorithms; Second, when spaceborne radars track distant targets, coordinate errors and dynamic model mismatch problems caused by the Earth's curvature and rotation effects (such as the Coriolis force) make it difficult for traditional methods to meet the high-precision tracking requirements.

[0003] The mainstream technologies in the current target tracking field are centered around the Kalman filtering system and its extended methods. Among them, the Extended Kalman Filter (EKF) linearizes nonlinear problems through the Jacobian matrix and is widely adopted because of its simple implementation. However, in strongly nonlinear scenarios, its accuracy is significantly reduced due to first-order truncation errors. The Interacting Multiple Model (IMM) filter improves the tracking robustness for maneuvering targets through multi-model probability weighted fusion and has become an important solution in engineering practice. However, its performance is still limited in complex noise environments. In recent years, the Unscented Kalman Filter (UKF) avoids explicit linearization through deterministic sampling and shows advantages in strongly nonlinear systems. However, the sharp increase in computational complexity caused by dimension expansion restricts real-time performance; Although the Particle Filter (PF) can handle non-Gaussian problems, particle degeneracy and computational bottlenecks limit its large-scale application. In addition, the fusion of deep learning and filtering technologies (such as neural network-assisted Kalman filtering) improves adaptability in specific scenarios, but the balance between model complexity and real-time performance remains a key challenge.

[0004] The existing technologies still face multiple bottlenecks in practical applications. First, in non-linear and strong noise scenarios, the first-order linearization error of the EKF will be significantly amplified in a strongly non-linear target motion model, resulting in the deviation or even loss of the tracking trajectory; although the IMM improves robustness through multi-model fusion, it is still prone to track interruption or mis-tracking problems in complex electromagnetic interference or non-Gaussian noise environments. Second, for the long-distance target tracking of spaceborne radars, traditional methods are based on the flat-earth assumption. When directly converting the spherical coordinates measured by the radar into Cartesian coordinates, the projection error caused by the earth's curvature increases exponentially with the distance. At the same time, the dynamic effects of the Coriolis force and centrifugal force caused by the earth's rotation on the target motion state have not been fully compensated in the state equation modeling of the existing technologies, further exacerbating the tracking error. In addition, the number of sampling points of the UKF increases exponentially with the system dimension, the PF needs to maintain a large-scale particle set to maintain accuracy, and although the deep learning fusion method improves performance but increases the system burden, it is difficult to meet the real-time requirements of resource-constrained platforms such as spaceborne radars. Finally, existing methods mostly rely on preset motion models (such as CV, CT), and their adaptability to unknown maneuvering modes or complex electromagnetic environments is limited, resulting in insufficient generalization of the algorithm in dynamic scenarios, further restricting the accuracy and reliability of target tracking. Summary of the Invention

[0005] To solve the above problems existing in the prior art, the present invention provides a space-based radar target tracking method, device, and equipment with phase correction.

[0006] The technical problems to be solved by the present invention are realized through the following technical solutions:

[0007] In a first aspect, the present invention provides a space-based radar target tracking method with phase correction, including:

[0008] Obtain first target data, and convert the first target data into the space-based radar coordinate system to obtain second target data; wherein, the first target data is the target data information in the ground-based radar coordinate system, and the second target data is the target data information in the space-based radar coordinate system;

[0009] Perform down-conversion processing, quadrature demodulation processing, and phase unwrapping processing on the second target data in sequence to obtain continuous target initial phase information;

[0010] Construct a Kalman state transition equation based on the target initial phase information, and perform Kalman filtering processing and phase inversion processing in sequence using the Kalman state transition equation to obtain inversion data, and combine the inversion data with the target measurement data to obtain the final target data;

[0011] Determine whether to execute the dynamic curvature compensation strategy through the final target data;

[0012] When implementing the dynamic curvature compensation strategy, obtain the preset curvature compensation term, and calculate the target motion trajectory by using the IMM algorithm based on the final target data and the curvature compensation term;

[0013] Among them, the preset curvature compensation term is set based on the force exerted on the target by the earth's inertial force.

[0014] Optionally, perform down-conversion processing, quadrature demodulation processing, and phase unwrapping processing on the second target data in sequence to obtain continuous target initial phase information, including:

[0015] Perform down-conversion processing on the second target data to obtain the intermediate-frequency signal of the target;

[0016] Perform quadrature demodulation processing on the intermediate-frequency signal of the target to obtain the baseband complex signal;

[0017] Extract the baseband phase in the baseband complex signal, and perform phase unwrapping processing on the baseband phase to obtain continuous target initial phase information.

[0018] Optionally, construct a Kalman state transition equation based on the target initial phase information, and perform Kalman filtering processing and phase inversion processing in sequence by using the Kalman state transition equation to obtain inversion data, and combine the inversion data with the target measurement data to obtain the final target data, including:

[0019] Construct a Kalman state transition equation and an observation equation through the target initial phase information;

[0020] Based on the Kalman state transition equation and the observation equation, use the Kalman filtering algorithm to obtain the filtered target phase information;

[0021] Perform phase inversion processing on the filtered target phase information to obtain the inverted target velocity information and the inverted target position information; among them, the inverted target velocity information and the inverted target position information constitute the inversion data;

[0022] Calculate the variances of the inverted target velocity information and the target measurement velocity information respectively to obtain the first velocity variance and the second velocity variance, and calculate the covariance between the inverted target velocity information and the target measurement velocity information to obtain the velocity covariance; among them, the target measurement velocity information is the velocity information of the target corresponding to the target measurement data;

[0023] Calculate the variances of the inverted target position information and the target measurement position information respectively to obtain the first position variance and the second position variance, and calculate the covariance between the inverted target position information and the target measurement position information to obtain the position covariance; among them, the target measurement position information is the position information of the target corresponding to the target measurement data;

[0024] The first velocity weighting coefficient and the second velocity weighting coefficient are calculated using the first velocity variance, the second velocity variance, and the velocity covariance;

[0025] The first position weighting coefficient and the second position weighting coefficient are calculated using the first position variance, the second position variance, and the position covariance;

[0026] The first velocity weighting coefficient is multiplied by the target measured velocity information, and the second velocity weighting coefficient is multiplied by the inverted target velocity information, and the sum of the two is used to obtain the final target velocity information;

[0027] The first position weighting coefficient is multiplied by the target measured position information, and the second position weighting coefficient is multiplied by the inverted target position information, and the sum of the two is used to obtain the final target position information;

[0028] The final target velocity information and the final target position information together constitute the final target data;

[0029] Among them, the first velocity weighting coefficient and the second velocity weighting coefficient are respectively expressed as:

[0030]

[0031] α v represents the first velocity weighting coefficient, β v represents the second velocity weighting coefficient, represents the first velocity variance, represents the second velocity variance, Cov(v A ,v B ) represents the velocity covariance, v A represents the target measured velocity information, v B represents the inverted target velocity information.

[0032] Optionally, the final target velocity information is expressed as:

[0033] v final =α v ·v A +β v ·v B ;

[0034] The final target position information is expressed as:

[0035] (x final ,y final )=α1·(x A ,y A )+β1·(x B ,y B );

[0036] Among them, v finalIndicates the final target speed information, (x final , y final ) indicates the final target position information, x final represents the abscissa of the final target position information, y final represents the ordinate of the final target position information, α v represents the first speed weighting coefficient, β v represents the second speed weighting coefficient, v A represents the target measured speed information, v B represents the inverted target speed information, α1 represents the first position weighting coefficient, β1 represents the second position weighting coefficient, (x A , y A ) represents the target measured position information, (x B , y B ) represents the inverted target position information, x A represents the abscissa of the target measured position information, y A represents the ordinate of the target measured position information, x B represents the abscissa of the inverted target position information, y B represents the ordinate of the inverted target position information.

[0037] Optionally, determine whether to execute the dynamic curvature compensation strategy based on the final target data, including:

[0038] Convert the final target data to the spherical coordinate system to obtain the third target data;

[0039] Judge whether the distance between the target position in the third target data and the origin of the spherical coordinate system is greater than a preset distance threshold;

[0040] When the distance between the target position in the third target data and the origin of the spherical coordinate system is greater than or equal to the preset distance threshold, determine to execute the dynamic curvature compensation strategy.

[0041] Optionally, after judging whether the distance between the target position in the third target data and the origin of the spherical coordinate system is greater than the preset distance threshold, it further includes:

[0042] When the distance between the target position in the third target data and the origin of the spherical coordinate system is less than the preset distance threshold, directly use the final target data combined with the IMM algorithm to calculate the target motion trajectory.

[0043] Optionally, the preset curvature compensation term is expressed as:

[0044]

[0045] k represents the preset curvature compensation term, represents the target distance dimension acceleration curvature compensation term, represents the target azimuth - dimension acceleration curvature compensation term, represents the target elevation - dimension acceleration curvature compensation term; r represents the distance from the ground - based radar to the target, represents the first - order derivative between the target azimuth angle θ and time in the spherical coordinate system, δ represents the target elevation angle in the spherical coordinate system, represents the first - order derivative between δ and time, represents the first - order derivative between r and time.

[0046] Optionally, when implementing the dynamic curvature compensation strategy, obtain a preset curvature compensation term, and calculate the target motion trajectory using the IMM algorithm through the final target data and the curvature compensation term, including:

[0047] When implementing the dynamic curvature compensation strategy, obtain a preset curvature compensation term;

[0048] Multiply the preset curvature compensation term by an empirical coefficient and add it to the identity matrix to obtain a curvature compensation value;

[0049] Multiply the curvature compensation value by the state - transition matrix of the final target data to obtain a curvature - compensated state - transition matrix;

[0050] Use the curvature - compensated state - transition matrix and calculate the target motion trajectory using the IMM algorithm.

[0051] In a second aspect, the present invention provides a space - based radar target tracking device with phase correction. The space - based radar target tracking device with phase correction includes: a coordinate conversion unit, a phase information acquisition unit, a Kalman filtering unit, a judgment unit, and a curvature compensation unit;

[0052] The coordinate conversion unit is configured to: obtain first - target data and convert the first - target data into the space - based radar coordinate system to obtain second - target data; wherein, the first - target data is the target data information in the ground - based radar coordinate system, and the second - target data is the target data information in the space - based radar coordinate system;

[0053] The phase information acquisition unit is configured to: perform down - conversion processing, quadrature demodulation processing, and phase unwrapping processing on the second - target data in sequence to obtain continuous target initial phase information;

[0054] The Kalman filtering unit is configured to: construct a Kalman state - transition equation based on the target initial phase information, and perform Kalman filtering processing and phase inversion processing in sequence using the Kalman state - transition equation to obtain inversion data, and combine the inversion data with the target measurement data to obtain final target data;

[0055] The judgment unit is configured to: determine whether to implement the dynamic curvature compensation strategy through the final target data;

[0056] The curvature compensation unit is used for: when implementing the dynamic curvature compensation strategy, obtaining a preset curvature compensation term, and calculating a target motion trajectory by using the IMM algorithm based on the final target data and the curvature compensation term;

[0057] Wherein, the preset curvature compensation term is set based on the acting force of the earth's inertial force on the target.

[0058] In a third aspect, the present invention provides a space-based radar target tracking device with phase correction, including: a processor, a storage medium and a bus. The storage medium stores machine-readable instructions executable by the processor. When the space-based radar target tracking device with phase correction runs, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the space-based radar target tracking method with phase correction as described in the first aspect above.

[0059] The present invention provides a method, device and equipment for space-based radar target tracking with phase correction. Among them, a method for space-based radar target tracking with phase correction includes: acquiring first target data and converting the first target data into the space-based radar coordinate system to obtain second target data; wherein, the first target data is the target data information in the ground-based radar coordinate system, and the second target data is the target data information in the space-based radar coordinate system; performing down-conversion processing, quadrature demodulation processing and phase unwrapping processing on the second target data in sequence to obtain continuous target initial phase information; constructing a Kalman state transition equation based on the target initial phase information, and performing Kalman filtering processing and phase inversion processing in sequence using the Kalman state transition equation to obtain inversion data, and combining the inversion data with the target measurement data to obtain final target data; determining whether to execute a dynamic curvature compensation strategy through the final target data; when executing the dynamic curvature compensation strategy, acquiring a preset curvature compensation term, and calculating the target motion trajectory by using the IMM algorithm through the final target data and the curvature compensation term; wherein, the preset curvature compensation term is set based on the acting force of the earth's inertial force on the target. In the present invention, first, through the coordinate conversion between the first target data and the second target data, the second target data closer to the radar observation perspective can be obtained. This process eliminates the earth curvature error introduced by coordinate conversion in the traditional method, improves the spatial positioning accuracy of long-distance targets, and thus alleviates the exponentially increasing projection error problem caused by the flat earth assumption. Secondly, the second target data is sequentially subjected to down-conversion, quadrature demodulation and phase unwrapping processing to extract high-precision initial phase information. This refined processing method based on phase information improves the measurement stability in complex electromagnetic interference and non-Gaussian noise environments, provides a more reliable state estimation basis for subsequent filtering, and enhances the robustness of the system to strong noise scenarios. Further, a Kalman state transition equation is constructed based on the extracted target initial phase information, and combined with Kalman filtering and phase inversion processing to obtain the final target data. This step realizes the effective fusion of dynamic modeling and observation data, avoids the error amplification problem caused by first-order linearization of EKF, and at the same time has lower computational complexity compared with methods such as UKF and PF, and is more suitable for the resource-constrained environment of spaceborne platforms. Finally, it is judged whether to execute the dynamic curvature compensation strategy according to the final target data, and when necessary, a preset curvature compensation term is introduced, and the IMM algorithm is combined for multi-model fusion calculation to obtain the target motion trajectory. This strategy not only effectively compensates the influence of the Coriolis force and centrifugal force caused by the earth's rotation, but also improves the adaptability to target maneuvering behaviors through the IMM algorithm, and solves the problem of insufficient generalization ability of traditional fixed models such as CV and CT in unknown maneuvering scenarios. In summary, the present invention improves the overall tracking accuracy and reliability of the space-based radar system, and meets the urgent needs of modern space surveillance tasks for high performance and high stability.

[0060] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. Description of the Drawings

[0061] Figure 1 It is a schematic flowchart of a space-based radar target tracking method with phase correction provided by an embodiment of the present invention;

[0062] Figure 2 It exemplarily shows an error comparison result graph of the EKF filtering algorithm based on the method of the present invention, the existing IMM algorithm, and the traditional single model;

[0063] Figure 3 It is a schematic structural diagram of a space-based radar target tracking device with phase correction provided by an embodiment of the present invention;

[0064] Figure 4 It is a schematic structural diagram of a space-based radar target tracking device with phase correction provided by an embodiment of the present invention. Detailed Embodiments

[0065] The present invention will be further described in detail below in conjunction with specific embodiments, but the embodiments of the present invention are not limited thereto.

[0066] In order to improve the overall tracking accuracy and reliability of the space-based radar system, an embodiment of the present invention provides a space-based radar target tracking method with phase correction. Figure 1 It is a schematic flowchart of a space-based radar target tracking method with phase correction provided by an embodiment of the present invention, as Figure 1 shown, including:

[0067] S101. Obtain first target data, and transform the first target data into the space-based radar coordinate system to obtain second target data.

[0068] Among them, the first target data is the target data information in the ground-based radar coordinate system, and the second target data is the target data information in the space-based radar coordinate system. In addition, the space-based radar coordinate system is also called the geocentric inertial coordinate system.

[0069] The second target data is expressed as:

[0070]

[0071] s RF (t) represents the second target data corresponding to the continuous-domain time t, A represents the amplitude information of the second target data, j represents the imaginary unit, f0 represents the carrier frequency of the ground-based radar, represents the signal time delay, r represents the distance from the ground-based radar to the target, c is the speed of light, f d is the Doppler frequency shift, and n(t) represents the environmental noise corresponding to the continuous-domain time t.

[0072] S102. Perform down-conversion processing, quadrature demodulation processing, and phase unwrapping processing on the second target data in sequence to obtain continuous target initial phase information.

[0073] Optionally, S102 may specifically include:

[0074] Perform down-conversion processing on the second target data to obtain a target intermediate-frequency signal;

[0075] Perform quadrature demodulation processing on the target intermediate-frequency signal to obtain a baseband complex signal;

[0076] Extract the baseband phase in the baseband complex signal, and perform phase unwrapping processing on the baseband phase to obtain continuous target initial phase information.

[0077] The target intermediate-frequency signal is expressed as:

[0078]

[0079] s IF (t) represents the target intermediate-frequency signal corresponding to the continuous-domain time t, j represents the imaginary unit, and f0 represents the carrier frequency of the ground-based radar.

[0080] The baseband phase is expressed as:

[0081] φ'(t) = -2πf0τ + 2πf d t + φ noise ;

[0082] φ'(t) is the baseband phase corresponding to the continuous-domain time t, and φ noise represents the baseband phase noise.

[0083] Since the output range of the arctan function is (-π, π), there may be a 2π jump in the actual phase. Therefore, in this embodiment, the continuous phase is restored through unwrapping.

[0084] Specifically, first perform discretization processing on the time-domain baseband phase to obtain the discretized baseband phase, and then perform phase unwrapping processing on the discretized phase information to obtain continuous target initial phase information.

[0085] The process of phase unwrapping processing can be described as:

[0086] Calculate the adjacent baseband phase Δ'φ: Δ'φ = φ'(k) - φ'(k - 1);

[0087] where φ'(k) represents the baseband phase corresponding to the kth moment, and φ'(k - 1) represents the baseband phase corresponding to the (k - 1)th moment.

[0088] If |Δ'φ| > π, correct Δ'φ to Δ'φ - 2π·sign(Δ'φ), where sign(·) represents a piecewise function, and then accumulate the corrected phase difference, the continuous target initial phase information. sign(·) is represented as follows:

[0089]

[0090] S103. Construct a Kalman state transition equation based on the target initial phase information, and perform Kalman filtering processing and phase inversion processing successively using the Kalman state transition equation to obtain inversion data, and combine the inversion data with the target measurement data to obtain the final target data.

[0091] Optionally, S103 may specifically include:

[0092] Construct a Kalman state transition equation and an observation equation through the target initial phase information;

[0093] Based on the Kalman state transition equation and the observation equation, use the Kalman filtering algorithm to obtain the filtered target phase information;

[0094] Perform phase inversion processing on the filtered target phase information to obtain the inverted target velocity information and the inverted target position information; where the inverted target velocity information and the inverted target position information constitute the inversion data;

[0095] Calculate the variances of the inverted target velocity information and the target measurement velocity information respectively to obtain the first velocity variance and the second velocity variance, and calculate the covariance between the inverted target velocity information and the target measurement velocity information to obtain the velocity covariance; where the target measurement velocity information is the velocity information of the target corresponding to the target measurement data;

[0096] Calculate the variances of the inverted target position information and the target measurement position information respectively to obtain the first position variance and the second position variance, and calculate the covariance between the inverted target position information and the target measurement position information to obtain the position covariance; where the target measurement position information is the position information of the target corresponding to the target measurement data;

[0097] Use the first velocity variance, the second velocity variance, and the velocity covariance to calculate the first velocity weighting coefficient and the second velocity weighting coefficient;

[0098] Use the first position variance, the second position variance, and the position covariance to calculate the first position weighting coefficient and the second position weighting coefficient;

[0099] Multiply the first velocity weighting coefficient by the target measurement velocity information, multiply the second velocity weighting coefficient by the inverted target velocity information, and sum the two to obtain the final target velocity information;

[0100] Multiply the first position weighting coefficient by the target measurement position information, multiply the second position weighting coefficient by the inverted target position information, and sum the two to obtain the final target position information;

[0101] Construct the final target data from the final target velocity information and the final target position information.

[0102] It should be noted that in this embodiment, the target measurement data can be the data information of the target directly obtained by the space-based radar.

[0103] The Kalman state transition equation is expressed as:

[0104]

[0105] represents the Kalman state transition equation corresponding to the k-th moment, represents the Kalman state transition equation corresponding to the (k - 1)-th moment, and B represents the state transition coefficient;

[0106] Among them,

[0107]

[0108]

[0109] Among them, φ(k) represents the target initial phase information corresponding to the k-th moment, φ(k - 1) represents the target initial phase information corresponding to the (k - 1)-th moment, φ(k - 2) represents the target initial phase information corresponding to the (k - 2)-th moment, and Δt represents the time difference between φ(k - 1) and φ(k - 2).

[0110] The observation equation is expressed as:

[0111]

[0112] represents the observation equation corresponding to the k-th moment, Q represents the observation coefficient, V k represents the observation noise corresponding to the k-th moment.

[0113] Based on the Kalman state transition equation and the observation equation, the Kalman filtering algorithm is used to obtain the filtered target phase information

[0114] Using the filtered target phase information Invert to obtain the inverted target velocity information v B and the inverted target position information (x B , y B ), and the process is as follows:

[0115] First, calculate the radial velocity v of the target based on the following formula r , denotes the result of taking the first derivative with respect to time t.

[0116]

[0117] v x denotes the lateral velocity of the inverted target velocity information, v y denotes the longitudinal velocity of the inverted target velocity information. Through v x and v y together constitute the inverted target velocity information v B .

[0118] Since there is a relationship: where λ represents the wavelength, the inverted target position information (x B , y B ) can be obtained.

[0119] After obtaining the inverted target velocity information v B , the calculation processes of the first velocity weighting coefficient and the second velocity weighting coefficient are taken as examples to illustrate as follows:

[0120] The first velocity weighting coefficient and the second velocity weighting coefficient are respectively expressed as:

[0121]

[0122] α v denotes the first velocity weighting coefficient, β v denotes the second velocity weighting coefficient, denotes the first velocity variance, denotes the second velocity variance, Cov(v A , v B ) denotes the velocity covariance, v A denotes the target measured velocity information, v B denotes the inverted target velocity information.

[0123] Optionally, the final target velocity information is expressed as:

[0124] v final = α v · v A + β v · v B ;

[0125] The final target position information is expressed as:

[0126] (x final , y final ) = α1·(xA , y A ) + β1·(x B , y B );

[0127] Among them, v final represents the final target speed information, (x final , y final ) represents the final target position information, x final represents the abscissa of the final target position information, y final represents the ordinate of the final target position information, α v represents the first speed weighting coefficient, β v represents the second speed weighting coefficient, v A represents the target measured speed information, v B represents the inverted target speed information, α1 represents the first position weighting coefficient, β1 represents the second position weighting coefficient, (x A , y A ) represents the target measured position information, (x B , y B ) represents the inverted target position information, x A represents the abscissa of the target measured position information, y A represents the ordinate of the target measured position information, x B represents the abscissa of the inverted target position information, y B represents the ordinate of the inverted target position information.

[0128] It should be noted that the acquisition of the first position weighting coefficient and the second position weighting coefficient is similar to the acquisition process of the first speed weighting coefficient and the second speed weighting coefficient mentioned above, and thus will not be elaborated in this embodiment.

[0129] S104. Determine whether to execute the dynamic curvature compensation strategy based on the final target data.

[0130] Optionally, S104 may specifically include:

[0131] Convert the final target data to the spherical coordinate system to obtain the third target data;

[0132] Determine whether the distance between the target position in the third target data and the origin of the spherical coordinate system is greater than a preset distance threshold;

[0133] When the distance between the target position in the third target data and the origin of the spherical coordinate system is greater than or equal to the preset distance threshold, determine to execute the dynamic curvature compensation strategy.

[0134] Optionally, after determining whether the distance between the target position in the third target data and the origin of the spherical coordinate system is greater than a preset distance threshold, it further includes:

[0135] When the distance between the target position in the third target data and the origin of the spherical coordinate system is less than the preset distance threshold, directly use the final target data and combine it with the IMM algorithm to calculate the target motion trajectory.

[0136] S105. When implementing the dynamic curvature compensation strategy, obtain a preset curvature compensation term, and use the final target data and the curvature compensation term to calculate the target motion trajectory by means of the IMM algorithm.

[0137] Among them, the preset curvature compensation term is set based on the acting force of the earth's inertial force on the target.

[0138] The derivation process of the preset curvature compensation term is as follows:

[0139] Express the unit vectors of the spherical coordinate system as: Among them, represents the distance unit vector, represents the azimuth angle unit vector, represents the elevation angle unit vector.

[0140] Let the position vector of the target in the inertial system be Then the target velocity is That is, the target velocity is the derivative of the position vector with respect to time t.

[0141]

[0142] Differentiate with respect to time to obtain the acceleration

[0143] Performing vector differential operations on the above formula gives:

[0144]

[0145] Among them, r represents the distance from the ground radar to the target, θ represents the target azimuth angle in the spherical coordinate system, δ represents the target elevation angle in the spherical coordinate system, represents the first derivative between θ and time, represents the first derivative between δ and time, represents the first derivative between r and time, represents the second derivative between r and time, represents the second derivative between θ and time, represents the second derivative between δ and time,

[0146] Since in the spherical coordinate system, the Earth will generate additional inertial force terms on the target, and these forces will affect the acceleration of the target. To compensate for this effect, it can be assumed that the target moves in a uniform straight line in the spherical coordinate system. Then the acceleration components observed in the spherical coordinate system must be zero.

[0147] That is, it is expressed as:

[0148]

[0149] After transposing the above formula, the initial curvature compensation term is obtained. They are respectively expressed as follows:

[0150]

[0151] Represents the initial curvature compensation term of the target range dimension acceleration. Represents the initial curvature compensation term of the target azimuth dimension acceleration. Represents the initial curvature compensation term of the target elevation dimension acceleration.

[0152] To simplify the calculation, in this embodiment, the high-order terms are ignored, and finally the preset curvature compensation term k is obtained.

[0153] The preset curvature compensation term k is expressed as:

[0154]

[0155] k represents the preset curvature compensation term. Represents the curvature compensation term of the target range dimension acceleration. Represents the curvature compensation term of the target azimuth dimension acceleration. Represents the curvature compensation term of the target elevation dimension acceleration; r represents the distance from the ground radar to the target. Represents the first derivative between the target azimuth angle θ and time in the spherical coordinate system, δ represents the target elevation angle in the spherical coordinate system. Represents the first derivative between δ and time. Represents the first derivative between r and time.

[0156] In addition, in this embodiment, the preset distance threshold can be set to 200 Km, that is, when the distance between the target position in the third target data and the origin of the spherical coordinate system is greater than or equal to 200 Km, it is determined to execute the dynamic curvature compensation strategy.

[0157] Optionally, when executing the dynamic curvature compensation strategy, obtain the preset curvature compensation term, and calculate the target motion trajectory through the final target data and the curvature compensation term by using the IMM algorithm, including:

[0158] When implementing the dynamic curvature compensation strategy, obtain the preset curvature compensation term;

[0159] Multiply the preset curvature compensation term by the empirical coefficient and add it to the identity matrix to obtain the curvature compensation value;

[0160] Multiply the curvature compensation value by the state transition matrix of the final target data to obtain the state transition matrix after curvature compensation;

[0161] Using the state transition matrix after curvature compensation, calculate the target motion trajectory by using the IMM algorithm.

[0162] In this embodiment, the empirical coefficient is expressed as:

[0163]

[0164] represents the empirical coefficient, r represents the distance from the target to the center of the earth, R e represents the equivalent curvature radius of the earth (usually taking the average curvature radius of the WGS84 ellipsoid, R e ≈6371 km), and h represents the target altitude.

[0165] In addition, in order to verify the effectiveness of the method of the present invention, Figure 2 exemplarily shows the error comparison result graph of the EKF filtering algorithm based on the method of the present invention, the existing IMM algorithm, and the traditional single model, as Figure 2 shown. Model 1 is the mean square error of the EKF filtering using only the constant velocity (CV) model; Model 2 is the mean square error of the EKF filtering using only the constant acceleration (CA) model; Model 3 is the mean square error of the EKF filtering using only the constant turn (CT) model. It can Figure 2 be seen that the RMSE (root mean square error) of the method of the present invention is the smallest, verifying the robustness and effectiveness of the method of the present invention.

[0166] An embodiment of the present invention provides a space-based radar target tracking method with phase correction, including: obtaining first target data and converting the first target data into the space-based radar coordinate system to obtain second target data; wherein, the first target data is target data information in the ground-based radar coordinate system, and the second target data is target data information in the space-based radar coordinate system; performing down-conversion processing, quadrature demodulation processing, and phase unwrapping processing on the second target data in sequence to obtain continuous target initial phase information; constructing a Kalman state transition equation based on the target initial phase information, and performing Kalman filtering processing and phase inversion processing in sequence using the Kalman state transition equation to obtain inversion data, and combining the inversion data with target measurement data to obtain final target data; determining whether to execute a dynamic curvature compensation strategy through the final target data; when executing the dynamic curvature compensation strategy, obtaining a preset curvature compensation term, and calculating the target motion trajectory using the IMM algorithm through the final target data and the curvature compensation term; wherein, the preset curvature compensation term is set based on the acting force of the earth's inertial force on the target. In the present invention, first, through the coordinate conversion between the first target data and the second target data, the second target data closer to the radar observation perspective can be obtained. This process eliminates the earth curvature error introduced by coordinate conversion in the traditional method, improves the spatial positioning accuracy of long-distance targets, and thus alleviates the exponentially increasing projection error problem caused by the flat earth assumption. Secondly, the second target data is sequentially subjected to down-conversion, quadrature demodulation, and phase unwrapping processing to extract high-precision initial phase information. This refined processing method based on phase information improves the measurement stability in complex electromagnetic interference and non-Gaussian noise environments, provides a more reliable state estimation basis for subsequent filtering, and enhances the robustness of the system to strong noise scenarios. Further, a Kalman state transition equation is constructed based on the extracted target initial phase information, and combined with Kalman filtering and phase inversion processing to obtain the final target data. This step realizes the effective fusion of dynamic modeling and observation data, avoids the error amplification problem caused by first-order linearization of EKF, and at the same time has lower computational complexity compared with methods such as UKF and PF, and is more suitable for the resource-constrained environment of spaceborne platforms. Finally, it is judged whether to execute the dynamic curvature compensation strategy according to the final target data, and when necessary, a preset curvature compensation term is introduced, and the IMM algorithm is combined for multi-model fusion calculation to obtain the target motion trajectory. This strategy not only effectively compensates for the influence of the Coriolis force and centrifugal force caused by the earth's rotation, but also improves the adaptability to target maneuvering behaviors through the IMM algorithm, and solves the problem of insufficient generalization ability of traditional fixed models such as CV and CT in unknown maneuvering scenarios. In summary, the present invention improves the overall tracking accuracy and reliability of the space-based radar system, and meets the urgent needs of modern space surveillance tasks for high performance and high stability.

[0167] The method provided by the embodiments of the present invention can be applied to an electronic device. Specifically, the electronic device can be: a desktop computer, a portable computer, a smart mobile terminal, a server, etc., which are not limited in the embodiments of the present invention.

[0168] Based on the same inventive concept, the embodiments of the present invention also provide a space-based radar target tracking device with phase correction. Figure 3 As shown in the structural schematic diagram of a space-based radar target tracking device with phase correction provided by the embodiments of the present invention, Figure 3 as shown, it includes: a coordinate conversion unit 301, a phase information acquisition unit 302, a Kalman filter unit 303, a judgment unit 304, and a curvature compensation unit 305;

[0169] The coordinate conversion unit 301 is used to: acquire first target data, and convert the first target data into the space-based radar coordinate system to obtain second target data; wherein, the first target data is the target data information in the ground-based radar coordinate system, and the second target data is the target data information in the space-based radar coordinate system;

[0170] The phase information acquisition unit 302 is used to: perform down-conversion processing, quadrature demodulation processing, and phase unwrapping processing on the second target data in sequence to obtain continuous target initial phase information;

[0171] The Kalman filter unit 303 is used to: construct a Kalman state transition equation based on the target initial phase information, and perform Kalman filtering processing and phase inversion processing in sequence using the Kalman state transition equation to obtain inversion data, and combine the inversion data with the target measurement data to obtain the final target data;

[0172] The judgment unit 304 is used to: determine whether to execute the dynamic curvature compensation strategy through the final target data;

[0173] The curvature compensation unit 305 is used to: when the dynamic curvature compensation strategy is executed, acquire a preset curvature compensation term, and calculate the target motion trajectory using the IMM algorithm through the final target data and the curvature compensation term;

[0174] Wherein, the preset curvature compensation term is set based on the acting force of the earth's inertial force on the target.

[0175] Figure 4A schematic structural diagram of a space-based radar target tracking device with phase correction provided by an embodiment of the present invention includes: a processor 410, a storage medium 420, and a bus 430. The storage medium 420 stores machine-readable instructions executable by the processor 410. When the space-based radar target tracking device with phase correction runs, the processor 410 communicates with the storage medium 420 through the bus 430, and the processor 410 executes the machine-readable instructions to perform the steps of the above method embodiment. The specific implementation manners and technical effects are similar and will not be elaborated here.

[0176] The storage medium may include a Random Access Memory (RAM), or may also include a Non-Volatile Memory (NVM), such as at least one disk memory. Optionally, the storage medium may also be at least one storage device located far from the aforementioned processor.

[0177] The above-mentioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processing (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0178] It should be noted that the terms "first", "second", etc. are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present invention. On the contrary, they are only examples of devices and methods consistent with some aspects of the present invention.

[0179] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc., mean that the specific features or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification.

[0180] Although the present invention has been described in connection with various embodiments herein, however, in the process of implementing the claimed invention, those skilled in the art can understand and implement other variations of the above-disclosed embodiments by viewing the drawings and the disclosure. In the description of this specification, the term "including" does not exclude other components or steps, the term "a" or "an" does not exclude a plurality of cases, and the meaning of "a plurality" is two or more, unless otherwise specifically defined. In addition, certain measures are described in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0181] The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A space-based radar target tracking method with phase correction, characterized in that, Including: Obtain the first target data and transform the first target data into the space-based radar coordinate system to obtain the second target data; wherein, the first target data is the target data information in the ground-based radar coordinate system, and the second target data is the target data information in the space-based radar coordinate system; Perform down-conversion processing, quadrature demodulation processing, and phase unwrapping processing on the second target data in sequence to obtain continuous target initial phase information; Construct a Kalman state transition equation based on the target initial phase information, and perform Kalman filtering processing and phase inversion processing in sequence using the Kalman state transition equation to obtain inversion data, and combine the inversion data with target measurement data to obtain the final target data; Determine whether to execute the dynamic curvature compensation strategy based on the final target data; When executing the dynamic curvature compensation strategy, obtain a preset curvature compensation term, and use the IMM algorithm to calculate the target motion trajectory through the final target data and the curvature compensation term; Wherein, the preset curvature compensation term is set based on the acting force of the earth's inertial force on the target.

2. The phase-corrected space-based radar target tracking method according to claim 1, wherein The performing down-conversion processing, quadrature demodulation processing, and phase unwrapping processing on the second target data in sequence to obtain continuous target initial phase information includes: Perform down-conversion processing on the second target data to obtain an intermediate-frequency signal of the target; Perform quadrature demodulation processing on the intermediate-frequency signal of the target to obtain a baseband complex signal; Extract the baseband phase in the baseband complex signal, and perform phase unwrapping processing on the baseband phase to obtain the continuous target initial phase information.

3. The phase correction space-based radar target tracking method according to claim 1, characterized in that The constructing a Kalman state transition equation based on the target initial phase information, and performing Kalman filtering processing and phase inversion processing in sequence using the Kalman state transition equation to obtain inversion data, and combining the inversion data with target measurement data to obtain the final target data includes: Construct a Kalman state transition equation and an observation equation through the target initial phase information; Based on the Kalman state transition equation and the observation equation, use the Kalman filtering algorithm to obtain the filtered target phase information; Perform phase inversion processing on the filtered target phase information to obtain the inverted target velocity information and the inverted target position information; wherein the inverted target velocity information and the inverted target position information constitute the inversion data; Calculate the variances of the inverted target velocity information and the target measurement velocity information respectively to obtain the first velocity variance and the second velocity variance, and calculate the covariance between the inverted target velocity information and the target measurement velocity information to obtain the velocity covariance; wherein, the target measurement velocity information is the velocity information of the target corresponding to the target measurement data; Calculate the variances of the inverted target position information and the target measurement position information respectively to obtain the first position variance and the second position variance, and calculate the covariance between the inverted target position information and the target measurement position information to obtain the position covariance; wherein, the target measurement position information is the position information of the target corresponding to the target measurement data; Calculate a first velocity weighting coefficient and a second velocity weighting coefficient by using the first velocity variance, the second velocity variance, and the velocity covariance; Calculate a first position weighting coefficient and a second position weighting coefficient by using the first position variance, the second position variance, and the position covariance; Multiply the first velocity weighting coefficient by the target measured velocity information, multiply the second velocity weighting coefficient by the inverted target velocity information, and sum the two to obtain the final target velocity information; Multiply the first position weighting coefficient by the target measured position information, multiply the second position weighting coefficient by the inverted target position information, and sum the two to obtain the final target position information; The final target velocity information and the final target position information together constitute the final target data; Wherein, the first velocity weighting coefficient and the second velocity weighting coefficient are respectively expressed as: α v represents the first speed weighting coefficient, β v represents the second speed weighting coefficient, represents the first speed variance, represents the second speed variance, Cov(v A ,v B ) represents the speed covariance, v A represents the target measurement speed information, v B represents the inverted target speed information.

4. The phase-corrected space-based radar target tracking method according to claim 3, wherein The final target velocity information is expressed as: v final = α v ·v A + β v ·v B ; The final target position information is expressed as: (x final ,y final ) = α1·(x A ,y A ) + β1·(x B ,y B ); Among them, v final represents the final target speed information, (x final , y final ) represents the final target position information, x final represents the abscissa of the final target position information, y final represents the ordinate of the final target position information, α v represents the first speed weighting coefficient, β v represents the second speed weighting coefficient, v A represents the target measured speed information, v B represents the inverted target speed information, α1 represents the first position weighting coefficient, β1 represents the second position weighting coefficient, (x A , y A ) represents the target measured position information, (x B , y B ) represents the inverted target position information, x A represents the abscissa of the target measured position information, y A represents the ordinate of the target measured position information, x B represents the abscissa of the inverted target position information, y B represents the ordinate of the inverted target position information.

5. The phase correction space-based radar target tracking method according to claim 4, characterized in that Determining whether to execute a dynamic curvature compensation strategy based on the final target data includes: Converting the final target data into the spherical coordinate system to obtain third target data; Judging whether the distance between the target position in the third target data and the origin of the spherical coordinate system is greater than a preset distance threshold; When the distance between the target position in the third target data and the origin of the spherical coordinate system is greater than or equal to the preset distance threshold, determine to execute the dynamic curvature compensation strategy.

6. The phase-corrected space-based radar target tracking method according to claim 5, wherein After judging whether the distance between the target position in the third target data and the origin of the spherical coordinate system is greater than the preset distance threshold, it further includes: When the distance between the target position in the third target data and the origin of the spherical coordinate system is less than the preset distance threshold, directly use the final target data combined with the IMM algorithm to calculate the target motion trajectory.

7. The phase-corrected space-based radar target tracking method according to claim 1, wherein The preset curvature compensation term is expressed as: k represents a preset curvature compensation term, represents the curvature compensation term of the acceleration in the target distance dimension, represents the curvature compensation term of the acceleration in the target azimuth dimension, represents the curvature compensation term of the acceleration in the target pitch dimension; r represents the distance from the ground radar to the target, represents the first derivative between the azimuth angle θ of the target and time in the spherical coordinate system, and δ represents the elevation angle of the target in the spherical coordinate system, represents the first derivative between δ and time, represents the first derivative between r and time.

8. The phase-corrected space-based radar target tracking method according to claim 1, characterized in that When executing the dynamic curvature compensation strategy, obtaining a preset curvature compensation term, and using the final target data and the curvature compensation term, calculating the target motion trajectory by using the IMM algorithm, includes: When executing the dynamic curvature compensation strategy, obtaining the preset curvature compensation term; Multiply the preset curvature compensation term by an empirical coefficient and add it to the identity matrix to obtain a curvature compensation value; Multiply the curvature compensation value by the state transition matrix of the final target data to obtain a curvature-compensated state transition matrix; Using the curvature-compensated state transition matrix, calculate the target motion trajectory by using the IMM algorithm.

9. A space-based radar target tracking device with phase correction, characterized in that, The phase-corrected space-based radar target tracking device includes: a coordinate conversion unit, a phase information acquisition unit, a Kalman filter unit, a judgment unit, and a curvature compensation unit; The coordinate conversion unit is configured to: obtain first target data, and convert the first target data into the space-based radar coordinate system to obtain second target data; wherein, the first target data is target data information in the ground-based radar coordinate system, and the second target data is target data information in the space-based radar coordinate system; The phase information acquisition unit is configured to: perform down-conversion processing, quadrature demodulation processing, and phase unwrapping processing on the second target data in sequence to obtain continuous target initial phase information; The Kalman filtering unit is configured to: construct a Kalman state transition equation based on the target initial phase information, and perform Kalman filtering processing and phase inversion processing in sequence using the Kalman state transition equation to obtain inversion data, and combine the inversion data with target measurement data to obtain final target data; The judgment unit is configured to: determine whether to execute the dynamic curvature compensation strategy based on the final target data; The curvature compensation unit is configured to: when the dynamic curvature compensation strategy is executed, obtain a preset curvature compensation term, and calculate the target motion trajectory using the IMM algorithm through the final target data and the curvature compensation term; Wherein, the preset curvature compensation term is set based on the acting force of the earth's inertial force on the target.

10. A space-based radar target tracking device with phase correction, characterized in that, Comprising: A processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the space-based radar target tracking device with phase correction runs, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the space-based radar target tracking method with phase correction according to any one of claims 1-8.

Images