An aircraft detection method and system based on an adaptive interactive multi-model algorithm

Abstract

The present invention discloses an aircraft detection method and system based on an adaptive interactive multi-model algorithm, belonging to the technical field of strong attack and defense confrontation. The method includes: determining a motion model during the motion process and a Markov probability transition matrix at the previous moment; estimating the state variables and model probabilities of each motion model at the previous moment; calculating the state variables, state vector covariance matrices, and model probabilities of each motion model at the next moment; performing state variable fusion and state vector covariance matrix fusion; when the detection is not over, updating the Markov probability transition matrix at the previous moment; updating the next moment to the previous moment and using the updated Markov probability transition matrix as the Markov probability transition matrix at the previous moment. The present invention can reduce the errors caused by the matching delay and lag of the motion model, improve the state estimation effect of multi-modal maneuvering targets, and greatly improve the detection accuracy of high-value targets.

Description

Technical Field

[0001] The present invention belongs to the technical field of strong attack and defense confrontation, and particularly relates to a method and system for detecting an aircraft based on an adaptive interactive multiple model algorithm. Background Art

[0002] In a strong attack and defense confrontation environment, the motion law of a high-value target within a relatively certain time period can be equivalently decomposed into multiple basic models, and it is a feasible method to adopt multimodal fitting for the complex motion process of the target. When detecting and estimating the state of a maneuvering target, since some traditional filtering and detection methods can achieve good results when the target maneuvers in a single mode, but the effect is not ideal when the target maneuvers in multiple modes.

[0003] Most of the existing research on the basic IMM algorithm adjusts the probability of the matching model through a pre-set Markov probability transition matrix, and these IMM algorithms based on certain prior knowledge and statistical laws can only adjust the probability of the matching model after a period of time when the target mode changes, and there are obvious delays and lags during the model switching process. Summary of the Invention

[0004] One of the purposes of the present invention is to provide a method for detecting an aircraft based on an adaptive interactive multiple model algorithm, which can reduce the error caused by the delay and lag of motion model matching, improve the state estimation effect of a multi-modal maneuvering target, and greatly improve the detection accuracy of high-value targets.

[0005] Another purpose of the present invention is to provide a system for detecting an aircraft based on an adaptive interactive multiple model algorithm.

[0006] In order to achieve one of the above purposes, the present invention is implemented by adopting the following technical solutions:

[0007] A method for detecting an aircraft based on an adaptive interactive multiple model algorithm, the aircraft detection method comprising:

[0008] Step S1, detecting a target by using an aircraft to obtain a motion model of the target during the motion process, and determining the motion model during the motion process and the Markov probability transition matrix at the previous moment;

[0009] Step S2, estimating the state variables and model probabilities at the previous moment of each motion model;

[0010] Step S3, using the Markov probability transition matrix and the estimation result to calculate the state variables, state vector covariance matrices and model probabilities of each motion model at the next moment;

[0011] Step S4: Use the state variables, state vector covariance matrices, and model probabilities of each motion model at the next moment to perform state variable fusion and state vector covariance matrix fusion in sequence;

[0012] Step S5: When the detection is not over, use the model probabilities of each motion model at the next moment to update the Markov probability transition matrix at the previous moment;

[0013] Step S6: Update the next moment to the previous moment, use the updated Markov probability transition matrix as the Markov probability transition matrix at the previous moment, return to Step S2, and continue the detection until the detection is completed.

[0014] Furthermore, the specific implementation process of Step S3 includes:

[0015] Step S31: Use the estimation result and the Markov probability transition matrix to calculate the initial values of the state variables and state vector covariance matrices of each motion model during filtering at the previous moment;

[0016] Step S32: Determine the residuals, residual covariances, state variables, and state vector covariance matrices of each motion model at the next moment according to the initial values of the state variables and state vector covariance matrices of each motion model during filtering at the previous moment;

[0017] Step S33: Use the model probability estimation results of each motion model at the previous moment, the Markov probability transition matrix, and the residuals and residual covariances at the next moment to calculate the model probabilities of each motion model at the next moment.

[0018] Furthermore, the specific process of Step S32 includes:

[0019] Step S321: Use the initial values of the state variables and state vector covariance matrices of each motion model during filtering at the previous moment to predict the state variables and state vector covariance matrices of each motion model at the next moment;

[0020] Step S322: Use the predicted state variables and state vector covariance matrices of each motion model at the next moment to calculate the residuals and residual covariances of each motion model at the corresponding moment to determine the filtering gain of each motion model at the corresponding moment;

[0021] Step S323: Use the residuals, residual covariances, and filtering gains of each motion model at the next moment to update the predicted results of the state variables and state vector covariance matrices of each motion model at the corresponding moment.

[0022] Furthermore, in Step S5, the specific process of the update includes:

[0023] Step S51: Calculate the error compression ratio between any two motion models by using the model probability of each motion model at the next moment and the Markov probability transition matrix at the previous moment.

[0024] Step S52: Use the error compression ratio to correct the corresponding elements in the Markov probability transition matrix at the previous moment.

[0025] Further, in the step S51, the error compression ratio is:

[0026]

[0027] where is the error compression ratio between the i th motion model and the j th motion model; is the element value corresponding to the i rd row and the j th column in the Markov probability transition matrix at the previous moment; is the element value corresponding to the j rd row and the i th column in the Markov probability transition matrix at the previous moment; and are the model probabilities of the i th motion model and the j th motion model at the k moment, respectively.

[0028] Further, in the step S52, the correction is performed according to the following formula:

[0029] ;

[0030] ;

[0031] where is the corrected element value corresponding to the i rd row and the i th column in the Markov probability transition matrix at the previous moment; is the corrected element value corresponding to the i rd row and the j th column in the Markov probability transition matrix at the previous moment; l is the exponential factor; , , , N is the number of motion models.

[0032] To achieve the second above-mentioned object, the present invention adopts the following technical solution to implement:

[0033] An aircraft detection system based on an adaptive interactive multiple model algorithm, the aircraft detection system comprising:

[0034] A detection module, configured to: detect a target using an aircraft to obtain a motion model of the target during movement, and determine the motion model during movement and the Markov probability transition matrix at the previous moment;

[0035] An estimation module, configured to: estimate the state variables and model probabilities at the previous moment of each motion model;

[0036] A calculation module, configured to: use the Markov probability transition matrix and the estimation result, and adopt the IMM algorithm to calculate the state variables, state vector covariance matrices, and model probabilities of each motion model at the next moment;

[0037] A fusion module, configured to: use the state variables, state vector covariance matrices, and model probabilities of each motion model at the next moment to perform state variable fusion and state vector covariance matrix fusion in sequence;

[0038] A judgment module, configured to: judge whether the detection is ended. If so, output the state variable fusion result and the state vector covariance matrix fusion result, and end; if not, update the Markov probability transition matrix at the previous moment using the model probabilities of each motion model at the next moment and transmit it to the update module;

[0039] An update module, configured to: update the next moment to the previous moment, use the updated Markov probability transition matrix as the Markov probability transition matrix at the previous moment and transmit it to the estimation module to continue the detection until the detection is completed.

[0040] Further, the calculation module includes

[0041] A first calculation sub-module, configured to: use the estimation result and the Markov probability transition matrix to calculate the initial values of the state variables and state vector covariance matrices when filtering each motion model at the previous moment;

[0042] A determination sub-module, configured to: determine the residuals, residual covariances, state variables, and state vector covariance matrices of each motion model at the next moment according to the initial values of the state variables and state vector covariance matrices when filtering each motion model at the previous moment;

[0043] A second calculation sub-module, configured to: use the model probability estimation results, Markov probability transition matrix, and residuals and residual covariances at the next moment of each motion model at the previous moment to calculate the model probabilities of each motion model at the next moment.

[0044] Further, the determination sub-module includes:

[0045] A prediction sub-unit, configured to: use the state variables and the initial values of the state vector covariance matrices of each motion model during filtering at the previous moment to predict the state variables and the state vector covariance matrices of each motion model at the next moment;

[0046] A calculation sub-unit, configured to: use the predicted state variables and state vector covariance matrices of each motion model at the next moment to calculate the residuals and residual covariances of each motion model at the corresponding moment, so as to determine the filtering gain of each motion model at the corresponding moment;

[0047] An update sub-unit, configured to: use the residuals, residual covariances, and filtering gains of each motion model at the next moment to update the prediction results of the state variables and state vector covariance matrices of each motion model at the corresponding moment.

[0048] Further, the judgment module includes:

[0049] A third calculation sub-module, configured to: use the model probabilities of each motion model at the next moment and the Markov probability transition matrix at the previous moment to calculate the error compression ratios of any two motion models;

[0050] A correction sub-module, configured to: use the error compression ratios to correct the corresponding elements in the Markov probability transition matrix at the previous moment.

[0051] In summary, the technical solution of the present invention has the following technical effects:

[0052] In this embodiment, through the motion models during the motion process, the Markov probability transition matrix at the previous moment, and the estimation results of the state variables and model probabilities at the previous moment, the state variables, state vector covariance matrices, and model probabilities of each motion model at the next moment are calculated, realizing the fusion of the state variables and the fusion of the state vector covariance matrices of all motion models during the motion process; and using the model probabilities of each motion model at the next moment, the adaptive adjustment of the Markov probability transition matrix is realized, thereby reducing the errors caused by the delay and lag during the switching process of multiple motion models; the present invention can adaptively and quickly switch to the matching model according to the current matching situation, has a good effect when performing state estimation on multi-modal maneuvering targets, can greatly improve the detection accuracy of the aircraft for maneuvering targets, ensure that the aircraft effectively obtains target detection information, and provides a method support for maneuvering target detection. Description of the Drawings

[0053] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0054] Figure 1 Schematic diagram of the aircraft detection method based on the adaptive interactive multiple model algorithm of the present invention;

[0055] Figure 2 Schematic diagram of the aircraft detecting the target. Specific embodiments

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0057] In the actual observation environment, the maneuvering form of the target is unknown and changing, but the movement of the target can be approximated by some basic motion models (such as the maneuvering turn model and the uniform motion model). During the detection process, the position and velocity of the target are taken as state variables, that is , then the target state equation can be expressed as:

[0058] ; (1)

[0059] where is the system state at k time, is the state transition matrix, is the noise transfer matrix, is the process noise variable. Here, all the eigenvalues of the state transition matrix are in the left half of the S-plane, with the characteristics of slow time-variation and boundedness.

[0060] Aircraft are all equipped with corresponding sensors and can independently measure range and angle, as shown in Figure 2 . Then the observation equation of the aircraft for the maneuvering target can be expressed as:

[0061] ; (2)

[0062] where .

[0063] Linearizing it gives

[0064] Then the measurement equation is: ; (3)

[0065] In equations (2) and (3), and respectively represent the distance and azimuth angle between the aircraft and the target, and represent the position of the aircraft at k itself at a certain moment, which is generally calculated by its own navigation system. is the i th moving aircraft at k a certain moment, is the i th aircraft (i.e., the motion model) at k a certain moment of the observation matrix. represents the observation noise, and and are independent zero-mean Gaussian white noises, and their variances are respectively and . Equations (1) and (3) constitute the state equation and measurement equation for the aircraft to detect the target.

[0066] Based on the above principle, this embodiment presents an aircraft detection method based on the adaptive interacting multiple model algorithm. Referring to Figure 1 , this aircraft detection method includes:

[0067] Step S1: Use the aircraft to detect the target to obtain the motion model of the target during the movement, and determine the motion model during the movement and the Markov probability transition matrix at the previous moment.

[0068] Assume that the IMM algorithm includes N motion models. At k -1 moment, the state variables and covariance matrices of each motion model are expressed as and , and the probability of each model is .

[0069] The Markov probability transition matrix in this embodiment is: ; (4)

[0070] Wherein, is kThe Markov probability transition matrix at time - 1; is k the element value corresponding to the i th row and the j th column in the Markov probability transition matrix at time - 1 (i.e., k the probability that the i th motion model at time - 1 transfers to the j th motion model), , , , N where is the number of motion models.

[0071] Step S2: Estimate the state variables and model probabilities of each motion model at the previous moment.

[0072] Step S3: Using the Markov probability transition matrix and the estimation results, adopt the IMM algorithm to calculate the state variables, state vector covariance matrices, and model probabilities of each motion model at the next moment.

[0073] The specific implementation process of this step includes:

[0074] Step S31: Using the estimation results and the Markov probability transition matrix, calculate the initial values of the state variables and state vector covariance matrices when filtering each motion model at the previous moment;

[0075] According to the state variables, covariance matrices, and model probability values of each motion model at k time - 1, perform state interaction to generate k the initial values for filtering at time.

[0076] In this embodiment, the initial value of the state variable is: ; (5)

[0077] where, is the initial value of the state variable of the j th motion model when filtering at k time - 1; is the estimation result of the state variable of the i th motion model at k time - 1; is the estimation result of the model probability of the i th motion model at k time - 1. In this embodiment, the initial value of the state vector covariance matrix is:

[0078]

[0079] ; (6)

[0080] Among them, is the initial value of the state vector covariance matrix of the j th motion model at the filtering time of k -1 moment; and are respectively the true value of the state variable of the i th motion model at k -1 moment and the estimated result of the state variable of the j th motion model at k -1 moment; is the true value of the state vector covariance matrix of the i th motion model at k -1 moment; is the transpose matrix.

[0081] Step S32: Determine the residual, residual covariance, state variable, and state vector covariance matrix of each motion model at the next moment according to the state variable and the initial value of the state vector covariance matrix of each motion model at the filtering time of the previous moment.

[0082] The specific process of step S32 includes:

[0083] Step S321: Predict the state variable and the state vector covariance matrix of each motion model at the next moment by using the state variable and the initial value of the state vector covariance matrix of each motion model at the filtering time of the previous moment.

[0084] In this embodiment, the prediction is performed according to the following formula;

[0085] ; (7)

[0086] ; (8)

[0087] Among them, is the predicted value of the state variable of the k th motion model at the j th moment predicted at k -1 moment; is the state transition matrix of the j th motion model at k -1 moment; is the initial value of the state variable of the j th motion model at the filtering time of k -1 moment; is the predicted value of the state vector covariance matrix of the k th motion model at the j th moment predicted at k -1 moment; is k the noise transfer matrix at -1 moment. is the variance of the process noise variable of the j th motion model at k time - 1.

[0088] Step S322: Use the state variables and state vector covariance matrices of each motion model at the next moment for prediction, calculate the residuals and residual covariances of each motion model at the corresponding moment to determine the filtering gain of each motion model at the corresponding moment.

[0089] The residuals and residual covariances in this embodiment are respectively:

[0090] ;

[0091] ; (9)

[0092] where and are respectively the residual and residual covariance of the j th motion model at k time; is the observation value of the j th motion model at k time; is the observation matrix of the j th motion model at k time; and are respectively the predicted state variable value and the predicted state vector covariance matrix value of the k th motion model predicted at j time - 1 at k time; is the variance of the observation noise of the j th motion model at k time.

[0093] The filtering gain in this embodiment is:

[0094] ; (10)

[0095] where is the filtering gain of the j th motion model at k time.

[0096] Step S323: Use the residuals, residual covariances and filtering gains of each motion model at the next moment to update the prediction results of the state variables and state vector covariance matrices of each motion model at the corresponding moment.

[0097] This embodiment is updated according to the following formula;

[0098] ; (11)

[0099] ; (12)

[0100] wherein, is the updated value of the state variable of the j th motion model at the k moment; is the predicted value of the state variable of the k th motion model at the j th motion model predicted at the k -1 moment; is the filtering gain of the j th motion model at the k moment; is the residual of the j th motion model at the k moment; is the updated value of the state vector covariance matrix of the j th motion model at the k moment; is the predicted value of the state vector covariance matrix of the k th motion model predicted at the j -1 moment at the k moment; is the residual covariance of the j th motion model at the k moment.

[0101] Step S33: Calculate the model probability of each motion model at the next moment by using the model probability estimation result of each motion model at the previous moment, the Markov probability transition matrix, and the residual and residual covariance at the next moment.

[0102] The model probability in this embodiment is:

[0103] ; (13)

[0104] ; (14)

[0105] wherein, is the model probability of the j th motion model at the k moment; is an intermediate variable; is the element value corresponding to the k th row and the i th column in the Markov probability transition matrix at the j -1 moment; is the i th motion model at the kModel probability estimation results at time - 1.

[0106] Step S4: Use the state variables, state - vector covariance matrices, and model probabilities of each motion model at the next moment to perform state - variable fusion and state - vector covariance - matrix fusion in sequence.

[0107] In this embodiment, state - variable fusion and state - vector covariance - matrix fusion are performed according to the following formulas:

[0108] ; (15)

[0109] ; (16)

[0110] where, is the state - variable fusion result; is the state - vector covariance - matrix fusion result.

[0111] Step S5: When the detection is not over, use the model probabilities of each motion model at the next moment to update the Markov probability transition matrix at the previous moment.

[0112] In the above IMM basic algorithm, the Markov probability transition matrix plays a crucial role in updating the model probability. However, in the traditional IMM algorithm, most of them are obtained through prior knowledge and remain unchanged throughout the filtering process. This leads to the model probability being updated only after a period of time when the target modality changes. Since the Markov probability transition matrix cannot be adaptively updated according to the target maneuvering modality, the corresponding model probability cannot be adaptively updated either. This results in an obvious delay and lag in the model - switching process, and thus brings a certain error. This embodiment can give the ratio of the model error compression rate, realize the adaptive update of the Markov transition matrix, and achieve the adaptive adjustment of the matrix. The specific update process includes:

[0113] Step S51: Use the model probabilities of each motion model at the next moment and the Markov probability transition matrix at the previous moment to calculate the error compression ratio between any two motion models.

[0114] The error compression ratio in this embodiment is:

[0115] ; (17)

[0116] where, is i the error compression ratio between the j th motion model and the is the element value corresponding to the i th row and the j th column in the Markov probability transition matrix at the previous moment; is the element value corresponding to the j th row and the i th column in the Markov probability transition matrix at the previous moment; and are the model probabilities of the i th motion model and the j th motion model at the k th moment, respectively.

[0117] Step S52: Use the error compression ratio to correct the corresponding elements in the Markov probability transition matrix at the previous moment.

[0118] The corrected Markov probability transition matrix at the previous moment (i.e., k -1 moment) is:

[0119] ; (18)

[0120] In this embodiment, the correction is performed according to the following formula:

[0121] ; (19)

[0122] ; (20)

[0123] where is the correction value of the element (i.e., the diagonal element) corresponding to the i th row and the i th column in the corrected Markov probability transition matrix at the previous moment; is the correction value of the element (i.e., the non-diagonal element) corresponding to the i th row and the j th column in the Markov probability transition matrix; l is the exponential factor; , , , N is the number of motion models.

[0124] Since the Markov probability transition matrix is generally a symmetric positive definite matrix with dominant main diagonal, when , it can be seen from equation (17) that ; when , . This shows that the corrected compression ratio It can achieve the adaptive adjustment of the Markov matrix. This adaptive update method can automatically increase the transition probability of the matching model and decrease the transition probability of the non-matching model.

[0125] Step S6: Update the next moment to the previous moment, use the updated Markov probability transition matrix as the Markov probability transition matrix of the previous moment, and return to step S2 to continue the detection until the detection is completed.

[0126] To verify the effectiveness of the technical solution of this embodiment, a simulation experiment was carried out:

[0127] The simulation conditions were set as follows: The total simulation duration was 180 s, the sampling period T = 1 s, the initial position of the aircraft was (-200 m, 1000 m), and the maneuvering speed was 120 m / s; The target made a left turn maneuver of 0.8° / s from 30 s to 80 s and a right turn maneuver of 0.8° / s from 130 s to 180 s, and maintained a uniform motion at other times. The initial states of the position and speed of the target were The system noise and the observation noise were independent of each other and were respectively and .

[0128] Under the above target maneuvering situation, the IMM algorithm used three models, which were set as follows: a uniform motion model, a left maneuvering turn model of 0.8° / s, and a right maneuvering turn model of 0.8° / s. The initial values of the model probabilities were The initial value of the Markov probability transition matrix was:

[0129]

[0130] The aircraft used the improved adaptive IMM filtering algorithm (Improved Adaptive IMM, IA-IMM) and the basic IMM algorithm proposed in this embodiment respectively, and carried out 300 Monte Carlo tests respectively. The root-mean square error values (Root-mean Square Error, RMSE) of each parameter were taken as the evaluation index.

[0131] RMSE can reflect the detection accuracy, and its calculation method is:

[0132] (21)

[0133] In formula (21), n represents the number of simulation times. x represents the true value, represents at k the estimated value at the moment.

[0134] Taking a certain simulation as an example, using the technical solution of this embodiment to predict the motion trajectory of the target, it basically coincides with the real trajectory, and the error is small. The algorithm proposed in this embodiment (the improved adaptive IMM filtering algorithm, that is, the technical solution of this embodiment) can quickly switch to the matching model according to the maneuvering situation of the target and significantly increase the probability of the matching model. The basic IMM algorithm switches relatively late and cannot effectively increase the probability of the matching model, ultimately resulting in an increase in tracking error and a decrease in filtering accuracy.

[0135] The root mean square errors of position and velocity for 300 tests were statistically calculated. During the short time period of target maneuvering mode switching, the error will have certain fluctuations (such as at 1s, 30s, and 130s), which are caused by the incomplete matching during the model transition process. In addition, since the proposed method can adaptively adjust the probability transition matrix, once switched to the matching model, this adaptive algorithm can effectively reduce the tracking error. From the algorithm performance comparison results in Table 1, it can be seen that the proposed adaptive IMM algorithm effectively improves the position and velocity tracking accuracy. Considering the comprehensive x and y directions, the accuracy is improved by 6.21% and 8.8% respectively.

[0136]

[0137] The above embodiment can be implemented by using the technical solution given in the following embodiment:

[0138] Another embodiment provides an aircraft detection system based on an adaptive interactive multiple model algorithm. This aircraft detection system includes:

[0139] A detection module, configured to: use an aircraft to detect a target to obtain the motion model of the target during movement, and determine the motion model during movement and the Markov probability transition matrix at the previous moment;

[0140] An estimation module, configured to: estimate the state variables and model probabilities of each motion model at the previous moment;

[0141] A calculation module, configured to: use the Markov probability transition matrix and the estimation results, and adopt the IMM algorithm to calculate the state variables, state vector covariance matrices, and model probabilities of each motion model at the next moment;

[0142] A fusion module, configured to: use the state variables, state vector covariance matrices, and model probabilities of each motion model at the next moment to perform state variable fusion and state vector covariance matrix fusion in sequence;

[0143] A judgment module, configured to: judge whether the detection is ended. If so, output the fused result of the state variables and the fused result of the state vector covariance matrix, and end; if not, update the Markov probability transition matrix at the previous moment by using the model probability of each motion model at the next moment, and transmit it to the update module;

[0144] An update module, configured to: update the next moment to the previous moment, and use the updated Markov probability transition matrix as the Markov probability transition matrix at the previous moment and transmit it to the estimation module to continue the detection until the detection is completed.

[0145] Further, the calculation module includes

[0146] A first calculation sub-module, configured to: calculate the initial values of the state variables and the state vector covariance matrix of each motion model during filtering at the previous moment by using the estimation result and the Markov probability transition matrix;

[0147] A determination sub-module, configured to: determine the residual, residual covariance, state variables and state vector covariance matrix of each motion model at the next moment according to the initial values of the state variables and the state vector covariance matrix of each motion model during filtering at the previous moment;

[0148] A second calculation sub-module, configured to: calculate the model probability of each motion model at the next moment by using the model probability estimation result of each motion model at the previous moment, the Markov probability transition matrix, and the residual and residual covariance at the next moment.

[0149] Further, the determination sub-module includes:

[0150] A prediction sub-unit, configured to: predict the state variables and the state vector covariance matrix of each motion model at the next moment by using the initial values of the state variables and the state vector covariance matrix of each motion model during filtering at the previous moment;

[0151] A calculation sub-unit, configured to: calculate the residual and residual covariance of each motion model at the corresponding moment by using the predicted state variables and the state vector covariance matrix of each motion model at the next moment to determine the filtering gain of each motion model at the corresponding moment;

[0152] An update sub-unit, configured to: update the predicted results of the state variables and the state vector covariance matrix of each motion model at the corresponding moment by using the residual, residual covariance and filtering gain of each motion model at the next moment.

[0153] Further, the judgment module includes:

[0154] A third calculation sub-module, configured to calculate an error compression ratio between any two motion models by using the model probability of each motion model at the next moment and the Markov probability transition matrix at the previous moment;

[0155] A correction sub-module, configured to correct corresponding elements in the Markov probability transition matrix at the previous moment by using the error compression ratio.

[0156] The principles, formulas, and their parameter definitions involved in the above embodiments are all applicable and will not be elaborated here one by one.

[0157] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification. The above embodiments only represent several implementation manners of the present application, and their descriptions are relatively specific and detailed, but they should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. An aircraft detection method based on an adaptive interactive multi-model algorithm, characterized in that, The aircraft detection method includes: Step S1: Use the aircraft to detect the target to obtain the motion model of the target during the movement process, and determine the motion model during the movement process and the Markov probability transition matrix at the previous moment; Step S2: Estimate the state variables and model probabilities of each motion model at the previous moment; Step S3: Use the Markov probability transition matrix and the estimation results to calculate the state variables, state vector covariance matrices, and model probabilities of each motion model at the next moment; Step S4: Use the state variables, state vector covariance matrices, and model probabilities of each motion model at the next moment to perform state variable fusion and state vector covariance matrix fusion in sequence; Step S5: When the detection is not completed, use the model probabilities of each motion model at the next moment to update the Markov probability transition matrix at the previous moment; In step S5, the specific update process includes: Step S51: Use the model probabilities of each motion model at the next moment and the Markov probability transition matrix at the previous moment to calculate the error compression ratio between any two motion models; Step S52: Use the error compression ratio to correct the corresponding elements in the Markov probability transition matrix at the previous moment; In step S51, the error compression ratio is: ； Among them, is the error compression ratio of the i th motion model and the j th motion model; is the element value corresponding to the i th row and the j th column in the Markov probability transition matrix at the previous moment; is the element value corresponding to the j th row and the i th column in the Markov probability transition matrix at the previous moment; and are respectively the model probabilities of the i th motion model and the j th motion model at the k moment; In step S52, the correction is performed according to the following formula: ； ； Among them, is the correction value corresponding to the element in the i th row and i th column of the Markov probability transition matrix at the previous moment after correction; is the correction value corresponding to the element in the i th row and j th column of the Markov probability transition matrix at the previous moment after correction; l is the exponential factor; , , , N is the number of motion models; Step S6: Update the next moment to the previous moment, use the updated Markov probability transition matrix as the Markov probability transition matrix at the previous moment, return to step S2, and continue the detection until the detection is completed.

2. The aircraft detection method according to claim 1, characterized in that The specific implementation process of step S3 includes: Step S31: Use the estimation results and the Markov probability transition matrix to calculate the initial values of the state variables and state vector covariance matrices of each motion model during filtering at the previous moment; Step S32: Determine the residuals, residual covariances, state variables, and state vector covariance matrices of each motion model at the next moment according to the initial values of the state variables and state vector covariance matrices of each motion model during filtering at the previous moment; Step S33: Use the model probability estimation results of each motion model at the previous moment, the Markov probability transition matrix, and the residuals and residual covariances at the next moment to calculate the model probabilities of each motion model at the next moment.

3. The aircraft detection method according to claim 2, wherein, The specific process of step S32 includes: Step S321: Use the initial values of the state variables and state vector covariance matrices of each motion model during filtering at the previous moment to predict the state variables and state vector covariance matrices of each motion model at the next moment; Step S322: Use the predicted state variables and state vector covariance matrices of each motion model at the next moment to calculate the residuals and residual covariances of each motion model at the corresponding moment to determine the filtering gain of each motion model at the corresponding moment; Step S323: Use the residuals, residual covariances, and filtering gains of each motion model at the next moment to update the prediction results of the state variables and state vector covariance matrices of each motion model at the corresponding moment.

4. An aircraft detection system based on an adaptive interactive multiple model algorithm, characterized in that, The aircraft detection system includes: The detection module is configured to: use an aircraft to detect a target to obtain a motion model of the target during movement, and determine the motion model during movement and the Markov probability transition matrix at the previous moment; The estimation module is configured to: estimate the state variables and model probabilities of each motion model at the previous moment; The calculation module is configured to: use the Markov probability transition matrix and the estimation results, and adopt the IMM algorithm to calculate the state variables, state vector covariance matrices and model probabilities of each motion model at the next moment; The fusion module is configured to: use the state variables, state vector covariance matrices and model probabilities of each motion model at the next moment to perform state variable fusion and state vector covariance matrix fusion in sequence; The judgment module is configured to: judge whether the detection is completed. If so, output the state variable fusion result and the state vector covariance matrix fusion result, and end; if not, use the model probabilities of each motion model at the next moment to update the Markov probability transition matrix at the previous moment and transmit it to the update module; The judgment module includes: The third calculation sub-module is configured to: use the model probabilities of each motion model at the next moment and the Markov probability transition matrix at the previous moment to calculate the error compression ratio between any two motion models; The correction sub-module is configured to: use the error compression ratio to correct the corresponding elements in the Markov probability transition matrix at the previous moment; The error compression ratio is: ； Among them, is the error compression ratio of the i th motion model and the j th motion model; is the element value corresponding to the i th row and the j th column in the Markov probability transition matrix at the previous moment; is the element value corresponding to the j th row and the i th column in the Markov probability transition matrix at the previous moment; and are respectively the model probabilities of the i th motion model and the j th motion model at the k th moment; The correction is performed according to the following formula: ； ； Among them, is the correction value of the element corresponding to the i th row and the i th column in the Markov probability transition matrix at the previous moment after correction; is the correction value of the element corresponding to the i th row and the j th column in the Markov probability transition matrix at the previous moment after correction; l is the exponential factor; , , , N is the number of motion models; The update module is configured to: update the next moment to the previous moment, and use the updated Markov probability transition matrix as the Markov probability transition matrix at the previous moment and transmit it to the estimation module to continue the detection until the detection is completed.

5. The aircraft detection system according to claim 4, characterized in that, The calculation module includes The first calculation sub-module is configured to: use the estimation results and the Markov probability transition matrix to calculate the initial values of the state variables and state vector covariance matrices of each motion model during filtering at the previous moment; The determination sub-module is configured to: determine the residuals, residual covariances, state variables and state vector covariance matrices of each motion model at the next moment according to the initial values of the state variables and state vector covariance matrices of each motion model during filtering at the previous moment; The second calculation sub-module is configured to: use the model probability estimation results of each motion model at the previous moment, the Markov probability transition matrix, and the residuals and residual covariances at the next moment to calculate the model probabilities of each motion model at the next moment.

6. The aircraft detection system according to claim 5, wherein, The determination sub-module includes: The prediction sub-unit is configured to: use the initial values of the state variables and state vector covariance matrices of each motion model during filtering at the previous moment to predict the state variables and state vector covariance matrices of each motion model at the next moment; The calculation sub-unit is configured to: use the predicted state variables and state vector covariance matrices of each motion model at the next moment to calculate the residuals and residual covariances of each motion model at the corresponding moment to determine the filtering gain of each motion model at the corresponding moment; An update subunit, configured to: update the prediction results of the state variables and the state vector covariance matrix of each motion model at the corresponding moment by using the residual, the residual covariance, and the filtering gain of each motion model at the next moment.

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