CN109655826A - The low slow Small object track filtering method of one kind and device - Google Patents
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
The invention discloses a kind of based on the low slow Small object track filtering method of Interactive Multiple-Model-Kalman filtering, comprising: step 1: obtaining target Initial state estimation value X by Track initialization algorithm0With Initial state estimation covariance matrix P0；Step 2: setting target movement model collection obtains corresponding state-transition matrix, radar measurement matrix according to kinetic characteristic；Step 3: calculating kth moment corresponding model prediction probability and mixing probability；Step 4: calculating kth moment admixture, mixing covariance；Step 5: calculating the one-step prediction value of+1 moment of kth each model admixture and the one-step prediction value and prediction covariance matrix of prediction covariance matrix and measurement；Step 6: calculating+1 moment of kth Target state estimator value and estimate covariance matrix, and calculate likelihood function and model probability；K value is added 1, then executes step 3 again.The present invention improves a mark and is associated to power, and finally improves radar detection precision.
Description
Technical Field
The invention belongs to the technical field of radars, and particularly relates to a low-slow small target track filtering method and device based on interactive multi-model-Kalman filtering.
Background
The traditional radar can not effectively detect a high-altitude fast large target (short for a high-altitude fast target) and a low-altitude slow small target (short for a low-altitude slow small target), and along with the rapid development of low-altitude openness and unmanned aerial vehicle technology, the traditional radar can not effectively prevent low-altitude safety. In view of the above, a low-slow small-target detection radar is developed, which can timely find low-slow small-flight targets within a certain range and adopt corresponding treatment means. However, when the unmanned aerial vehicle is detected at low altitude, the target association result can be influenced by various clutter and interference, and especially when a target close to the ground is detected, the detection error of the radar and the angle measurement precision of a stronger ground clutter target in the pitching direction can be greatly influenced, so that the noise pitch angle precision is sharply deteriorated, the pitching error is multiplied, the association result is greatly influenced, the track is greatly irregular, and the detection precision and the tracking precision of the radar are influenced finally.
In summary, the prior art has the following disadvantages: the traditional radar cannot effectively detect the low-altitude low-speed small target, and the detection precision and the tracking precision of the existing low-speed small target detection radar are poor.
Disclosure of Invention
In order to solve the technical problems, the invention provides a low-speed small target track filtering method and device based on interactive multi-model-Kalman filtering.
A low-slow small target track filtering method based on interactive multi-model-Kalman filtering comprises the following steps:
step 1: obtaining an estimated value X of a target initial state through a track initial algorithm0And initial state estimate covariance matrix P0；
Step 2: setting a target motion model set, and obtaining a corresponding state transition matrix and a radar measurement matrix according to the target motion state;
and step 3: calculating the prediction probability and the mixing probability of the target motion model corresponding to the kth moment;
and 4, step 4: calculating a target motion model mixed state and a mixed covariance corresponding to the kth moment;
and 5: calculating a one-step predicted value and a predicted covariance matrix of the mixed state of each target motion model at the (k + 1) th moment and a measured one-step predicted value and a predicted covariance matrix;
step 6: calculating a target state estimation value and an estimated covariance matrix at the (k + 1) th moment, and calculating a likelihood function and a target motion model probability;
the value of k is incremented by 1 and then step 3 is performed again.
In a preferred embodiment, the track initiation algorithm comprises: a logic track starting algorithm or a track starting algorithm based on Hough transformation; the detection form of the radar detector comprises the following steps: square rate detection or linear detection.
In a preferred embodiment, the set of object motion models is defined as [ M [ ]1,M2,...,Mn]The corresponding state transition matrix is defined as [ F ]1,F2,...,Fn]The radar measurement matrix is defined as [ H ]1,H2,...,Hn]Wherein n represents the number of target motion models in a target motion model set, the target motion model set is a target motion model set obtained according to different motion states and motion characteristics of a target, the target motion model comprises a uniform velocity linear motion model, a uniform acceleration linear motion model and a cooperative turning model, wherein M is the number of target motion models in the target motion model set, and M is the number of target motion models in the target motion modeliRepresenting the motion model of the ith object, FiRepresenting the state transition matrix corresponding to the ith object motion model, HiAnd (3) representing a radar measurement matrix corresponding to the ith target motion model, wherein i is a positive integer greater than or equal to 1.
In a preferred embodiment, the object motion model predicts a probabilityAnd mixed probabilitiesIs obtained by the following steps:
3a) wherein the object motion model predicts the probabilityRepresenting the probability of the motion model of the ith object from time k to time k +1,representing the probability of the occurrence of the jth target motion model at the kth time, and calculating the prediction probability of the target motion model by using the following formula
Wherein, gamma isi|j＝Pr{rk+1＝i|rkJ, wherein said Γ ″i|jRepresenting the probability of a transition from an object motion model j to an object motion model i when the object is moving, where rk、rk+1Respectively representing target motion models of a target at the k moment and the k +1 moment, Pr {. is used for solving the probability of occurrence of an event, and Σ is used for summing operation;
3b) wherein the probability of mixingRepresenting the probability of transferring from the ith target motion model to the jth target motion model from the k time to the k +1 time when the target moves, and calculating the mixed probability by using the result obtained in the step 3a) and the following formula
Wherein c represents a normalization constant, the magnitude of c andas a result of the calculation, k represents the kth time, and k is a positive integer of 1 or more.
In a preferred embodiment, the time k mixing state is obtained by the following stepsSum and mixture covariance
4a) WhereinAndrepresenting the interaction result of the state and the covariance, and calculating the mixed state at the k-th time by using the result obtained in the step 3b) and the following formulaSum and mixture covariance
WhereinAndupdated values representing the hybrid state and hybrid covariance of the target at time k (.)TRepresenting a matrix transpose operation.
In a preferred embodiment, each target motion model M at the k +1 th moment is calculated by the following stepsiOne-step prediction of hybrid statesAnd a prediction covariance matrixAnd one-step prediction value of measurementAnd a prediction covariance matrixWhere i represents the ith object motion model:
5a) calculating a one-step predicted value of the mixing state at the k +1 th time by using the following formulaAnd the prediction covariance matrix
Wherein u isiRepresenting the process noise matrix of the ith target motion model, wherein the known process noise obeys the mean value of 0 and the standard deviation is sigmauNormal distribution of (2); qi k+1|kRepresenting a process noise covariance matrix of an ith target motion model from a kth time to a (k + 1) th time;
5b) calculating a predicted value of one step measured from the k-th time to the k + 1-th time by using the result obtained in the step 5a) and the following formulaAnd a prediction covariance matrix
Wherein R isi k+1|kRepresenting the measured noise covariance matrix of the ith target motion model from the kth moment to the (k + 1) th moment, wherein the measured noise covariance matrix has the following size: ri k+1|k＝wi k+1|k(wi k+1|k)TWherein w isi k+1|kRepresenting a measurement noise matrix of the ith target motion model from the kth moment to the kth +1 moment, wherein the known measurement noise obeys a mean value of 0 and a standard deviation of sigmawWhere i is 1,2, … n.
In a preferred embodiment of the present invention,
wherein σuRepresenting the process noise standard deviation, TsRepresenting the scan period of the radar antenna.
In a preferred embodiment, the target state estimate at time k +1 of the ith moving target model is calculated byAnd estimate covariance matrixAnd calculating likelihood functionsProbability of target motion model
6a) Calculating the target state estimation value of the ith moving target model at the (k + 1) th moment according to the calculation result of the step 5 and the following formulaAnd estimate covariance matrix
Wherein z isk+1Represents the measured value of the target at the k +1 th time, (.)TFor matrix transposition operations, (.)-1Performing matrix inversion operation;
6b) calculating the likelihood function at the k +1 th time using the following formula based on the result obtained in step 6a)
Where exp (-) denotes solving a power series based on the natural logarithm e, det (-) denotes the value of the determinant,represents the square root operation;
6c) calculating the probability of the target motion model at the k +1 th moment according to the likelihood function obtained by calculation in the step 6b) and the following formula
The target association algorithm comprises the following steps: a nearest neighbor data association algorithm, a probability data interconnection algorithm or a joint probability data interconnection algorithm.
The invention discloses a low-slow small target track filtering device based on interactive multi-model-Kalman filtering, which comprises:
a processor;
a memory in electronic communication with the processor, the memory having stored therein instructions that, when executed by the processor, are capable of causing the apparatus to perform the method of any one of claims 1-8.
The present invention discloses a computer readable storage medium having stored thereon instructions which, when executed by a processor, implement a method as in one of the above.
The invention has the beneficial effects that: the method can be applied to targets (such as unmanned aerial vehicles, birds and the like) which are low in flying height, low in flying speed and small in size and difficult to detect, and filtering operation after correlation is completed. According to the method, an interactive multi-model-Kalman filtering algorithm is added in the radar data processing process, so that smooth target tracks can be realized, the association accuracy is improved, and the radar detection precision is improved compared with the traditional process of directly taking the measured data as final track update data.
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FIG. 1 is a general flow chart for implementing the low-slow small target trajectory filtering method based on the interactive multi-model-Kalman filtering.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
Fig. 1 is a general flowchart of an implementation of a low-slow small target trajectory filtering method based on an interactive multi-model-kalman filtering, and as shown in the figure, the low-slow small target trajectory filtering method of the present invention includes the following steps:
step 1 is performed at block 101: obtaining an estimated value X of a target initial state through a track initial algorithm0And initial state estimate covariance matrix P0；
Step 2 is performed at block 102: setting a target motion model set, and obtaining a corresponding state transition matrix and a radar measurement matrix according to motion characteristics;
step 3 is performed at block 103: calculating model prediction probability and mixing probability corresponding to the kth moment;
step 4 is performed at block 104: calculating a mixing state and a mixing covariance at the kth moment;
step 5 is performed at block 105: calculating a one-step predicted value and a predicted covariance matrix of each model mixed state at the (k + 1) th moment and a measured one-step predicted value and a predicted covariance matrix;
step 6 is performed at block 106: calculating a target state estimation value and an estimated covariance matrix at the (k + 1) th moment, and calculating a likelihood function and a model probability;
the value of k is incremented by 1 and then step 3 is performed again.
The detailed calculation method of the present invention is described below: in step 1, firstly initializing parameters, setting the initial output of the detector to be 0, and waiting for the detector to receive signals; obtaining the initial state estimation X of the target track through the target track initial algorithm0And a state estimation covariance matrix P0(ii) a The target track initiation algorithm includes a logic track initiation algorithm and a track initiation algorithm based on Hough transformation, and the example selects but is not limited to the track initiation algorithm based on Hough transformation. The detection form of the radar detector includes square rate detection, linear detection, etc., and the square rate detector is selected but not limited in this example.
In step 2, a set of object motion models is set to [ M [ ]1,M2,...,Mn]According to the motion characteristics, the corresponding state transition matrix is obtained as [ F ]1,F2,...,Fn]The radar measurement matrix is [ H ]1,H2,...,Hn]Wherein n represents the number of models in the model set;
the target motion model is a model set obtained according to different motion states and motion characteristics of the target, and common target motion models comprise a uniform velocity linear motion model, a uniform acceleration linear motion model, a cooperative turning model and the like. In the embodiment, but not limited to, a uniform linear motion model and a uniform acceleration linear motion model are selected to form a target motion model set.
This example was verified based on measured data with the following model parameters:
three-dimensional uniform linear motion model state transition matrix F1Comprises the following steps:
wherein T issIndicating the scanning period of the radar antenna, in this example T is chosens＝3s。
The three-dimensional uniform linear motion model measurement matrix is as follows:
three-dimensional uniform acceleration linear motion model state transition matrix F1Comprises the following steps:
the three-dimensional uniform acceleration linear motion model measurement matrix is as follows:
step 3, obtaining the model M according to the step 2i(where i ═ 1, 2.. times, n) and the corresponding model prediction probabilities are calculated by the measurement matrixAnd mixed probabilities
3a) Calculating model prediction probability using the following equation
Wherein, gamma isi|j＝Pr{rk+1＝i|rkJ, the probability of the transition from model j to model i when representing the object motion is Γi|j，rk、rk+1Respectively representing target motion models of a target at the k moment and the k +1 moment, Pr {. is used for solving the probability of occurrence of an event, and Σ is used for summing operation;
3b) calculating a mixture probability using the result obtained in step 3a) and the following formula
Wherein c represents a normalization constant, magnitude andthe calculation results are related;
step 4, calculating the mixing state at the kth moment according to the model prediction probability and the mixing probability obtained in the step 3Sum and mixture covariance
4a) Calculating the mixing state at the k-th time by using the result obtained in the step 3b) and the following formulaSum and mixture covariance
WhereinAndrepresents the state update value of the target at time k (.)TRepresenting a matrix transpose operation.
Step 5, calculating each model M at the k +1 th moment according to the state transition matrix obtained in the step 2 and the mixed state obtained in the step 4i(wherein i is 1, 2.),n) One-step prediction of hybrid statesAnd covariance matrix
5a) Calculating a one-step predicted value of the mixing state at the k +1 th time by using the following formulaAnd the prediction covariance matrix
Wherein u isiRepresenting a process noise matrix, known as the process noise obeys a mean of 0 and a standard deviation of σuNormal distribution of (2); qi k+1|kRepresents the process noise covariance matrix from time k to time k +1, in this example taking the form:
wherein σuExpressing the process noise standard deviation, take σp＝0.1。
Step 6, calculating the one-step prediction of the measurement at the k +1 th moment according to the measurement matrix obtained in the step 2 and the state one-step prediction state and covariance matrix obtained in the step 5And variance
6a) Calculating a predicted value measured at the k +1 th moment by using the result obtained in the step 5 and the following formulaAnd a prediction covariance matrix
Wherein R isi k+1|kRepresenting the measured noise of the ith model from the kth time to the (k + 1) th timeAcoustic covariance matrix, size: ri k+1|k＝wi k+1|k(wi k+1|k)TWherein w isi k+1|kRepresenting the measurement noise matrix of the ith model from the kth time to the kth +1 time, wherein the known measurement noise obeys a mean value of 0 and a standard deviation of sigmawNormal distribution of (2);
step 7, updating the target state estimation at the (k + 1) th moment according to the results of the step 5 and the step 6 by a track association algorithmAnd estimate covarianceAnd calculating likelihood functions
7a) Calculating the estimated value of the target state at the k +1 th moment according to the calculation results of the step 5 and the step 6 and the following formulaAnd estimate covariance matrix
Wherein z isk+1Represents the measured value of the target at the k +1 th time, (.)TFor matrix transposition operations, (.)-1A matrix inversion operation is performed.
7b) Calculating the likelihood function at the k +1 th time using the following formula based on the result obtained in step 7a)
Wherein,representing the probability of the occurrence of the ith moving object model at the time point k +1, exp (-) representing the solution to the power series based on the natural logarithm e, det (-) representing the value of the determinant,indicating a square root operation.
The target association algorithm comprises a nearest neighbor data association algorithm, a probability data interconnection algorithm, a joint probability data interconnection algorithm and the like, and because the relative clutter density of the experimental simulation environment is low and the target is a single target, the nearest neighbor data interconnection algorithm is selected but not limited in the embodiment.
Step 8, calculating the model probability of the k +1 th momentReturning to the step 4 to carry out the operation at the next moment.
8a) Calculating the model probability at the k +1 th moment according to the likelihood function obtained by the calculation in the step 7a) and the following formula
The effect of the present invention will be further explained with the simulation experiment. According to the simulation experiment result, wherein the simulation data source is as follows: the data signals mainly contain distance measurement information of x, y and z axes of a target under a Cartesian three-dimensional rectangular coordinate system. The simulation content comprises: the motion state of the simulation target is not subjected to the data processing of the invention and is compared with the motion state of the simulation target after the data processing of the invention. As can be seen from the simulation results, after the data processing of the invention, the track can be seen to be obviously smooth on the x-z plane reflecting the target pitching precision; and due to the serious irregularity of the original track, part of the traces deviate from the real international distance to be too large to be associated, so that the radar detection probability is reduced. After the track is smooth, the observed point track is more stable relative to the track, so that the point track association success rate is improved to a certain extent, and the aims of improving the radar detection precision and the tracking precision are finally fulfilled. In conclusion, the simulation experiment verifies the correctness, the effectiveness and the reliability of the method.
It will be evident to those skilled in the art that the embodiments of the present invention are not limited to the details of the foregoing illustrative embodiments, and that the embodiments of the present invention are capable of being embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the embodiments being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. Several units, modules or means recited in the system, apparatus or terminal claims may also be implemented by one and the same unit, module or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the embodiments of the present invention and not for limiting, and although the embodiments of the present invention are described in detail with reference to the above preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the embodiments of the present invention without departing from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A low-slow small target track filtering method based on interactive multi-model-Kalman filtering is characterized by comprising the following steps:
step 1: obtaining an estimated value X of a target initial state through a track initial algorithm0And initial state estimate covariance matrix P0；
Step 2: setting a target motion model set, and obtaining a corresponding state transition matrix and a radar measurement matrix according to the target motion state;
and step 3: calculating the prediction probability and the mixing probability of the target motion model corresponding to the kth moment;
and 4, step 4: calculating a target motion model mixed state and a mixed covariance corresponding to the kth moment;
and 5: calculating a one-step predicted value and a predicted covariance matrix of the mixed state of each target motion model at the (k + 1) th moment and a measured one-step predicted value and a predicted covariance matrix;
step 6: calculating a target state estimation value and an estimated covariance matrix at the (k + 1) th moment, and calculating a likelihood function and a target motion model probability;
the value of k is incremented by 1 and then step 3 is performed again.
2. The interactive multi-model-Kalman filtering-based low-slow small target trajectory filtering method according to claim 1, characterized in that the track initiation algorithm comprises: a logic track starting algorithm or a track starting algorithm based on Hough transformation; the detection form of the radar detector comprises the following steps: square rate detection or linear detection.
3. The interactive multi-model-Kalman filtering based low-slow small target trajectory filtering method of claim 2, characterized in that the set of target motion models is defined as [ M1,M2,…,Mn]The corresponding state transition matrix is defined as [ F ]1,F2,…,Fn]The radar measurement matrix is defined as [ H ]1,H2,…,Hn]Wherein n represents the number of target motion models in a target motion model set, the target motion model set is a target motion model set obtained according to different motion states and motion characteristics of a target, the target motion model comprises a uniform velocity linear motion model, a uniform acceleration linear motion model and a cooperative turning model, wherein M is the number of target motion models in the target motion model set, and M is the number of target motion models in the target motion modeliRepresenting the motion model of the ith object, FiRepresenting the state transition matrix corresponding to the ith object motion model, HiAnd (3) representing a radar measurement matrix corresponding to the ith target motion model, wherein i is a positive integer greater than or equal to 1.
4. The interactive multi-model-Kalman filtering based low-slow small target trajectory filtering method according to claim 3, characterized in that the target motion model predicts probabilityAnd mixed probabilitiesIs obtained by the following steps:
3a) wherein the object motion model predicts the probabilityRepresenting the probability of the motion model of the ith object from time k to time k +1,representing the probability of the occurrence of the jth target motion model at the kth time, and calculating the prediction probability of the target motion model by using the following formula
Wherein, gamma isi|j＝Pr{rk+1＝i|rkJ, wherein said Γ ″i|jRepresenting the probability of a transition from an object motion model j to an object motion model i when the object is moving, where rk、rk+1Respectively representing target motion models of a target at the k moment and the k +1 moment, Pr {. is used for solving the probability of occurrence of an event, and Σ is used for summing operation;
3b) wherein the probability of mixingTo show the eyesThe probability of transferring from the ith target motion model to the jth target motion model from the k time to the k +1 time during target motion is used for calculating the mixed probability by using the result obtained in the step 3a) and the following formula
Wherein c represents a normalization constant, the magnitude of c andas a result of the calculation, k represents the kth time, and k is a positive integer of 1 or more.
5. The method for filtering low-slow small target track based on interactive multi-model-Kalman filtering according to claim 4, characterized in that the k-th time mixing state is obtained by the following stepsSum and mixture covariance4a) WhereinAndrepresenting the interaction result of the state and the covariance, and calculating the mixed state at the k-th time by using the result obtained in the step 3b) and the following formulaSum and mixture covariance
WhereinAndupdated values representing the hybrid state and hybrid covariance of the target at time k (.)TRepresenting a matrix transpose operation.
6. The method for filtering low and slow small target tracks based on interactive multi-model-Kalman filtering as claimed in claim 5, characterized in that each target motion model M at the k +1 th moment is obtained by calculation through the following stepsiOne-step prediction of hybrid statesAnd a prediction covariance matrixAnd one-step prediction value of measurementAnd a prediction covariance matrixWhere i represents the ith object motion model:
5a) calculating a one-step predicted value of the mixing state at the k +1 th time by using the following formulaAnd the prediction covariance matrix
Wherein u isiRepresenting the process noise matrix of the ith target motion model, wherein the known process noise obeys the mean value of 0 and the standard deviation is sigmauNormal distribution of (2);representing a process noise covariance matrix of an ith target motion model from a kth time to a (k + 1) th time;
5b) calculating a predicted value of one step measured from the k-th time to the k + 1-th time by using the result obtained in the step 5a) and the following formulaAnd a prediction covariance matrix
Wherein,representing the measured noise covariance matrix of the ith target motion model from the kth moment to the (k + 1) th moment, wherein the measured noise covariance matrix has the following size: ri k+1|k＝wi k+1|k(wi k+1|k)TWherein w isi k+1|kRepresenting a measurement noise matrix of the ith target motion model from the kth moment to the kth +1 moment, wherein the known measurement noise obeys a mean value of 0 and a standard deviation of sigmawWhere i is 1,2, … n.
7. The method of claim 6, wherein the low-slow small target trajectory filtering method based on the interactive multi-model-Kalman filtering,
wherein σuRepresenting the process noise standard deviation, TsRepresenting the scan period of the radar antenna.
8. The method for filtering low-slow small target track based on interactive multi-model-Kalman filtering as claimed in claim 6, characterized in that the target state estimation value at the k +1 th moment of the ith moving target model is calculated by the following stepsAnd estimate covariance matrixAnd calculating likelihood functionsProbability of target motion model
6a) Calculating the target state estimation value of the ith moving target model at the (k + 1) th moment according to the calculation result of the step 5 and the following formulaAnd estimate covariance matrix
Wherein z isk+1Indicating the measured value of the target at the k +1 th time，(·)TFor matrix transposition operations, (.)-1Performing matrix inversion operation;
6b) calculating the likelihood function at the k +1 th time using the following formula based on the result obtained in step 6a)
Wherein,representing the probability of the occurrence of the ith moving object model at the time point k +1, exp (-) representing the solution to the power series based on the natural logarithm e, det (-) representing the value of the determinant,represents the square root operation;
6c) calculating the probability of the target motion model at the k +1 th moment according to the likelihood function obtained by calculation in the step 6b) and the following formula
The target association algorithm comprises the following steps: a nearest neighbor data association algorithm, a probability data interconnection algorithm or a joint probability data interconnection algorithm.
9. A low-slow small target track filtering device based on interactive multi-model-Kalman filtering is characterized by comprising:
a processor;
a memory in electronic communication with the processor, the memory having stored therein instructions that, when executed by the processor, are capable of causing the apparatus to perform the method of any one of claims 1-8.
10. A computer-readable storage medium storing instructions which, when executed by a processor, implement the method of any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201811538436.0A CN109655826B (en) | 2018-12-16 | 2018-12-16 | Low-slow small target track filtering method and device |
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| CN112989260A (en) * | 2021-02-07 | 2021-06-18 | 湖北理工学院 | Interactive multi-model filtering method of automatic feeding weighing system |
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| CN116383966A (en) * | 2023-03-30 | 2023-07-04 | 中国矿业大学 | Multi-unmanned system distributed cooperative positioning method based on interaction multi-model |
| CN118520374A (en) * | 2024-07-24 | 2024-08-20 | 四川九洲电器集团有限责任公司 | Target long-term track prediction method based on interaction multiple models |
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| CN113030959A (en) * | 2021-04-01 | 2021-06-25 | 成都汇蓉国科微系统技术有限公司 | Radar and photoelectric linkage anti-low-slow small-target 3D map picture jitter elimination method |
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| CN114895298A (en) * | 2022-04-08 | 2022-08-12 | 西安电子科技大学 | Method and device for measuring and correcting Bernoulli filtering of radar slow-speed weak maneuvering target |
| CN116383966A (en) * | 2023-03-30 | 2023-07-04 | 中国矿业大学 | Multi-unmanned system distributed cooperative positioning method based on interaction multi-model |
| CN116383966B (en) * | 2023-03-30 | 2023-11-21 | 中国矿业大学 | Multi-unmanned system distributed cooperative positioning method based on interaction multi-model |
| CN118520374A (en) * | 2024-07-24 | 2024-08-20 | 四川九洲电器集团有限责任公司 | Target long-term track prediction method based on interaction multiple models |
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