CN113466904B - Dynamic interference source tracking method and system - Google Patents
Dynamic interference source tracking method and system Download PDFInfo
- Publication number
- CN113466904B CN113466904B CN202110656381.9A CN202110656381A CN113466904B CN 113466904 B CN113466904 B CN 113466904B CN 202110656381 A CN202110656381 A CN 202110656381A CN 113466904 B CN113466904 B CN 113466904B
- Authority
- CN
- China
- Prior art keywords
- model
- filtering
- state
- tracking
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 98
- 238000001914 filtration Methods 0.000 claims abstract description 154
- 230000033001 locomotion Effects 0.000 claims abstract description 122
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 74
- 239000013598 vector Substances 0.000 claims abstract description 36
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 27
- 230000002452 interceptive effect Effects 0.000 claims abstract description 24
- 230000003044 adaptive effect Effects 0.000 claims abstract description 22
- 230000008569 process Effects 0.000 claims description 33
- 239000011159 matrix material Substances 0.000 claims description 24
- 230000003993 interaction Effects 0.000 claims description 7
- 238000002156 mixing Methods 0.000 claims description 3
- 230000001788 irregular Effects 0.000 abstract description 3
- 230000000875 corresponding effect Effects 0.000 description 22
- 238000010586 diagram Methods 0.000 description 16
- 238000003860 storage Methods 0.000 description 14
- 230000007704 transition Effects 0.000 description 11
- 230000006870 function Effects 0.000 description 10
- 238000004590 computer program Methods 0.000 description 9
- 238000004088 simulation Methods 0.000 description 7
- 230000001133 acceleration Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 230000000052 comparative effect Effects 0.000 description 3
- 230000001186 cumulative effect Effects 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000005562 fading Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000000739 chaotic effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003825 pressing Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a dynamic interference source tracking method and a system, which are used for carrying out space orthogonal decomposition on a motion state in a three-dimensional space to three orthogonal directions to obtain a state vector; respectively adopting an interactive multi-model method to carry out model matching in three orthogonal directions to obtain a motion model; applying the motion model obtained by matching to a filtering tracking method, and respectively adopting an adaptive volume Kalman filtering method combined with an improved noise estimator to perform tracking filtering in three orthogonal directions to obtain a filtering tracking result in each direction; and performing vector superposition on the obtained filtering tracking result in each direction to obtain a final tracking result in the three-dimensional space. The invention adopts an interactive multi-model algorithm based on motion state space decomposition to realize model matching of irregular motion interference sources with lower complexity, and adopts an adaptive volume Kalman filtering algorithm combined with an improved noise estimator to realize continuous and accurate tracking of dynamic interference sources.
Description
Technical Field
The invention belongs to the technical field of target tracking, and particularly relates to a dynamic interference source tracking method and system.
Background
The Global Navigation Satellite System (GNSS) is a generic name of a Satellite Navigation System that provides continuous positioning, navigation, and Time service (PVT) for terrestrial users through a space Satellite constellation. Currently, there are four satellite navigation systems around the world, namely GPS in the united states, GLONASS in russia, beidou in china, and galileo in europe. In addition, there are respective regional satellite navigation systems in japan and india. The initial birth of GNSS is for military use, but with the rapid development of economy and technology, the demand of the civilian field for GNSS is continuously increasing, and in the current society, GNSS plays a crucial role in both military and civilian use and development.
Because of the importance of GNSS, interference techniques for GNSS are continuously developed, and the interference techniques can be classified into two types, namely, compressive interference and deceptive interference, according to different interference modes. The pressing type interference enables a GNSS receiver to be incapable of capturing and tracking real satellite signals by emitting high-power interference signals, so that positioning cannot be achieved; the deceptive jamming enables the GNSS receiver to obtain wrong positioning results by transmitting satellite signals carrying false information, and further can directly induce a target to go to a specified place.
Compared with a static interference source, the interference source carried on a maneuvering platform has a larger influence range due to the maneuverability and is more difficult to position. In order to deal with such interference sources, a maneuvering target tracking technology is required to dynamically track the traveling track of the maneuvering target. For a mobile platform, the change of the motion state of the mobile platform cannot be predicted in advance, so that only the acquired data can be tracked to reasonably infer the future state of the mobile platform. To accurately track a maneuvering target, an accurate motion state model needs to be established for the maneuvering target, and then observed data is subjected to filtering tracking through an accurate filtering algorithm. For an unknown dynamic interference source, a motion state model of the unknown dynamic interference source cannot be acquired a priori, and an interactive multi-model algorithm can be adopted to describe the actual motion state of the unknown dynamic interference source.
However, when the mobility of the maneuvering platform where the interference source is located is high, the number of models required by the multi-model algorithm is increased more and more, so that the complexity of the algorithm is increased suddenly, and the timeliness of tracking filtering is seriously affected. Considering that the motion of an object in a three-dimensional space can be decomposed into three mutually orthogonal one-dimensional motions in three directions, the motion state of an interference source at each moment is subjected to orthogonal decomposition, the motion state is divided into three one-dimensional motions to be tracked respectively, and then the results are combined to obtain the motion state prediction of the three-dimensional space. For one-dimensional motion, the motion state is only three motion states of uniform motion, uniform variable-speed motion and variable accelerated motion, so that the model set in a multi-model algorithm does not need to be expanded. In addition, in consideration of the unknown and variable noise influence when observing the dynamic interference source, an improved adaptive volume kalman filtering algorithm based on a Sage-Husa noise estimator is designed in the chapter and is used for tracking the unknown and variable dynamic interference source.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a dynamic interference source tracking method and system for overcoming the defects in the prior art, wherein a matching filtering algorithm for an interference source motion model is established by using an interactive multi-model method based on motion state space orthogonal decomposition, and then an improved time-varying noise estimator is combined with a volume kalman filtering algorithm to track the trajectory of the interference source.
The invention adopts the following technical scheme:
a dynamic interference source tracking method, comprising the steps of:
s1, performing space orthogonal decomposition on a motion state in a three-dimensional space to three orthogonal directions to obtain a state vector;
s2, performing model matching on the state vector obtained in the step S1 in three orthogonal directions by respectively adopting an interactive multi-model method to obtain a motion model;
s3, applying the motion model obtained by matching in the step S2 to a filtering tracking method, and respectively adopting a self-adaptive volume Kalman filtering method combined with an improved noise estimator to perform tracking filtering in three orthogonal directions to obtain a filtering tracking result in each direction;
and S4, performing vector superposition on the filtering tracking result in each direction obtained in the step S3 to obtain a final tracking result in the three-dimensional space.
Specifically, in step S1, the target tracking problem in the three-dimensional space is decomposed into target tracking problems in three one-dimensional directions, specifically:
x x,k ＝F x x x,k-1 +v x,k-1
x y,k ＝F y x y,k-1 +v y,k-1
x z,k ＝F z x z,k-1 +v z,k-1
wherein x is x,k ,x y,k ,x z,k Motion state vectors of the interference source in x, y and z directions at the moment k, F x ,F y ,F z Respectively, the motion state model of the interference source in the x, y and z directions, v x,k-1 ,v y,k-1 ,v z,k-1 The noise generated during the tracking filtering process in the x, y and z directions respectively.
Specifically, step S2 specifically includes:
firstly, input interaction is carried out on the input state corresponding to each model according to model probability to obtain the input state x of each model mi(k-1) And corresponding state covariance P mi(k-1) Then using the interacted input state x mi(k-1) And corresponding state covariance P mi(k-1) As filtering input, sending the input to corresponding filter for parallel filtering to obtain the state estimation result x at the current moment i(k) And P i(k) And updating the model probability according to the likelihood function of the observation value, and performing weighted mixing on the output result of the parallel filtering and the updated model probability to obtain a corresponding filtering output value.
Further, the corresponding filtering output value is specifically:
wherein x is k The motion state of the interference source at the moment k, M is the number of motion state models in the model set, u i(k) Probability, x, corresponding to model i i(k) For the input state at time k of the filtering algorithm using model i, P k Is the state covariance at time k, P i(k) The state covariance at time k is the filter algorithm using model i.
Further, the output of each modelEnter state x mi(k-1) And corresponding state covariance P mi(k-1) The method specifically comprises the following steps:
wherein u is ji(k-1) The probability of changing the model of the motion state of the interference source from the model j to the model i, M is the number of the models in the model set, x j(k-1) For the input state of the filtering algorithm using model j at time k-1, P j(k-1) For the filtering algorithm using model j, the state covariance matrix, x, at time k-1 mi(k-1) Is a weighted mixture of input states of a filtering algorithm employing different motion models according to model probabilities.
Specifically, step S3 specifically includes:
carrying out filtering tracking on the interference source by using a cubature Kalman filtering algorithm, firstly estimating a state value at the current moment by using a state estimation value at the previous moment to obtain a state prediction value at the current moment; then, correcting the predicted value by using the observed value as a reference, and taking the corrected result as a state estimation result at the current moment; and finally, estimating the noise parameters according to the observed values and the estimated values, and correcting related parameters in the filtering algorithm.
Further, taking the corrected result as the state estimation result of the current time specifically includes:
x k ＝x k|k-1 +K(z k -z k|k-1 )
P k ＝P k|k-1 -KP zz K T
wherein x is k For motion state estimation of the interference source at time k, P k For estimation of the state covariance at time k, x k|k-1 For the predicted motion state of the interferer at time k-1 based on the motion state of the interferer at time k-1, P k|k-1 Is k atPredicted value of the state covariance matrix of the moment, K being Kalman gain, z k Is an observed value at time k, z k|k-1 For a predicted observed value at time k, P zz To observe the covariance matrix.
Further, updating the filter parameters according to the observed values is as follows:
R k ＝(1-λ k )R k-1 +λ k [(z k -z k|k-1 )(z k -z k|k-1 ) T ]
Q k ＝(1-λ k )Q k-1 +λ k [(x k -x k|k-1 )(x k -x k|k-1 ) T ]
＝(1-λ k )Q k-1 +λ k [K(z k -z k|k-1 )(z k -z k|k-1 ) T K T ]
updating the filter parameters according to the estimated values is as follows:
R k ＝(1-λ k )R k-1 +λ k P zz
Q k ＝(1-λ k )Q k-1 +λ k KP zz K T
＝(1-λ k )Q k-1 +λ k (P k|k-1 -P k )
wherein λ is k As a weighting coefficient, R k Process noise variance at time k, Q k Is the observed noise variance at time k, z k Is an observed value at time k, z k|k-1 For predicted observed values at time K, K being the Kalman gain, R k-1 Process noise variance at time k, Q k-1 Is the observed noise variance, P, at time k zz To observe the covariance matrix, P k Is the state covariance matrix at time k.
Further, the weighting factor λ k The method specifically comprises the following steps:
wherein alpha is a forgetting factor.
Another technical solution of the present invention is a dynamic interference source tracking system, including:
the decomposition module is used for carrying out space orthogonal decomposition on the motion state in the three-dimensional space to three orthogonal directions to obtain a state vector;
the matching module is used for performing model matching on the state vector obtained by the decomposition module in three orthogonal directions by respectively adopting an interactive multi-model method to obtain a motion model;
the filtering module is used for applying the motion model obtained by matching of the matching module to a filtering tracking method, and tracking filtering is carried out in three orthogonal directions by adopting a self-adaptive volume Kalman filtering method combined with an improved noise estimator respectively to obtain a filtering tracking result in each direction;
and the tracking module is used for performing vector superposition on the filtering tracking result in each direction obtained by the filtering module to obtain the final tracking result in the three-dimensional space.
Compared with the prior art, the invention at least has the following beneficial effects:
according to the dynamic interference source tracking method, the movement of the three-dimensional space is decomposed into the superposition of the movement in a plurality of one-dimensional spaces, and the model matching is performed by adopting an interactive multi-model algorithm, so that the movement state of the interference source is more accurately described, and higher tracking precision can be achieved. The robustness of the filtering tracking algorithm is improved by improving the method for updating the relevant parameters according to the observation values in the adaptive volume Kalman filtering algorithm into the method for updating the relevant parameters according to the observation values and the estimation values. The convergence speed of the noise estimation is improved by modifying the fading memory index of the time-varying noise estimator from a constant to a variable.
Furthermore, the target tracking problem in the three-dimensional space is decomposed into the target tracking problems in three one-dimensional directions, so that the complex problem can be simplified. For a moving target in a three-dimensional space, the change of the motion state of the moving target depends on the numerical value and the direction of the acceleration, different motion state models can be dozens of models, and if the model matching accuracy is improved, the algorithm complexity is greatly increased. For a moving target in a one-dimensional space, the moving target only moves linearly, so that the change of the corresponding motion state only depends on the change of the acceleration value, and the complexity of matching of a motion model can be greatly simplified.
Furthermore, for one-dimensional motion, the corresponding motion states only include uniform-speed linear motion, uniform-speed-changing linear motion and variable-speed linear motion, so that a model set can be correspondingly established by using three motion state models and applied to an interactive multi-model algorithm, and accurate matching of the interference source motion state model is realized.
Further, the filtering output value is a motion state estimation result obtained by a corresponding filtering tracking algorithm under each motion state model, and the motion state estimation result of the target interference source at the current moment can be obtained by performing mixed weighting on the motion state estimation result.
Further, here, the mixing of the input state values by the model probabilities is a necessary step of the interactive multi-model algorithm. The interactive multi-model algorithm is widely applied in the field of target tracking, and is only applied to the scheme.
Further, the motion state of the interference source at each time is estimated through step S3.
Furthermore, a volume Kalman filtering algorithm is adopted as the filtering tracking algorithm of the scheme, and the core steps of the volume Kalman filtering algorithm are that firstly, the motion state of the current moment is estimated according to the motion state of the target at the previous moment, and then, the estimation result is corrected according to the observation value, so that the estimation precision of the motion state is improved, and the influence of process noise on state estimation is reduced.
Furthermore, the filtering parameter is updated through the observed value and the estimated value, and the filtering tracking precision can be effectively improved.
Further, the purpose of the weighting coefficients is to increase the speed of noise estimation in noisy time-varying scenarios. By using the fading memory index which can change with time, the weight of recent data in noise estimation can be increased, and therefore, a noise estimation result closer to the true value of the current time can be obtained at each time.
In conclusion, the invention adopts an interactive multi-model algorithm based on motion state space decomposition to realize model matching of irregular motion interference sources with lower complexity, and adopts an adaptive volume Kalman filtering algorithm combined with an improved noise estimator to realize continuous and accurate tracking of dynamic interference sources.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a system scenario diagram of the present invention;
FIG. 2 is a flow chart of the method of the present invention;
FIG. 3 is a schematic diagram of the variation of tracking RMS error with time according to the present invention, wherein (a) is the comparison of results in x, y, and z directions, and (b) is the comparison of results as a whole;
FIG. 4 is a schematic diagram of the accumulated RMS error as a function of time for the present invention, wherein (a) is the comparison of the results in the x, y, z directions and (b) is the comparison of the results as a whole;
FIG. 5 is a schematic diagram of the variation of tracking RMS error with time in a noise-segmented time-varying scene according to the present invention, wherein (a) is the comparison of results in three directions of x, y and z, and (b) is the comparison of results as a whole;
FIG. 6 is a diagram showing the variation of the accumulated RMS error with time in a noise-segmented time-varying scenario, where (a) is the comparison of the results in the three directions x, y, and z, and (b) is the comparison of the results as a whole;
FIG. 7 is a schematic diagram of the variation of tracking RMS error with time under a completely random time-varying noise scene according to the present invention, wherein (a) is the comparison of the results in the three directions x, y and z, and (b) is the comparison of the results as a whole;
FIG. 8 is a diagram illustrating the variation of the accumulated RMS error with time in a noisy completely random time-varying scenario, where (a) is the comparison of the results in the three directions x, y, and z, and (b) is the comparison of the results as a whole;
FIG. 9 is a tracking RMS error comparison diagram of the adaptive filtering algorithm and the non-adaptive filtering algorithm of the present invention in a noise completely random time varying scenario, wherein (a) is the comparison of results in x, y, z directions, and (b) is the comparison of results as a whole;
fig. 10 is a comparison graph of the accumulated root mean square error of the adaptive filtering algorithm and the non-adaptive filtering algorithm in the noise completely random time-varying scene, where (a) is the comparison of results in the x, y, and z directions, and (b) is the comparison of results as a whole;
FIG. 11 is a comparison of the tracking trace of the present invention and the real trace.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
Referring to fig. 1, a system scenario used by the dynamic interference source tracking method of the present invention includes an interference source carried by a mobile platform, which can implement interference attack on a large area by using mobility, and at the same time, can avoid positioning attack on the wide area by an interference countermeasure system. A large number of base station devices can be used as sensors for observing the motion state of the interference source, and as in the positioning process in chapter iii, signals collected by the base station are fed back to the main control unit, and the main control unit measures and calculates the motion state of the interference source through information contained in multiple paths of signals, and uses the observation result for filtering and tracking to obtain the estimation of the motion trajectory of the dynamic interference source. Besides the vehicle-mounted interference sources which can be maneuvered on a plane, the vehicle-mounted interference sources which are also mounted on an unmanned aerial vehicle platform and can be maneuvered in a three-dimensional space have more complex and changeable motion states, so that the movement track is more difficult to track and predict.
The target tracking problem can be modeled as the following process in practice
Wherein x is k Is the motion state of the object at time k, z k Is the observed state of the target at time k. F is a state transition matrix which converts the motion state x at the current moment k And the motion state x of the previous moment k-1 And (4) associating. As can be seen from equation (1), the motion state of the object at each momentThe motion state at the last moment and the state transition matrix are closely related, and the size of the state transition matrix depends on the selection of the motion model, so that accurate model selection is important for accurate filter tracking. h (-) represents an observation function that associates an observation vector with a state vector. v. of k Process noise generated during state transition, w k To observe the noise, the two are not correlated with each other. The solution of the problem is to use the observed value and the state value at the current moment to estimate the state value at the next moment, and the loop of the process is continued until the filtering tracking is terminated.
The scheme provided by the invention is also divided into two parts, namely an interactive multi-model algorithm based on the target motion state space orthogonal decomposition, and the model matching is realized; the second part is an adaptive volume Kalman filtering process based on the improved time-varying noise estimator. The overall scheme flow chart is shown in figure 2. Before the filtering tracking is started, a filtering algorithm needs to be initialized, and parameters such as a filtering tracking starting point, an observation noise initial value and a process noise initial value are set. After the initialization is completed, the initialized motion state vector is firstly subjected to orthogonal decomposition and decomposed into three mutually orthogonal directions, then the filtering process is carried out in parallel in each direction, and the obtained filtering estimation results are subjected to vector superposition again, so that the final motion state estimation value is obtained. In the whole process, the middle parallel filtering part is continuously carried out, and the observed state value at each moment is also orthogonally decomposed into three directions, and the three directions are respectively involved in the filtering tracking in each direction. For an object moving in one dimension, there are only three possible outcomes to its motion state at each instant: uniform motion, uniform variable motion, and variable motion. Corresponding to three different motion states, three different motion models are arranged in the IMM module to respectively correspond to the three different motion states, so that the vast majority of motion states can be covered, and accurate matching of the target motion state model is achieved. In a filtering algorithm module, a volume Kalman filtering algorithm is selected and used, and is combined with a noise estimator, an adaptive volume Kalman filtering algorithm based on an improved time-varying noise estimator is designed, and errors caused by observation noise and process noise are gradually reduced in a continuous filtering cycle.
The invention discloses a dynamic interference source tracking method, which is an adaptive volume Kalman filtering tracking method combined with an improved time-varying noise estimator, and comprises the following steps:
s1, performing space orthogonal decomposition on a motion state in a three-dimensional space to three orthogonal directions; for a moving object in one dimension, its state vector is
x k ＝[p k v k a k ] T (2)
Wherein p is k 、v k And a k Respectively, the position, velocity, and acceleration of the tracked target. Correspondingly, the state transition matrix in equation (1) is a 3-order matrix. When looking at a motorized target in three-dimensional space, its state vector is
x k ＝[p x,k v x,k a x,k p y,k v y,k a y,k p z,k v z,k a z,k ] T (3)
Correspondingly, the state transition matrix needs to be expanded to a 9 th order matrix as follows:
and F is a state transition matrix corresponding to any motion model. Substituting the formula (3) and the formula (4) into the formula (1) to obtain
The problem of target tracking in three-dimensional space is seen as a combination of tracking problems in three one-dimensional directions. Based on this, consider directly resolving the target tracking problem in three-dimensional space into target tracking problems in three one-dimensional directions, i.e.
The motion state decomposition means that the obtained observation vector and the estimated state vector are decomposed into three mutually orthogonal directions, and three tracking problems as shown in the same formula (6) are formed. Since the problem in three directions is completely consistent, the specific steps from time k-1 to time k in the scheme are described in detail herein by taking the filtering tracking process in any one direction as an example.
S2, in each direction, model matching is carried out by adopting an interactive multi-model algorithm;
the model matching filtering adopts an interactive multi-model algorithm, firstly, the input state corresponding to each model is interacted according to model probability to obtain an interacted input state value, then, the interacted input state value is used as filtering input, and a corresponding filtering output value is obtained. Similarly, interaction is also required for the filtering output, and the model probability is also required to be updated according to the filtering residual obtained under different models.
S201, input interaction
And for the filtering process under each model, an input state value corresponds to, and an output state is obtained through the filtering process. In the interactive multi-model algorithm, a plurality of input state values need to be interacted according to model probability, and thus the real input state value in the filtering algorithm is obtained. Set of hypothesis models C m In total, there are M models M 1 ,m 2 ,...,m M ]For the ith model, the model prediction probability is as follows:
wherein, P ji Representing slave model m j Transfer to model m i Probability of (u) j(k-1) As a model m at the time k-1 j The probability of (c). The model transition probability obtained from the model prediction probability is as follows:
the hybrid input state values of the filter are obtained from the model transition probabilities as follows:
the mixed input is weighted summation of corresponding initial input under each model, and the weight is corresponding model transition probability.
S202, parallel filtering
Input state x under each model obtained after input interaction mi(k-1) Corresponding state covariance P mi(k-1) And the observed value z obtained at the current time k Sending the data to a corresponding filter for parallel filtering processing to obtain a state estimation result x of the current moment i(k) And P i(k) . And the result will participate in the filtering loop at the next instant.
S203, model probability updating
For each model, obtaining a filtering residual d according to the obtained filtering result and observation result i(k) Sum innovation covariance matrix P zi(k) From which a likelihood function of the observed value is obtained
The function represents how close the observed value is to the estimated filtered value, and is a reference when updating the model probability. Based on this, the model m can be converted into i Is updated to
S204, outputting interaction
As with the input interaction, the output results of the parallel filtering need to be weighted and mixed according to the updated model probabilities to obtain the final result
The result is the final filtering output result at this moment.
S3, respectively adopting an adaptive volume Kalman filtering algorithm combined with an improved noise estimator to perform tracking filtering in each direction;
carrying out filtering tracking on the interference source by using a cubature Kalman filtering algorithm, firstly estimating a state value at the current moment by using a state estimation value at the previous moment to obtain a state prediction value at the current moment; and then, correcting the predicted value by using the observed value as a reference, and taking the corrected result as a state estimation result at the current moment. And finally, estimating the noise parameters according to the observed values and the estimated values, and correcting related parameters in the filtering algorithm according to the estimated noise parameters.
S301, initializing parameters
Setting initial parameters of filtering: initial value x of state 0 Initial value of covariance P 0 Observation of initial value of covariance of noise 0 Initial value of covariance of process noise Q 0 。
S302, one-step prediction
Computing the covariance matrix square root
S k-1 ＝chol(P k-1 ) (13)
chol(P k-1 ) Finger pair matrix P k-1 The Cholesky decomposition of (1) can decompose the matrix into an upper triangular matrix and a lower triangular matrix, and the two matrices are transposes of each other.
Calculating volume points
x i,k-1 ＝S k-1 η i +x k-1 (14)
Wherein eta is i Refers to a column vector composed of the I-th column elements in the volume point set, eta = [ I = [) n -I n ]。
Propagation volume point
x i,k|k-1 ＝Fx i,k-1 (15)
State value one-step prediction
Wherein,
for weight, n is the dimension of the state vector.
Covariance one-step prediction
S303, observation correction
Computing the covariance matrix square root
S k|k-1 ＝chol(P k|k-1 ) (18)
Calculating volume points
x i,k-1 ＝S k-1 η i +x k-1 (19)
Propagation volume point
z i,k|k-1 ＝Hx i,k|k-1 (20)
One-step prediction of observed values
Calculating innovation covariance
Calculating cross covariance
Computing Kalman gain
Correcting one-step prediction results
S304, updating the filtering parameters
Updating filter parameters based on observations
Updating filter parameters based on the estimated values
Parameter λ in equations (26) and (27) k Are weighting coefficients.
In order to increase the convergence speed of the filtering algorithm, the invention designs the filtering algorithm into the following form:
wherein alpha is a forgetting factor.
And S4, performing vector superposition on the filtering tracking result in each direction to obtain a final tracking result in the three-dimensional space.
In another embodiment of the present invention, a dynamic interference source tracking system is provided, which can be used to implement the above dynamic interference source tracking method.
The decomposition module is used for carrying out space orthogonal decomposition on the motion state in the three-dimensional space to three orthogonal directions to obtain a state vector;
the matching module is used for performing model matching on the state vector obtained by the decomposition module in three orthogonal directions by respectively adopting an interactive multi-model method to obtain a motion model;
the filtering module is used for applying the motion model obtained by matching of the matching module to a filtering tracking method, and tracking filtering is carried out in three orthogonal directions by adopting a self-adaptive volume Kalman filtering method combined with an improved noise estimator respectively to obtain a filtering tracking result in each direction;
and the tracking module is used for performing vector superposition on the filtering tracking result in each direction obtained by the filtering module to obtain the final tracking result in the three-dimensional space.
In yet another embodiment of the present invention, a terminal device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc., which is a computing core and a control core of the terminal, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor according to the embodiment of the present invention may be used for the operation of the dynamic interference source tracking method, including:
performing space orthogonal decomposition on the motion state in the three-dimensional space to three orthogonal directions to obtain a state vector; performing model matching on the obtained state vectors in three orthogonal directions by adopting an interactive multi-model method to obtain a motion model; applying the motion model obtained by matching to a filtering tracking method, and respectively adopting an adaptive volume Kalman filtering method combined with an improved noise estimator to perform tracking filtering in three orthogonal directions to obtain a filtering tracking result in each direction; and carrying out vector superposition on the obtained filtering tracking result in each direction to obtain a final tracking result in a three-dimensional space.
In still another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a terminal device and is used for storing programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer readable storage medium may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory.
One or more instructions stored in the computer-readable storage medium may be loaded and executed by the processor to implement the corresponding steps of the dynamic interference source tracking method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded by the processor and perform the steps of:
performing space orthogonal decomposition on the motion state in the three-dimensional space to three orthogonal directions to obtain a state vector; performing model matching on the obtained state vectors in three orthogonal directions by respectively adopting an interactive multi-model method to obtain a motion model; applying the motion model obtained by matching to a filtering tracking method, and respectively adopting an adaptive volume Kalman filtering method combined with an improved noise estimator to perform tracking filtering in three orthogonal directions to obtain a filtering tracking result in each direction; and performing vector superposition on the obtained filtering tracking result in each direction to obtain a final tracking result in the three-dimensional space.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Consider a randomly varying acceleration source of disturbance that is not regularly mobile and the scheme described hereinbefore is applied to track this source. The running track of the interference source is obtained by calculating an acceleration value and an initial state value which are randomly generated, and the initial state is set as follows:
p 0 ＝[500m 200m 300m] T
v 0 ＝[10m/s 5m/s 0m/s] T
a 0 ＝[0m/s 2 0m/s 2 10m/s 2 ] T
wherein p is 0 、v 0 And a 0 Initial position, velocity, and acceleration, respectively; amplitude value obedience of accelerationA value of 0m/s 2 Variance of 10 (m/s) 2 ) 2 The direction (azimuth angle and pitch angle) obeys the interval of [ - π, π]Are uniformly distributed. All tracking processes in the simulation lasted 300 moments.
Comparison scheme
Comparative scheme 1: and performing model matching by adopting an interactive multi-model algorithm without decomposing the motion state.
Comparative scheme 2: and performing noise estimation by adopting a time-varying noise estimator.
Comparative scheme 3: and filtering and tracking by adopting a non-adaptive cubature Kalman filtering algorithm.
Figure 3 shows the root mean square error comparison of the tracking results for the two schemes in the three x, y, z directions and overall. The measurement noise in the simulation was set to 10000m 2 And the initial value of the variance of the measurement noise at the start of the filter tracking is set to 1000m 2 The initial value of the covariance of the process noise is set to 100m 2 The simulation result is the average of the results of 100 filtering processes. It can be seen that, in all directions or in the whole, the tracking error convergence speed of the interactive multi-model algorithm based on the motion state space decomposition is higher, the fluctuation amplitude is smaller, a stable value is reached in a short time, and the small fluctuation is kept after the convergence. This shows that by decomposing the motion state and adopting the method of interactive multi-model algorithm, the motion state of the target in each direction can be described more accurately in the filtering process, thereby reducing the tracking error generated in each step and achieving the effect of rapid convergence. The right graph of fig. 3 compares the overall tracking error, and it can be seen that the effect is consistent with the effect in each direction, and the overall motion state space decomposition-based method has a faster convergence rate, so that the tracking error can reach an acceptable level in a short time. The curve in fig. 4 refers to the root mean square error of all previous accumulated tracking results from the true values for both methods. It can be seen that the proposed solution of motion state decomposition has a lower accumulated error, either in three directions or overall, and the accumulated error decreases as the tracking filtering proceeds,this shows that the overall tracking accuracy gradually increases with the continuous filtering until the accuracy limit of the filtering tracking algorithm is reached finally.
Fig. 5 compares an improved noise estimator with a time-varying noise estimator in three directions. In the simulation, the noise variance was set to 10000m in the first 100 moments 2 Jumping to 250000m within time 101-200 2 Then jump back to 10000m 2 . The initial value of the variance of the observed noise is set to 1000m 2 The initial value of the process noise variance is set to 100m 2 The simulation result is the average of the results of 100 filtering processes. It can be seen that the various schemes are substantially identical in both convergence speed and fluctuation amplitude after convergence, but the improved scheme has less fluctuation in the initial stage and also has no fluctuation of a larger magnitude in the error convergence process, which indicates that the improved scheme is more stable in the initial stage. At the first transition of the noise, the performance in the three directions is good, but the overall result is almost the same, and when the noise jumps again, the errors of the two directions start to decrease until convergence. In order to show the performance of the improved scheme and the original scheme more clearly, the accumulated errors of the tracking results obtained by the two schemes are also compared.
The cumulative error results in fig. 6 provide a visual indication of the performance of both schemes. It can be seen that in the initial phase, the improved scheme has less fluctuation in three directions than the scheme of the time-varying noise estimator. However, as can be seen from fig. 6 (a), in the x-direction and z-direction, the improved scheme has a large jump in the accumulated error after the noise jump, and the scheme of the time-varying noise estimator is more stable. As can be seen from fig. 6 (b), the cumulative error of the improved scheme drops faster during the first 100 moments. This is because the improvement sets that the newer data occupies more weight in the noise estimation, and the older data occupies less weight, so that the new data is more sensitive, and the new input observation will have a larger influence on the tracking accuracy at the time of observing the noise jump.
Figures 7 and 8 compare the performance of the improved scheme and the scheme incorporating the time varying noise estimator from two perspectives, respectively. The simulation scene is set to be that the noise variance changes in each moment, and the variance value obeys to 100m 2 ,250000m 2 ]Uniform distribution within the interval. The initial value of the variance of the observed noise is set to 1000m 2 The initial value of the process noise variance is set to 100m 2 The simulation result is the average of the results of 100 filtering processes. It can be seen that in fig. 7, the performances of the two are difficult to distinguish and are close to the same, because when the noise is completely time-varying, the noise at each time may have different variances, and therefore, the accuracy of the noise estimation cannot be improved by the increase of the observed value; in fig. 8, the comparison of the accumulated error is more clear. The left graph shows that the accumulated errors in the three directions are good and bad, and even completely consistent in the y direction, while in the right graph, it can be seen that the accumulated errors of the improved scheme drop faster in the subsequent process, although the performance of the two is quite close in the initial stage. The adaptive volume Kalman filtering algorithm combined with the improved noise estimator has a faster error convergence speed in a time-varying noise scene, and a tracking result can approach a true value more quickly.
Fig. 9 compares root mean square errors of the tracking results of the two schemes in all directions and the overall performance, and it can be seen that the tracking result error obtained by the CKF algorithm always fluctuates greatly, while the tracking error of the ACKF algorithm converges quickly after a short fluctuation and fluctuates around a stable value, and the fluctuation amplitude is much smaller than the obtained result of the CKF algorithm. This shows that the noise estimation can effectively estimate the noise covariance in the filtering process, so that the filtering estimation result can more quickly converge to the vicinity of the true value, and can effectively suppress the error fluctuation which may be generated after convergence. Fig. 10 compares the root mean square of the accumulated tracking errors at each instant for the two methods. It can be seen that the cumulative error of the ACKF shows a very obvious downward trend with the continuous filtering, while the result of the CKF algorithm is always maintained around a stable value, and no convergence trend is observed. The method shows that the ACKF algorithm combined with noise estimation can effectively reduce accumulated errors, the tracking errors are continuously reduced along with the continuous filtering until the tracking errors reach the accuracy limit, and the accuracy limit of the standard CKF algorithm is obviously higher than that of the adaptive CKF algorithm. Therefore, under the scene of noise time variation, the volume Kalman filtering algorithm combined with noise estimation can achieve a more excellent tracking effect compared with the standard volume Kalman filtering algorithm. In practice, a maneuvering interference source often spans a larger spatial scale, so that noise also changes along with different positions of the maneuvering interference source, and a noise time-varying scene is more consistent with an actual scene, so that the adaptive volume kalman filtering algorithm is more suitable for interference source tracking in practice.
Fig. 11 shows a comparison of the tracking result obtained by the tracking scheme with the real track, and it can be seen that after an initial short chaotic state is experienced, the fluctuation amplitude of the tracking result around the real track becomes gradually smaller, and finally slowly approaches the real track.
In summary, the dynamic interference source tracking method and system of the present invention can effectively improve the tracking accuracy and filtering tracking robustness of the irregular motion dynamic interference source. In the stage of establishing a motion model, an interactive multi-model algorithm based on motion state space orthogonal decomposition is adopted to avoid the expansion of the scale of a model set and improve the model matching precision of a target; in the filtering and tracking stage, the motion state of the target is estimated by adopting an adaptive volume Kalman filtering algorithm based on an improved time-varying noise estimator, the noise estimator accurately estimates relevant parameters of actual observation noise and process noise and is used for guiding gain calculation and state updating in the filtering algorithm, and finally higher tracking precision is achieved compared with a non-adaptive filtering algorithm, and the improved time-varying noise estimator has higher error convergence speed compared with an original scheme.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (10)
1. A method for dynamic interference source tracking, comprising the steps of:
s1, carrying out space orthogonal decomposition on a motion state in a three-dimensional space to three orthogonal directions to obtain a state vector;
s2, performing model matching on the state vector obtained in the step S1 in three orthogonal directions by respectively adopting an interactive multi-model method to obtain a motion model;
s3, applying the motion model obtained by matching in the step S2 to a filtering tracking method, and respectively adopting an adaptive volume Kalman filtering method combined with an improved noise estimator to perform tracking filtering in three orthogonal directions to obtain a filtering tracking result in each direction;
and S4, performing vector superposition on the filtering tracking result in each direction obtained in the step S3 to obtain a final tracking result in the three-dimensional space.
2. The method according to claim 1, wherein in step S1, the target tracking problem in the three-dimensional space is decomposed into target tracking problems in three one-dimensional directions, specifically:
x x,k ＝F x x x,k-1 +v x,k-1
x y,k ＝F y x y,k-1 +v y,k-1
x z,k ＝F z x z,k-1 +v z,k-1
wherein x is x,k ,x y,k ,x z,k Motion state vectors of the interference source in x, y and z directions at the moment k, F x ,F y ,F z Respectively, the motion state model of the interference source in the x, y and z directions, v x,k-1 ,v y,k-1 ,v z,k-1 The noise generated in the tracking filtering process in the x direction, the y direction and the z direction respectively.
3. The method according to claim 1, wherein step S2 is specifically:
firstly, input interaction is carried out on the input state corresponding to each model according to model probability to obtain the input state x of each model mi(k-1) And corresponding state covariance P mi(k-1) Then using the interacted input state x mi(k-1) And corresponding state covariance P mi(k-1) As filtering input, sending the input to corresponding filter for parallel filtering to obtain the state estimation result x at the current moment i(k) And P i(k) And updating the model probability according to the likelihood function of the observation value, and performing weighted mixing on the output result of the parallel filtering and the updated model probability to obtain a corresponding filtering output value.
4. The method according to claim 3, wherein the corresponding filter output values are in particular:
wherein x is k The motion state of the interference source at the moment k, M is the number of motion state models in the model set, u i(k) Probability, x, corresponding to model i i(k) For the input state at time k of the filtering algorithm using model i, P k Is the state covariance at time k, P i(k) The state covariance at time k is the filter algorithm using model i.
5. Method according to claim 3, characterized in that the input state x of each model mi(k-1) And corresponding state covariance P mi(k-1) The method specifically comprises the following steps:
wherein u is ji(k-1) The probability of changing the interference source motion state model from the model j to the model i is shown, M is the number of the motion state models in the model set, x j(k-1) For the input state of the filtering algorithm using model j at time k-1, P j(k-1) Is the state covariance matrix at time k-1 for the filtering algorithm using model j.
6. The method according to claim 1, wherein step S3 is specifically:
carrying out filtering tracking on the interference source by using a cubature Kalman filtering algorithm, and firstly estimating a state value at the current moment by using a state estimation value at the previous moment to obtain a state predicted value at the current moment; then, the observation value is used as a reference to correct the predicted value, and the corrected result is used as the state estimation result of the current moment; and finally, estimating the noise parameters according to the observed values and the estimated values, and correcting related parameters in the filtering algorithm.
7. The method according to claim 6, wherein the modified result is used as a state estimation result at the current time, and specifically comprises:
x k ＝x k|k-1 +K(z k -z k|k-1 )
P k ＝P k|k-1 -KP zz K T
wherein x is k For the state of motion of the source of interference at time k, P k Is the state covariance at time k, x k|k-1 For the predicted motion state of the interference source at time k, P, based on the motion state of the interference source at time k-1 k|k-1 Is the predicted value of the state covariance matrix at time K, K being the Kalman gain, z k Is an observed value at time k, z k|k-1 As expectedObserved value at time k, P zz To observe the covariance matrix.
8. The method of claim 6, wherein updating the filter parameters based on the observations is as follows:
R k ＝(1-λ k )R k-1 +λ k [(z k -z k|k-1 )(z k -z k|k-1 ) T ]
Q k ＝(1-λ k )Q k-1 +λ k [(x k -x k|k-1 )(x k -x k|k-1 ) T ]
＝(1-λ k )Q k-1 +λ k [K(z k -z k|k-1 )(z k -z k|k-1 ) T K T ]
updating the filter parameters according to the estimated values is as follows:
R k ＝(1-λ k )R k-1 +λ k P zz
Q k ＝(1-λ k )Q k-1 +λ k KP zz K T
＝(1-λ k )Q k-1 +λ k (P k|k-1 -P k )
wherein λ is k As a weighting coefficient, R k Process noise variance at time k, Q k Is the observed noise variance at time k, z k Is an observed value at time k, z k|k-1 For predicted observed values at time K, K being the Kalman gain, R k-1 Process noise variance, Q, at time k-1 k-1 Is the observed noise variance, P, at time k-1 zz To observe the covariance matrix, P k Is the state covariance at time k.
9. Method according to claim 8, characterized in that the weighting factor λ k The method specifically comprises the following steps:
wherein alpha is a forgetting factor.
10. A dynamic interference source tracking system, comprising:
the decomposition module is used for carrying out space orthogonal decomposition on the motion state in the three-dimensional space to three orthogonal directions to obtain a state vector;
the matching module is used for performing model matching on the state vectors obtained by the decomposition module in three orthogonal directions by respectively adopting an interactive multi-model method to obtain a motion model;
the filtering module is used for applying the motion model obtained by matching of the matching module to a filtering tracking method, and tracking filtering is carried out in three orthogonal directions by adopting a self-adaptive volume Kalman filtering method combined with an improved noise estimator respectively to obtain a filtering tracking result in each direction;
and the tracking module is used for performing vector superposition on the filtering tracking result in each direction obtained by the filtering module to obtain the final tracking result in the three-dimensional space.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110656381.9A CN113466904B (en) | 2021-06-11 | 2021-06-11 | Dynamic interference source tracking method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110656381.9A CN113466904B (en) | 2021-06-11 | 2021-06-11 | Dynamic interference source tracking method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113466904A CN113466904A (en) | 2021-10-01 |
| CN113466904B true CN113466904B (en) | 2022-12-09 |
Family
ID=77869814
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110656381.9A Active CN113466904B (en) | 2021-06-11 | 2021-06-11 | Dynamic interference source tracking method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113466904B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115460047B (en) * | 2022-08-02 | 2024-06-11 | 北京睿信丰科技有限公司 | Method and device for quickly tracking frequency offset and distance under complex channel and electronic equipment |
| CN116299576B (en) * | 2023-05-12 | 2023-12-12 | 中国人民解放军国防科技大学 | Deception jamming detection method and device for integrated navigation system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104459751A (en) * | 2014-11-24 | 2015-03-25 | 中国空间技术研究院 | Double-station radar space target relative navigation method based on GNSS reflected signals |
| CN104915970A (en) * | 2015-06-12 | 2015-09-16 | 南京邮电大学 | Multi-target tracking method based on track association |
| CN109000642A (en) * | 2018-05-25 | 2018-12-14 | 哈尔滨工程大学 | A kind of improved strong tracking volume Kalman filtering Combinated navigation method |
| CN110389362A (en) * | 2018-04-23 | 2019-10-29 | 三星电子株式会社 | Global navigational satellite system receiver and its method |
| CN112731494A (en) * | 2020-12-23 | 2021-04-30 | 大唐半导体设计有限公司 | Method and device for realizing positioning calculation |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040006424A1 (en) * | 2002-06-28 | 2004-01-08 | Joyce Glenn J. | Control system for tracking and targeting multiple autonomous objects |
| CN102568004A (en) * | 2011-12-22 | 2012-07-11 | 南昌航空大学 | Tracking algorithm for high maneuvering targets |
-
2021
- 2021-06-11 CN CN202110656381.9A patent/CN113466904B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104459751A (en) * | 2014-11-24 | 2015-03-25 | 中国空间技术研究院 | Double-station radar space target relative navigation method based on GNSS reflected signals |
| CN104915970A (en) * | 2015-06-12 | 2015-09-16 | 南京邮电大学 | Multi-target tracking method based on track association |
| CN110389362A (en) * | 2018-04-23 | 2019-10-29 | 三星电子株式会社 | Global navigational satellite system receiver and its method |
| CN109000642A (en) * | 2018-05-25 | 2018-12-14 | 哈尔滨工程大学 | A kind of improved strong tracking volume Kalman filtering Combinated navigation method |
| CN112731494A (en) * | 2020-12-23 | 2021-04-30 | 大唐半导体设计有限公司 | Method and device for realizing positioning calculation |
Non-Patent Citations (2)
| Title |
|---|
| 基于平方根Unscented卡尔曼滤波的车辆融合跟踪;陈莹等;《西安交通大学学报》;20050630;第39卷(第6期);第594-597页 * |
| 基于强跟踪平方根容积卡尔曼滤波的纯方位目标运动分析方法;王艳艳等;《计算机测量与控制》;20161125(第11期);第136-140页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113466904A (en) | 2021-10-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113466904B (en) | Dynamic interference source tracking method and system | |
| Xiong et al. | A robust single GPS navigation and positioning algorithm based on strong tracking filtering | |
| CN110849369A (en) | Robot tracking method, device, equipment and computer readable storage medium | |
| CN109827579A (en) | The method and system of Filtering Model real time correction in a kind of integrated positioning | |
| CN111008364A (en) | Method and system for cooperative passive positioning of double observers | |
| CN108549062B (en) | System platform for sea surface search radar and multi-model target tracking method | |
| KR102568948B1 (en) | Method and apparatus for determining velocity of obstacle, electronic device, storage medium and program | |
| CN116608859A (en) | Navigation method, storage medium and device of self-adaptive unscented Kalman filtering based on threshold processing | |
| Steinbring et al. | GPU-accelerated progressive Gaussian filtering with applications to extended object tracking | |
| CN113156368B (en) | Error parameter identification co-location method based on factor graph | |
| CN114018250B (en) | Inertial navigation method, electronic device, storage medium and computer program product | |
| Wang et al. | An improved robust estimation method for gnss/sins under gnss-challenged environment | |
| CN114565010A (en) | Adaptive Kalman noise estimation method and system based on data fusion | |
| Jwo et al. | Fuzzy adaptive interacting multiple model unscented Kalman filter for integrated navigation | |
| Ritter et al. | Decentralized dynamic data-driven monitoring of atmospheric dispersion processes | |
| Chen et al. | Using IMM adaptive estimator in GPS positioning | |
| Sun et al. | A modified marginalized Kalman filter for maneuvering target tracking | |
| Lv et al. | Underwater target tracking based on strong tracking sparse grid quadrature filter | |
| CN117930136B (en) | Multi-mobile sound source positioning method based on self-adaptive complex value return-to-zero neural dynamics | |
| Peng et al. | Maneuvering Target Tracking Using Current Statistical Model Based Adaptive UKF for Wireless Sensor Network. | |
| Altendorfer | Observable dynamics and coordinate systems for automotive target tracking | |
| Zhang et al. | Dynamic Interference Source Tracking Based on Motion State Decomposition | |
| CN117784133B (en) | Synthetic aperture passive positioning iterative motion compensation method and system | |
| CN116659487B (en) | Pose adjustment method, pose adjustment device, electronic equipment and readable storage medium | |
| CN107255824A (en) | A kind of high-dynamic GNSS vector tracking method that error is combined with non-error modeling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |