Target trajectory determination method based on mobile platform and related equipment

Abstract

The present invention discloses a method for determining a target trajectory based on a mobile platform and related equipment. The method comprises: constructing a first predicted motion trajectory, a second predicted motion trajectory and a third predicted motion trajectory based on the mobile platform through a uniform speed model, a uniform acceleration model and an enhanced cooperative turning model; calculating multiple hybrid model probabilities based on the three motion trajectories using a volumetric Kalman filter algorithm, an interactive multi-model filter algorithm and a Markov state transfer matrix; and obtaining the target trajectory by weighted fusion of multiple model probabilities and the volumetric Kalman filter value. This method combines the advantages of the volumetric Kalman algorithm and the interactive multi-model algorithm, can reduce the influence of noise on the determination of the motion trajectory, and can accurately track targets with complex motion trajectories and strong maneuverability.

Description

Technical Field

[0001] The present invention relates to the field of target tracking, and more specifically, to a target trajectory determination method based on a mobile platform and related equipment. Background Art

[0002] The target trajectory determination based on the mobile platform will be disturbed by a lot of noise data. In recent years, with the development of the cubature Kalman filter method, the influence of noise on the tracking effect can be better reduced. However, when the target trajectory is more complex, a single motion model is difficult to fit the target trajectory well when the target trajectory changes greatly. The interactive multi-model control algorithm performs real-time maneuver model detection by setting a model filter corresponding to the number of possible target models, and obtains the current optimal estimation state through weighted calculation, thereby achieving the purpose of model adaptive tracking. However, when the target is highly maneuverable, the tracking target error is large.

[0003] Therefore, it is necessary to propose a target trajectory determination method based on a mobile platform to at least partially solve the problems existing in the prior art. Summary of the invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention, which will be further described in detail in the Detailed Description of the Invention. The Summary of the Invention does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0005] In order to at least partially solve the above problems, in a first aspect, the present invention provides a target trajectory determination method based on a mobile platform, the method comprising:

[0006] According to the uniform speed model, the uniform acceleration model and the enhanced cooperative turning model, a first predicted motion trajectory, a second predicted motion trajectory and a third predicted motion trajectory of the target are respectively constructed based on the above mobile platform;

[0007] By using a volumetric Kalman filter algorithm, according to the first predicted motion trajectory, the second predicted motion trajectory and the third predicted motion trajectory, the volumetric particles and the volumetric Kalman filter value of each predicted motion trajectory are calculated respectively;

[0008] Using an interactive multi-model filtering algorithm, the mixed model probability of each predicted motion trajectory is calculated according to the volumetric particles, the Markov state transfer matrix and the enhanced cooperative turning model;

[0009] The target trajectory is obtained by performing weighted fusion according to each of the above hybrid model probabilities and the above volumetric Kalman filter values.

[0010] Optionally, the first predicted motion trajectory, the second predicted motion trajectory and the third predicted motion trajectory of the target are respectively constructed based on the mobile platform according to the uniform speed model, the uniform acceleration model and the enhanced cooperative turning model, including:

[0011] Constructing a state vector of the target, wherein the state vector includes the position, velocity and acceleration of the target;

[0012] Constructing the first predicted motion trajectory of the target based on the mobile platform according to the state vector, the noise data, the maneuvering acceleration of the target and the uniform velocity model, wherein the noise data includes at least one of Gaussian white noise, process noise of the mobile platform and process noise of the target motion;

[0013] Constructing the second predicted motion trajectory of the target based on the mobile platform according to the state vector, the noise data, the maneuvering acceleration and the uniform acceleration model;

[0014] The third predicted motion trajectory of the target based on the mobile platform is constructed according to the state vector, the noise data, the maneuvering acceleration, and the enhanced cooperative turning model.

[0015] Optionally, the interactive multi-model filtering algorithm is used to calculate the mixed model probability of each predicted motion trajectory according to the volume particles, the Markov state transfer matrix and the enhanced cooperative turning model, including:

[0016] Calculate the state equation and measurement equation of each of the above-mentioned volume particles according to the above-mentioned enhanced cooperative turning model;

[0017] The mixed model probability is calculated based on the state equation, the measurement equation, the noise data and the Markov state transfer matrix.

[0018] Optionally, the calculation of the hybrid model probability based on the state equation, the measurement equation, the noise data and the Markov state transfer matrix includes:

[0019] Calculate the particle observation average value based on the above measurement equation and the above volume particles;

[0020] Calculate the innovation filter covariance using the particle observation average, the noise data and the measurement equation;

[0021] The above-mentioned mixed model probability is calculated based on the above-mentioned new information filter covariance and the above-mentioned Markov state transfer matrix.

[0022] Optionally, the calculation of the innovation filter covariance using the particle observation average, the noise data and the measurement equation includes:

[0023] Calculate the predicted covariance based on the above particle observation averages;

[0024] Calculating the measurement covariance using the particle observation average, the noise data and the measurement equation;

[0025] The new information filtering covariance is obtained based on the above prediction covariance and the above measurement covariance.

[0026] Optionally, the calculation of the hybrid model probability based on the innovation filter covariance and the Markov state transfer matrix includes:

[0027] Calculate the likelihood function based on the above-mentioned innovation filter covariance;

[0028] The above-mentioned mixed model probability is calculated using the above-mentioned likelihood function and the above-mentioned Markov state transfer matrix.

[0029] Optionally, the above method further includes:

[0030] The Markov transfer matrix is ​​modified based on the mixed model probability.

[0031] In a second aspect, the present invention further proposes a target trajectory determination device based on a mobile platform, comprising:

[0032] Initialization unit: used for respectively constructing a first predicted motion trajectory, a second predicted motion trajectory and a third predicted motion trajectory of the target based on the above mobile platform according to a uniform speed model, a uniform acceleration model and an enhanced cooperative turning model;

[0033] A filtering unit: used for calculating the volumetric particles and the volumetric Kalman filter value of each predicted motion trajectory respectively according to the first predicted motion trajectory, the second predicted motion trajectory and the third predicted motion trajectory by using a volumetric Kalman filter algorithm;

[0034] Interaction unit: using an interactive multi-model filtering algorithm, the mixed model probability of each predicted motion trajectory is calculated according to the above-mentioned volumetric particles, Markov state transfer matrix and the above-mentioned enhanced cooperative turning model;

[0035] The target acquisition unit is used to acquire the target trajectory by performing weighted fusion according to each of the above hybrid model probabilities and the above volumetric Kalman filter values.

[0036] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the method for determining a target trajectory based on a mobile platform as described in any one of the first aspects above when executing the computer program stored in the memory.

[0037] In a fourth aspect, the present invention further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for determining a target trajectory based on a mobile platform according to any one of the above items in the first aspect is implemented.

[0038] In summary, this scheme constructs the first predicted motion trajectory, the second predicted motion trajectory and the third predicted motion trajectory based on the mobile platform through a uniform speed model, a uniform acceleration model and an enhanced collaborative turning model, and obtains multiple hybrid model probabilities through volumetric Kalman filtering algorithm, interactive multi-model filtering algorithm and Markov state transfer matrix calculation. The method of obtaining the target trajectory by weighted fusion of multiple model probabilities and the volumetric Kalman filtering value combines the advantages of the volumetric Kalman algorithm and the interactive multi-model algorithm, which can not only reduce the influence of noise on the determination of the motion trajectory, but also accurately track targets with complex motion trajectories and strong maneuverability.

[0039] The target trajectory determination method based on a mobile platform of the present invention, other advantages, objectives and features of the present invention will be partially reflected through the following description, and partly will also be understood by technicians in this field through research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0041] Figure 1 A schematic diagram of a method for determining a target trajectory based on a mobile platform provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of another method for determining a target trajectory based on a mobile platform provided in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of the root mean square error of a distance of a simulation result provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of a velocity root mean square error of a simulation result provided in an embodiment of the present application;

[0045] Figure 5 A schematic diagram of a target trajectory determination device based on a mobile platform provided in an embodiment of the present application

[0046] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The embodiments of the present application provide a target trajectory determination method based on a mobile platform and related equipment, which combine the advantages of the volumetric Kalman algorithm and the interactive multi-model algorithm, can not only reduce the impact of noise on motion trajectory determination, but also accurately track targets with complex motion trajectories and strong maneuverability.

[0048] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0049] See also Figure 1 , is a flow chart of a method for determining a target trajectory based on a mobile platform provided in an embodiment of the present application, which may specifically include:

[0050] S110, constructing a first predicted motion trajectory, a second predicted motion trajectory, and a third predicted motion trajectory of the target based on the mobile platform according to a uniform speed model, a uniform acceleration model, and an enhanced cooperative turning model;

[0051] Specifically, in the above mobile platform, a first predicted motion trajectory of the target is constructed according to the uniform speed model, a second predicted motion trajectory of the target is determined using the uniform acceleration model, and a third predicted motion trajectory of the target is established based on the enhanced cooperative turning model;

[0052] S120, using a volumetric Kalman filter algorithm, according to the first predicted motion trajectory, the second predicted motion trajectory, and the third predicted motion trajectory, respectively calculate the volumetric particles and the volumetric Kalman filter value of each predicted motion trajectory;

[0053] Specifically, the volume particles and volume Kalman filter values ​​corresponding to the first predicted motion trajectory are calculated according to the volume Kalman filter algorithm, the volume particles and volume Kalman filter values ​​corresponding to the second predicted motion trajectory are calculated using the volume Kalman filter algorithm, and the volume particles and volume Kalman filter values ​​corresponding to the third predicted motion trajectory are calculated based on the volume Kalman filter algorithm. The above multiple volume particles and volume Kalman filter values ​​can be calculated in parallel to reduce the calculation time.

[0054] S130, using an interactive multi-model filtering algorithm, respectively calculating the mixed model probability of each predicted motion trajectory according to the volumetric particles, the Markov state transfer matrix and the enhanced collaborative turning model;

[0055] Specifically, the three groups of volumetric particles and volumetric Kalman filter values ​​calculated in step S120, the Markov state transfer matrix and the enhanced collaborative turning model are used to respectively calculate the mixed model probabilities of multiple predicted motion trajectories corresponding to the first predicted motion trajectory, the second predicted motion trajectory and the third predicted motion trajectory in step S120.

[0056] S140, performing weighted fusion according to each of the above hybrid model probabilities and the above volumetric Kalman filter values ​​to obtain the target trajectory.

[0057] Specifically, a value is obtained by weighted fusion of the multiple hybrid model probabilities calculated in step S130 and the multiple volumetric Kalman filter values ​​calculated in step S120, and this value is the target trajectory.

[0058]

[0059] in, is the filter output value of weighted fusion, that is, the target trajectory, represents the state value of the target S at the moment k of the predicted motion trajectory, Represents the probability of the mixed model corresponding to the predicted motion trajectory, i can be 1, 2, 3 corresponding to the first predicted motion trajectory, the second predicted motion trajectory and the third predicted motion trajectory respectively.

[0060] In summary, this method combines the advantages of the volumetric Kalman algorithm and the interactive multiple model algorithm, which can not only reduce the influence of noise on the determination of motion trajectory, but also accurately track targets with complex motion trajectory and strong maneuverability.

[0061] In some examples, the first predicted motion trajectory, the second predicted motion trajectory, and the third predicted motion trajectory of the target are respectively constructed based on the mobile platform according to the uniform speed model, the uniform acceleration model, and the enhanced cooperative turning model, including:

[0062] Constructing a state vector of the target, wherein the state vector includes the position, velocity and acceleration of the target;

[0063] Specifically, the state vector representation of the target can be expressed by formula (2):

[0064] X (s) (k) = [x (s) v x x (s) a x x (s) y (s) a y y (s) a z z (s) ] T (2)

[0065] Among them, X (s) (k) represents the state vector of the target, x (s) represents the x-position of the target, v x x (s) represents the speed in the x direction, a x x (s) represents the acceleration in the x direction, y (s) Indicates the y-direction position, a y y (s) represents the acceleration in the y direction, a z z (s) Represents the acceleration in the z direction. The x, y, and z directions are the right-handed coordinate system established based on the mobile platform.

[0066] Constructing the first predicted motion trajectory of the target based on the mobile platform according to the state vector, the noise data, the maneuvering acceleration of the target and the uniform velocity model, wherein the noise data includes at least one of Gaussian white noise, process noise of the mobile platform and process noise of the target motion;

[0067] Specifically, the first predicted motion trajectory It can be expressed by formula (3):

[0068]

[0069] Among them, v (s) It has zero mean and covariance Q (s) Gaussian white noise; v (q) is the maneuvering acceleration, which follows a normal distribution with a mean of zero, Γ (q ) is the measured platform noise.

[0070]

[0071] T is the sampling period for monitoring the target device on the mobile platform.

[0072] Constructing the second predicted motion trajectory of the target based on the mobile platform according to the state vector, the noise data, the maneuvering acceleration and the uniform acceleration model;

[0073] Specifically, the second predicted motion trajectory It can be expressed by formula (4):

[0074]

[0075] Among them, v (s) It has zero mean and covariance Q (s) Gaussian white noise; v (q) is the maneuvering acceleration, which follows a normal distribution with a mean of zero,

[0076]

[0077] T is the sampling period for monitoring the target device on the mobile platform.

[0078] The third predicted motion trajectory of the target based on the mobile platform is constructed according to the state vector, the noise data, the maneuvering acceleration, and the enhanced cooperative turning model.

[0079] Specifically, the third predicted motion trajectory It can be expressed by formula (5):

[0080]

[0081] Among them, v (s) It has zero mean and covariance Q (s) Gaussian white noise; v (q) is the maneuvering acceleration, which follows a normal distribution with a mean of zero,

[0082]

[0083] According to the angular velocity of the maneuvering target of model j at time k-1 Iteration

[0084] In some examples, the interactive multi-model filtering algorithm is used to calculate the mixed model probability of each predicted motion trajectory according to the volume particles, the Markov state transfer matrix and the enhanced cooperative turning model, including:

[0085] Calculate the state equation and measurement equation of each of the above-mentioned volume particles according to the above-mentioned enhanced cooperative turning model;

[0086] Specifically, according to the enhanced cooperative turning model and volumetric particle The volume particle is calculated using formula (6): According to the obtained volume particles The measurement equation is calculated by formula (7):

[0087]

[0088] Wherein, j can be 1, 2, 3 corresponding to the first predicted motion trajectory, the second predicted motion trajectory and the third predicted motion trajectory respectively,

[0089]

[0090] According to the angular velocity of the maneuvering target of model j at time k-1 Iteration

[0091] The mixed model probability is calculated based on the state equation, the measurement equation, the noise data and the Markov state transfer matrix.

[0092] Specifically, the initial value of the Markov state matrix can be taken as The above-mentioned mixed model probability is calculated by the above-mentioned state equation, the above-mentioned measurement equation, the above-mentioned noise data and the above-mentioned Markov state transfer matrix.

[0093] In some examples, calculating the hybrid model probability based on the state equation, the measurement equation, the noise data, and the Markov state transition matrix includes:

[0094] Calculate the particle observation average value based on the above measurement equation and the above volume particles;

[0095] Specifically, the particle observation average value can be calculated according to formula (8):

[0096]

[0097] Wherein, j can be 1, 2, 3, corresponding to the first predicted motion trajectory, the second predicted motion trajectory, and the third predicted motion trajectory, respectively, H j represents the number of volume particles, represents the measurement equation calculated by formula (7).

[0098] Calculate the innovation filter covariance using the particle observation average, the noise data and the measurement equation;

[0099] Specifically, the innovation filter covariance can be calculated by formula (9):

[0100]

[0101] Wherein, j can be 1, 2, 3, corresponding to the first predicted motion trajectory, the second predicted motion trajectory, and the third predicted motion trajectory, respectively, R (q) =Q (q) (Q (q) ) T , R (s) =Q (s) (Q (s) ) T , Q (q) , Q (s) The other parameters are shown in the above examples and will not be described again.

[0102] The above-mentioned mixed model probability is calculated based on the above-mentioned new information filter covariance and the above-mentioned Markov state transfer matrix.

[0103] In some examples, the calculation of the innovation filter covariance using the particle observation average, the noise data, and the measurement equation includes:

[0104] Calculate the predicted covariance based on the above particle observation averages;

[0105] Specifically, the prediction covariance is calculated using formula (10):

[0106]

[0107] In the formula, j can be 1, 2, or 3, corresponding to the first predicted motion trajectory, the second predicted motion trajectory, and the third predicted motion trajectory, respectively.

[0108] Calculating the measurement covariance using the particle observation average, the noise data and the measurement equation;

[0109] Specifically, the prediction covariance p is calculated using formula (11): k+1 / k :

[0110]

[0111] In the formula, j can be 1, 2, or 3, corresponding to the first predicted motion trajectory, the second predicted motion trajectory, and the third predicted motion trajectory, respectively.

[0112] The new information filtering covariance is obtained based on the above prediction covariance and the above measurement covariance.

[0113] Specifically, the innovation filter covariance is calculated using formula (12):

[0114]

[0115] In some examples, the calculation of the hybrid model probability based on the innovation filter covariance and the Markov state transition matrix includes:

[0116] Calculate the likelihood function based on the above-mentioned innovation filter covariance;

[0117] Specifically, the likelihood function can be calculated using formula (13):

[0118]

[0119] In the formula, j can be 1, 2, or 3, corresponding to the first predicted motion trajectory, the second predicted motion trajectory, and the third predicted motion trajectory, respectively.

[0120] The above-mentioned mixed model probability is calculated using the above-mentioned likelihood function and the above-mentioned Markov state transfer matrix.

[0121] Specifically, the probability of the mixed model can be calculated using formula (14):

[0122]

[0123] Wherein, j can be 1, 2, 3 corresponding to the first predicted motion trajectory, the second predicted motion trajectory and the third predicted motion trajectory respectively, represents the modified Markov transfer matrix, Represents the probability of the mixture model at the previous moment.

[0124] In some examples, the method further includes:

[0125] The Markov transfer matrix is ​​modified based on the mixed model probability.

[0126] Specifically, the Markov transfer matrix can be determined by formula (15):

[0127]

[0128] in, γ1(k-1), γ2(k-1), and γ3(k-1) respectively represent the mixed probability models corresponding to the first predicted motion trajectory, the second predicted motion trajectory, and the third predicted motion trajectory at time (k-1).

[0129] And the Markov transfer matrix in formula (15) also satisfies the following characteristics:

[0130]

[0131] in, Represents the probability of the state value at time k-1 and the transformation of motion model i into motion model j is the Markov transfer matrix The corresponding data of row i and column j is: represents the target state prediction value, represents the prediction covariance, where i=1 is the uniform speed model, i=2 is the uniform acceleration model, i=3 is the enhanced cooperative turning model, j=1 is the uniform speed model, j=2 is the uniform acceleration model, and j=3 is the enhanced cooperative turning model.

[0132] Please refer to Figure 2 , Figure 2 A schematic flow chart of another method for determining a target trajectory based on a mobile platform provided in this embodiment.

[0133] In some examples, the above-mentioned target trajectory determination method based on the mobile platform may include:

[0134] S210, initializing the target predicted motion trajectory;

[0135] Specifically, the motion trajectory X of the high-speed maneuvering target under the shipborne platform is constructed. (s) (k+1), where 0<s<S, s represents the sth target, 0<k<K, k represents the kth moment, and the process noise caused by the ship's bow, roll or pitch motion is Γ (q) , construct the target state vector through formula (2):

[0136] X (s) (k) = [x (s) v x x (s) a x x (s) y (s) a y y (s) a z z (s) ] T (2)

[0137] According to formula (3), the first predicted motion trajectory of the target based on the carrier platform is constructed by using the state vector, noise data, the maneuvering acceleration of the target and the uniform velocity model:

[0138]

[0139] According to formula (4), the second predicted motion trajectory of the target based on the carrier platform is constructed by using the state vector, noise data, the maneuvering acceleration of the target and the uniform acceleration model:

[0140]

[0141] According to formula (5), the second predicted motion trajectory of the target based on the carrier platform is constructed by using the state vector, noise data, the maneuvering acceleration of the target and the enhanced cooperative turning model:

[0142]

[0143] S220, interactive fusion of input data;

[0144] Set the initial value Pi of the Markov probability transfer matrix to a 3×3 constant matrix;

[0145]

[0146] S230, processing target prediction trajectory by volumetric Kalman filtering;

[0147] Based on the first predicted motion trajectory, the second predicted motion trajectory and the third predicted motion trajectory, a volume Kalman filter is performed to obtain volume particles corresponding to the three predicted trajectories.

[0148] S240, calculating the probability of the mixed model;

[0149] Volumetric particles corresponding to the enhanced cooperative turning model and three predicted trajectories The state equations of the three volume particles are calculated using formula (6): According to the state equation of the volume particle obtained The measurement equation is calculated by formula (7):

[0150]

[0151] The particle observation average value can be calculated according to formula (8):

[0152]

[0153] The prediction covariance is calculated using formula (10):

[0154]

[0155] The prediction covariance p is calculated using formula (11): k+1 / k :

[0156]

[0157] The innovation filter covariance is calculated using formula (12):

[0158]

[0159] The likelihood function is calculated using formula (13):

[0160]

[0161] The mixed model probability is calculated using formula (14):

[0162]

[0163] There are three hybrid model probabilities obtained, which correspond to the first predicted motion trajectory, the second predicted motion trajectory and the third predicted motion trajectory respectively.

[0164] S250, weighted fusion calculation output estimation;

[0165] The output value is obtained by weighted fusion calculation using formula (1)

[0166]

[0167] S260, correct the Markov state transfer matrix.

[0168] The Markov transfer matrix can be modified by formula (15):

[0169]

[0170] See also Figure 3 and Figure 4 , Figure 3 A schematic diagram of the root mean square error of a distance of a simulation result provided in an embodiment of the present application; Figure 4 A schematic diagram of the velocity root mean square error of a simulation result provided in an embodiment of the present application.

[0171] In some examples, simulation experiments were conducted, and the software platform for the simulation experiments was: MATLAB2015;

[0172] The simulation data used in the simulation experiment: the target moves in 6 time periods within 100s. The target moves in a variable speed curve clockwise from 0 to 20s, in a uniform linear motion from 21 to 30s, in a variable speed curve counterclockwise from 31 to 50s, in a variable speed curve clockwise from 50 to 70s, in a variable speed curve counterclockwise from 71 to 90s, and in a uniformly accelerated linear motion from 91 to 100s. In order to evaluate the tracking accuracy, the following evaluation index formula is used to calculate the distance and velocity root mean square error ε of the tracking results of the present invention and the prior art respectively. k (x,y), ε k (v x ,v y ), and the calculation results are plotted and displayed on Figure 3 and Figure 4 middle.

[0173]

[0174] in, are the x and y coordinates in the output of the present invention, x k (x, y) is the actual position coordinate of the simulation target.

[0175]

[0176] in, is the speed in the output of the present invention, V k (v x ,v y ) is the actual speed of the simulation target.

[0177] Figure 3 and Figure 4 The figure shows an improved adaptive filtering method based on volumetric Kalman and interactive multiple models (ACT-CKF-IFIMM), which is a target trajectory determination method based on a mobile platform provided in this embodiment. It also shows a standard interactive multiple model filtering algorithm (CS-IMM), an enhanced interactive multiple model algorithm based on an extended Kalman filter (EKF) (AGIMM-EKF), and an enhanced interactive multiple model algorithm based on an unscented Kalman filter (UKF) (AGIMM-UKF). Figure 3 and Figure 4 It can be seen that the RMS error of distance and RMS error of velocity obtained by simulation based on this method are smaller than those of other algorithms, which shows that this method is more accurate in determining the target trajectory.

[0178] See also Figure 5 , Figure 5 A schematic diagram of a target trajectory determination device based on a mobile platform provided in an embodiment of the present application

[0179] A first acquisition unit: can be used to acquire a first orientation matrix, wherein the first orientation matrix includes a distance, an elevation angle and an azimuth angle of the target measured based on a radar and an elevation angle and an azimuth angle of the target measured based on infrared;

[0180] A second acquisition unit; may be used to acquire a second orientation matrix, wherein the second matrix includes a target position of the target acquired by the radar;

[0181] Computing unit: can be used to determine the target trajectory based on the first matrix, the second matrix, the third matrix and the preset motion equation, wherein the third matrix is ​​a unit matrix of the same dimension as the second matrix.

[0182] See also Figure 6 , Figure 6 A schematic diagram of an embodiment of an electronic device provided in an embodiment of the present application.

[0183] like Figure 6 As shown, an embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any method for determining a target trajectory based on a mobile platform are implemented.

[0184] Since the electronic device introduced in this embodiment is a device used to implement a target trajectory determination device based on a mobile platform in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation method of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0185] In the specific implementation process, when the computer program 311 is executed by the processor, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0186] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0187] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0188] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0189] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0190] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0191] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 The process of determining the trajectory based on the target of the mobile platform in the corresponding embodiment.

[0192] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0193] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0194] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0195] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0196] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0197] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0198] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for determining target trajectory based on a mobile platform, characterized in that: include: According to the uniform speed model, the uniform acceleration model and the enhanced cooperative turning model, a first predicted motion trajectory, a second predicted motion trajectory and a third predicted motion trajectory of the target are respectively constructed based on the mobile platform; Using a volumetric Kalman filter algorithm, according to the first predicted motion trajectory, the second predicted motion trajectory and the third predicted motion trajectory, respectively calculate the volumetric particles and the volumetric Kalman filter value of each predicted motion trajectory; Using an interactive multi-model filtering algorithm, respectively calculating the mixed model probability of each predicted motion trajectory according to the volume particles, the Markov state transfer matrix and the enhanced cooperative turning model; Obtaining a target trajectory by performing weighted fusion according to each of the hybrid model probabilities and the volumetric Kalman filter value; The interactive multi-model filtering algorithm is used to calculate the mixed model probability of each predicted motion trajectory according to the volume particles, the Markov state transfer matrix and the enhanced cooperative turning model, including: Calculate the state equation and measurement equation of each volume particle according to the enhanced cooperative turning model; Calculating the hybrid model probability based on the state equation, the measurement equation, noise data and the Markov state transition matrix; The calculating the hybrid model probability based on the state equation, the measurement equation, the noise data and the Markov state transfer matrix comprises: Calculate the particle observation average value according to the measurement equation and the volume particles; Calculating the innovation filter covariance using the particle observation average, the noise data and the measurement equation; Calculate the hybrid model probability based on the innovation filter covariance and the Markov state transfer matrix; The calculating of the innovation filter covariance using the particle observation average, the noise data and the measurement equation comprises: Calculating a predicted covariance based on the particle observation average; Calculating a measurement covariance using the particle observation average, the noise data, and the measurement equation; Acquire an innovation filter covariance based on the prediction covariance and the measurement covariance; The calculating the hybrid model probability based on the innovation filter covariance and the Markov state transfer matrix includes: Calculating a likelihood function based on the innovation filtering covariance; The mixed model probability is calculated using the likelihood function and the Markov state transfer matrix.

2. The method according to claim 1, characterized in that The method of constructing a first predicted motion trajectory, a second predicted motion trajectory, and a third predicted motion trajectory of the target based on the mobile platform according to the uniform speed model, the uniform acceleration model, and the enhanced cooperative turning model includes: Constructing a state vector of a target, wherein the state vector includes a position, a velocity, and an acceleration of the target; Constructing the first predicted motion trajectory of the target based on the mobile platform according to the state vector, the noise data, the maneuvering acceleration of the target, and the uniform velocity model, wherein the noise data includes at least one of Gaussian white noise, process noise of the mobile platform, and process noise of the target motion; Constructing the second predicted motion trajectory of the target based on the mobile platform according to the state vector, the noise data, the maneuvering acceleration and the uniform acceleration model; The third predicted motion trajectory of the target based on the mobile platform is constructed according to the state vector, the noise data, the maneuvering acceleration, and the enhanced cooperative turning model.

3. The method according to claim 1, characterized in that The method further comprises: The Markov transfer matrix is ​​modified based on the mixture model probability.

4. A target trajectory determination device based on a mobile platform, characterized in that: include: An initialization unit is used to construct a first predicted motion trajectory, a second predicted motion trajectory and a third predicted motion trajectory of the target based on the mobile platform according to a uniform speed model, a uniform acceleration model and an enhanced cooperative turning model; A filtering unit: used for calculating the volumetric particles and the volumetric Kalman filter value of each predicted motion trajectory respectively according to the first predicted motion trajectory, the second predicted motion trajectory and the third predicted motion trajectory by using a volumetric Kalman filter algorithm; Interaction unit: using an interactive multi-model filtering algorithm, respectively calculating the mixed model probability of each predicted motion trajectory according to the volume particles, the Markov state transfer matrix and the enhanced cooperative turning model; A target acquisition unit: used for acquiring a target trajectory by performing weighted fusion according to each of the hybrid model probabilities and the volumetric Kalman filter value; The interactive multi-model filtering algorithm is used to calculate the mixed model probability of each predicted motion trajectory according to the volume particles, the Markov state transfer matrix and the enhanced cooperative turning model, including: Calculate the state equation and measurement equation of each volume particle according to the enhanced cooperative turning model; Calculating the hybrid model probability based on the state equation, the measurement equation, noise data and the Markov state transition matrix; The calculating the hybrid model probability based on the state equation, the measurement equation, the noise data and the Markov state transfer matrix comprises: Calculate the particle observation average value according to the measurement equation and the volume particles; Calculating the innovation filter covariance using the particle observation average, the noise data and the measurement equation; Calculate the hybrid model probability based on the innovation filter covariance and the Markov state transfer matrix; The calculating of the innovation filter covariance using the particle observation average, the noise data and the measurement equation comprises: Calculating a predicted covariance based on the particle observation average; Calculating a measurement covariance using the particle observation average, the noise data, and the measurement equation; Acquire an innovation filter covariance based on the prediction covariance and the measurement covariance; The calculating the hybrid model probability based on the innovation filter covariance and the Markov state transfer matrix includes: Calculating a likelihood function based on the innovation filtering covariance; The mixed model probability is calculated using the likelihood function and the Markov state transfer matrix.

5. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of a target trajectory determination method based on a mobile platform as claimed in any one of claims 1 to 3 when executing the computer program stored in the memory.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a method for determining a target trajectory based on a mobile platform as described in any one of claims 1 to 3 is implemented.

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