WO2021008077A1 - Procédé et système de poursuite multi-cibles sous bruit de scintillation - Google Patents

Procédé et système de poursuite multi-cibles sous bruit de scintillation Download PDF

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WO2021008077A1
WO2021008077A1 PCT/CN2019/125850 CN2019125850W WO2021008077A1 WO 2021008077 A1 WO2021008077 A1 WO 2021008077A1 CN 2019125850 W CN2019125850 W CN 2019125850W WO 2021008077 A1 WO2021008077 A1 WO 2021008077A1
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target
distribution function
label
filter density
current moment
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PCT/CN2019/125850
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Chinese (zh)
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刘宗香
黄炳坚
武宏杰
李良群
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深圳大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence

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  • This application relates to the technical field of target tracking, and in particular to a method and system for multi-target tracking under flicker noise.
  • Tag Do Bernoulli filter can accurately estimate the number of targets and track target trajectories in clutter and noise environments, so it has been applied to many practical problems, such as radar target tracking, image data tracking, ground target tracking, sensor management, audio And other applications such as video data tracking, visual data tracking and cell tracking, and moving multi-target tracking.
  • the label multi-Bernoulli filter is mostly used when the noise is Gaussian noise, and the multi-target tracking effect in the flicker noise environment is not good. Therefore, how to effectively track multiple targets under flicker noise is a key technical problem that needs to be explored and resolved.
  • the main purpose of this application is to propose a multi-target tracking method and system under flicker noise, so as to solve the problem that the existing filters for multi-target tracking cannot be applied in the flicker noise environment.
  • the first aspect of the embodiments of the present application provides a multi-target tracking method under flicker noise, including:
  • the predicted distribution function of the target at the current moment and the predicted label Dobernulli filter density are obtained through prediction;
  • each target at the current moment includes a target that already exists at the current moment and a new-born target at the current moment;
  • the second aspect of the embodiments of the present application provides a multi-target tracking system under flicker noise, including:
  • the prediction module is used to use the distribution function of each target at the previous moment and the label Dobernuli filter density to obtain the predicted distribution function of the existing target at the current moment and the predicted label Doberman filter density;
  • Freshman target acquisition module used to set a preset distribution function and a preset label Dobernuli filter density for the Georgia target;
  • the merging module is used to merge the preset distribution function and the preset label Dobernulli filter density of the new target with the predicted distribution function and the predicted label Dobernuli filter density of the existing target at the current moment to obtain The predicted distribution function of each target at the current moment and the predicted label Do Bernoulli filter density;
  • each target at the current moment includes a target that already exists at the current moment and a new-born target at the current moment;
  • the update module is used to convert the predicted label Do Bernoulli filter density of each target at the current moment into the predicted delta-generalized label Do Bernoulli filter density, and use the variational Bayes method to measure the current moment, each The predicted distribution function of the target and the predicted ⁇ -generalized label multi-Bernoulli filter density are processed to obtain the updated distribution function of each target and the updated ⁇ -generalized label multi-Bernoulli filter density;
  • the cropping module is used to predict the distribution function of each target, predict the ⁇ -generalized label Do Bernoulli filter density and update the distribution function of each target through Gibbs sampling, update the ⁇ -generalized label Do Bernoulli filter density
  • the filtering density is jointly cropped; the remaining predicted distribution function and updated distribution function after cropping form the candidate distribution function at the current moment, and the remaining predicted ⁇ -generalized label Dobernulli filter density after cropping and updated ⁇ -generalized label Dober
  • the Nouli filter density is converted to the label Dobernuoli filter density to form the candidate label Dobernuoli filter density at the current moment;
  • the extraction module is used to perform pruning and fusion processing on the candidate distribution function at the current moment and the candidate label Dobernuelli filter density to obtain the distribution function of each target at the current moment and the label Dobernuelli filter density as the next moment
  • the input of the filter estimate the number of targets at the current time according to the label Do Bernoulli filter density of each target at the current time, and calculate the existence probability of each target at the current time; and according to the estimated number of targets, the target distribution function with high probability is sequentially Extracted, and the extracted target distribution function is used as the output of the filter at the current moment.
  • the embodiment of the present application proposes a method for tracking multiple targets under flicker noise.
  • the targets are divided into targets at the previous moment, existing targets at the current moment, and new targets according to time.
  • the distribution functions and labels of the targets at the previous moment are many.
  • the Bernoulli filter density is known, and it is used to predict the distribution function of the existing target at the current moment and the label Dobernulli filter density.
  • the remaining predicted distribution function and updated distribution function form the candidate distribution function at the current moment.
  • the cropped remaining predicted ⁇ -generalized label multi-Bernoulli filter density and updated ⁇ -generalized label multi-Bernoulli filter The density is converted to the label multi-Bernoulli filter density to form the candidate label multi-Bernoulli filter density at the current moment, and before the candidate distribution function and the candidate label multi-Bernoulli filter density are input to the filter, they are also pruned Fusion processing, so as to extract the effective components again on the basis of the candidate distribution function and the candidate label Dobery filter density, which constitutes the distribution function of the current time target and the label Doberman filter density, the above distribution function and label Doberman
  • the Nuuli filter density as the input of the filter at the next moment, estimates the number of targets at the current moment based on the label Dobernuoli filter density of each target at the current moment, calculates the existence probability of each target at the current moment, and according to the estimated target number,
  • the target with high probability is the tracking target of the filter, and the output of the filter is used to describe the target.
  • the multi-target tracking method under flicker noise provided by the embodiment of the present application can accurately extract the target state of the total target in the flicker noise environment, thereby improving the accuracy of multi-target tracking in the flicker noise environment .
  • FIG. 1 is a schematic diagram of the implementation process of the multi-target tracking method under flicker noise according to Embodiment 1 of the application;
  • FIG. 2 is a schematic diagram of the composition structure of a multi-target tracking system under flicker noise provided in the second embodiment of the application;
  • FIG. 5 is a filter output result obtained by processing the existing VB-PHD filtering method under flicker noise according to the third embodiment of the present application;
  • FIG. 6 is a schematic diagram of the average OSPA distance obtained after 100 experiments according to the multi-target tracking method under flicker noise in the first embodiment and the VB-PHD filtering method;
  • FIG. 7 is a schematic diagram of the estimation of the number of targets obtained after 100 experiments according to the multi-target tracking method under flicker noise in the first embodiment and the VB-PHD filtering method.
  • an embodiment of the present application proposes a multi-target tracking method under flicker noise, including steps S101 to S106.
  • the target at the previous moment and the target at the current moment refer to multiple tracking targets at different moments, and the distribution of the targets in the area is represented by the distribution function and the label Dobernulli filter density.
  • steps S101 to S103 are prediction steps
  • step S104 is an update step
  • step S105 is a cropping step
  • step S106 is an output step.
  • step S101 may be:
  • z k represents the measured value at time k, Indicates the mean value of the measurement, ⁇ k is the accuracy matrix, and ⁇ k is the degree of freedom of t distribution;
  • N represents the Gaussian distribution
  • IG represents the inverse gamma distribution
  • x k-1 represents the state component at the previous moment
  • m k-1 represents the state estimate mean
  • P k-1 represents the covariance matrix
  • R k-1 represents the noise Variance matrix
  • d represents the dimensions of the inverse gamma distribution parameters ⁇ k-1 and ⁇ k-1 ;
  • F k-1 is the state transition matrix
  • Q k-1 is the process noise variance Matrix
  • ⁇ ⁇ and ⁇ ⁇ are the propagation factors
  • x') is the single target transition density.
  • step S102 may be:
  • the preset distribution function of the newborn target is:
  • x k is the state component at time k
  • m k B is the estimated mean value of the state of the newborn target
  • P k B is the covariance matrix of the newborn target
  • ⁇ k, B and ⁇ k, B are the inverse G of the newborn target
  • step S103 may be:
  • a heuristic method is used to generate the parameters of the inverse gamma distribution, so as to predict the distribution function of the target at the current moment and the label multiple Bernoulli filter density according to the distribution function of the target at the previous moment and the label multi-Bernoulli filter density.
  • Nouri filter density and set the preset distribution function and preset label Do Bernoulli filter density for the newborn target; then set the preset distribution function and preset label Do Bernoulli filter density of the newborn target with the current time.
  • the predicted distribution function of the target and the predicted label Dobernulli filter density are combined to obtain the predicted distribution function of each target at the current moment and the predicted label Dobernullian filter density.
  • step S104 the predicted label Dobernullian filter density of each target at the current moment is converted into a delta-generalized label Dobernullian filter density, and the variational Bayes method is used to process the predicted distribution function of each target at the current moment. And predict the ⁇ -generalized label multi-Bernoulli filter density, obtain the updated distribution function of each target and update the ⁇ -generalized label multi-Bernoulli filter density, and provide a data basis for the joint cropping in the next step.
  • step S104 may be:
  • m k m k
  • P k P k
  • v k z k -H k m k
  • H k is the observation matrix
  • ⁇ k ⁇ k represents the 1-1 mapping from the label to the observation set:
  • Is the probability of detection Is the probability of missed detection
  • k(z) is the noise confounding degree that obeys the Poisson distribution.
  • step S105 the predicted distribution function of each target, the predicted ⁇ -generalized label Do Bernoulli filter density and the updated distribution function of each target, update the ⁇ -generalized label Do Bernoulli filter density of each target through Gibbs sampling
  • the filtering density is jointly cropped; the remaining predicted distribution function and updated distribution function after cropping form the candidate distribution function at the current moment, and the remaining predicted ⁇ -generalized label Dobernulli filter density after cropping and updated ⁇ -generalized label Dober
  • the Nulli filter density is converted into the label Dobernulli filter density to form the candidate label Dobernulli filter density at the current moment.
  • step S105 may be:
  • M is the number of observations at the current moment, and P is the target number at the current moment;
  • the predicted distribution function of each target, the predicted delta-generalized label Dobernulli filter density, the updated distribution function of each target, and the updated delta-generalized label Dobernuli filter density are jointly cropped, and the weight value is deleted.
  • the distribution function corresponding to the small target and the ⁇ -generalized label multi-Bernoulli filter density Take the remaining predicted distribution function and updated distribution function after cropping as the candidate distribution function at the current moment, and convert the cropped remaining predicted ⁇ -generalized label Dobernuoli filter density and updated ⁇ -generalized label Dobernuoli filter density Do Bernoulli filter density for the label, namely
  • step S106 before inputting the candidate distribution function and the candidate label multi-Bernoulli filter density to the filter, the pruning and fusion processing is also performed on them, so that the candidate distribution function and the candidate label multi-Bernoulli filter density are again based on The effective components are extracted to form the distribution function of the target at the current moment and the label Do Bernoulli filter density.
  • the above distribution function and label Do Bernoulli filter density are used as the input of the filter at the next moment.
  • the label multi-Bernoulli filter density estimates the number of targets at the current moment, calculates the existence probability of each target at the current moment, and according to the estimated number of targets, sequentially extracts the target distribution functions with high probability of existence, and the extracted target distribution function is taken as The output of the filter at the current moment.
  • step S106 may be:
  • the extracted target distribution function is used as the output of the filter.
  • the mean value of the distribution function output at the kth moment is the state estimate of each target at the current moment
  • the covariance of the distribution function output at the kth moment is the error estimate of each target at the current moment.
  • the second embodiment of the present application also provides a multi-target tracking system 20 under flicker noise, including but not limited to the following components:
  • the prediction module 21 is used to use the distribution function of each target at the previous moment and the label Dobernuli filter density to obtain the predicted distribution function of the existing target at the current moment and the predicted label Dobermanu filter density;
  • the Georgia target acquisition module 22 is used to set a preset distribution function and a preset label Dobernuli filter density for the Georgia target;
  • the merging module 23 is used for merging the preset distribution function and the preset label Do Bernoulli filter density of the new target with the predicted distribution function and the predicted label Do Bernoulli filter density of the existing target at the current moment, respectively, to obtain The predicted distribution function of the target and the multi-Bernoulli filter density of the predicted label;
  • the goals at the current moment include the existing goals at the current moment and the new-born goals at the current moment;
  • the update module 24 is used to convert the predicted label Dobernulli filter density of each target at the current moment into a delta-generalized label Dobernuli filter density, and use the variational Bayes method to measure the current moment and predict each target Processing the distribution function and predicting the ⁇ -generalized label Dobernuelli filter density to obtain the updated distribution function of each target and the updated ⁇ -generalized label Dobernuelli filter density;
  • the cropping module 25 is used to predict the distribution function of each target through Gibbs sampling, predict the ⁇ -generalized label Do Bernoulli filter density and update the distribution function of each target, update the ⁇ -generalized label Do Bernoulli filter
  • the density is jointly cropped; the remaining predicted distribution function and the updated distribution function after cropping form the candidate distribution function at the current moment, and the remaining predicted ⁇ -generalized label Do Bernoulli filter density after cropping and the updated ⁇ -generalized label Dobernu Convert the benefit filter density to the label Dobernulli filter density to form the candidate label Dobernuli filter density at the current moment;
  • the extraction module 26 is used to perform pruning and fusion processing on the joint distribution function at the current moment and the joint label Dobernuelli filter density to obtain the distribution function of each target at the current moment and the label Dobernuelli filter density as the next time filter
  • the input of the device estimate the number of targets at the current time according to the label Dobernulli filter density of each target at the current time, calculate the existence probability of each target at the current time; and according to the estimated number of targets, sequentially extract the target distribution function with high probability of existence Then, the extracted target distribution function is used as the output of the filter at the current moment.
  • the embodiment of the present application also uses practical applications to illustrate the target tracking effect of the multi-target tracking method under flicker noise and the multi-target tracking system under flicker noise in the first and second embodiments above.
  • 6 targets moving in the two-dimensional observation space [-1000(m), 1000(m)] ⁇ [-1000(m), 1000(m)] are selected as tracking targets.
  • the target motion observation time is 50s.
  • the target state is composed of position and speed, expressed as Where ⁇ i,x and ⁇ i,y represent position components with Represents the velocity component;
  • the state transition matrix of the target is
  • ⁇ v is the process noise standard deviation
  • the observation matrix is a
  • FIG. 3 is the simulation observation data collected by the sensor.
  • FIG. 4 shows the filter output results obtained by processing the existing VB-PHD filtering method under flicker noise
  • Figure 5 shows the processing obtained by the multi-target tracking method under flicker noise proposed in the embodiment of the present application.
  • the output result of the filter the circle represents the tracked target, showing the target tracking effect based on Figure 3.
  • the abscissa represents the horizontal distance between the target and the origin, in m
  • the ordinate represents the longitudinal distance between the target and the origin, in m.
  • the embodiment of the application also processes the simulation data of FIG. 3 separately according to the multi-target tracking method under flicker noise provided in Embodiment 1, and the existing VB-PHD filtering method under flicker noise, and performs 100 Monte Carlo experiments. Two statistical results were obtained. Among them, Fig. 6 is the statistical result of the average OSPA (Optimal Subpattern Assignment) distance of the two, and Fig. 7 is the statistical result of the target quantity estimation of the two.
  • OSPA Optimal Subpattern Assignment
  • the curve with "+” represents the filtering effect of using the VB-PHD filter
  • the curve with "*” represents the filtering effect of the multi-target tracking method under flicker noise provided in the first embodiment.
  • " indicates the number of real targets.
  • the ordinate represents the average OSPA distance, the unit is m, the abscissa represents the time, the unit is s; in Figure 7, the ordinate represents the target number estimation, the unit is 1, the abscissa represents the time, the unit is s.
  • the multi-target tracking method under flicker noise of this application can be more accurate Estimate the number of targets, the OSPA distance is smaller than the OSPA distance obtained by the existing method. Therefore, the multi-target tracking method under flicker noise and the multi-target tracking system under flicker noise in the first and second embodiments of the present application can be The filter can accurately estimate the number of targets and extract the target distribution function in the flicker noise environment, thereby improving the accuracy of multi-target tracking in the flicker noise environment.

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Abstract

L'invention concerne un procédé et un système de poursuite multi-cibles sous bruit de scintillation, applicables au domaine technique de la poursuite de cibles. Le procédé consiste à : à l'aide d'une fonction de distribution et d'une densité de filtre multi-Bernoulli labélisé des cibles au moment précédent, obtenir, par prédiction, une fonction de distribution prédite et une densité de filtre multi-Bernoulli labélisé prédite d'une cible existante au moment actuel (S101); définir une fonction de distribution prédéfinie et une densité de filtre multi-Bernoulli labélisé prédéfinie pour une nouvelle cible générée (S102); fusionner respectivement la fonction de distribution prédéfinie et la densité de filtre multi-Bernoulli labélisé prédéfinie de la nouvelle cible générée avec la fonction de distribution prédite et la densité de filtre multi-Bernoulli labélisé prédite de la cible existante au moment actuel, pour obtenir une fonction de distribution prédite et une densité de filtre multi-Bernoulli labélisé prédite des cibles au moment actuel (S103); convertir la densité de filtre multi-Bernoulli labélisé prédite des cibles au moment actuel en une densité de filtre multi-Bernoulli labélisé à généralisation δ prédite, et traiter la mesure au moment actuel, et la fonction de distribution prédite et la densité de filtre multi-Bernoulli labélisé à généralisation δ prédite des cibles par une méthode bayésienne variationnelle pour obtenir une fonction de distribution mise à jour et une densité de filtre multi-Bernoulli labélisé à généralisation δ mise à jour des cibles (S104); effectuer un recadrage conjoint de la fonction de distribution prédite et de la densité de filtre multi-Bernoulli labélisé à généralisation δ prédite des cibles, ainsi que de la fonction de distribution mise à jour et de la densité de filtre multi-Bernoulli labélisé à généralisation δ mise à jour des cibles par échantillonnage de Gibbs, former une fonction de distribution candidate au moment actuel à partir de la fonction de distribution prédite et de la fonction de distribution mise à jour restantes après le recadrage, et convertir la densité de filtre multi-Bernoulli labélisé à généralisation δ prédite et la densité de filtre multi-Bernoulli labélisé à généralisation δ mise à jour restantes en une densité de filtre multi-Bernoulli labélisé pour former une densité de filtre multi-Bernoulli labélisé candidate au moment actuel (S105); et effectuer une traitement d'élagage et de fusion de la fonction de distribution candidate et de la densité de filtre multi-Bernoulli labélisé candidate au moment actuel pour obtenir la fonction de distribution et la densité de filtre multi-Bernoulli labélisé des cibles au moment actuel en tant qu'entrée d'un filtre au moment suivant; estimer le nombre de cibles au moment actuel en fonction de la densité de filtre multi-Bernoulli labélisé des cibles au moment actuel, et calculer la probabilité d'existence des cibles au moment actuel; et extraire successivement des fonctions de distribution de cible présentant une forte probabilité d'existence en fonction du nombre estimé de cibles, et utiliser la fonction de distribution de cibles extraite en tant que sortie du filtre au moment actuel (S106). Selon le procédé, le filtre peut extraire avec précision l'état cible des cibles au moment actuel dans l'environnement d'un bruit de scintillation, ce qui permet d'améliorer la précision de la poursuite multi-cibles.
PCT/CN2019/125850 2019-07-16 2019-12-17 Procédé et système de poursuite multi-cibles sous bruit de scintillation WO2021008077A1 (fr)

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CN114325686A (zh) * 2021-12-23 2022-04-12 中国人民解放军国防科技大学 基于smc-phd滤波器的多目标跟踪方法
CN114325686B (zh) * 2021-12-23 2024-05-03 中国人民解放军国防科技大学 基于smc-phd滤波器的多目标跟踪方法
CN115220032A (zh) * 2022-06-09 2022-10-21 河海大学 基于多特征信息gm-phd滤波器的雷达多目标跟踪方法
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CN115937253B (zh) * 2022-11-16 2024-06-07 苏州经贸职业技术学院 厚尾量测噪声下的鲁棒泊松多伯努利滤波方法及相关设备
CN117634614A (zh) * 2023-12-08 2024-03-01 兰州理工大学 一种基于鲁棒MS-MeMBer滤波的群目标跟踪方法

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