36 results about "Bernoulli filter" patented technology

The invention discloses a multi-Bernoulli video multi-target detection tracking method based on YOLOv3, and belongs to the field of machine vision and intelligent information processing. According tothe method, a YOLOv3 detection technology is introduced under a multi-Bernoulli filtering framework, an anti-interference convolution feature is adopted to describe a target, a detection result and atracking result are interactively fused, and accurate estimation on the multi-target state of the video with unknown number and time varying is realized; in the tracking process, a matched detection frame is combined with a target track and a target template, target newborn judgment and occlusion target re-identification are carried out in real time, identity label information of a detection target and an estimation target is considered at the same time, target identity identification and track tracking are achieved, the tracking precision of an occluded target can be effectively improved, andtrack fragments are reduced. Experiments show that the method has a good tracking effect and robustness, and can widely meet the actual design requirements of systems such as intelligent video monitoring, man-machine interaction and intelligent traffic control.

The invention relates to a multi-target passive tracking method based on a wireless sensor network. The technical characteristics are that the multi-target passive tracking method based on the wireless sensor network comprises the following steps that: (1) a measurement model is established according to the receiving signal intensity of the sensor network; (2) according to the established measurement model from the step (1), the variable multi-target positioning and tracking is realized under the indoor environment through the combination of a multi-target Bernoulli filter algorithm and a particle filter algorithm. The multi-target passive tracking method based on the wireless sensor network provided by the invention is reasonable in design; the established measurement model has high accuracy under the indoor environment, and the model forecast value is approximate to the actual observation value; the target detecting and tracking algorithm has high accuracy and stability, and can detect and track a plurality of targets; and the measurement model and the target algorithm are appropriate in calculation complexity, and thus guaranteeing the running real-time performance of the tracking system.

The invention discloses an expansion target tracking method based on GLMB (Generalized labelled multi-bernoulli) filtering and Gibbs sampling. The expansion target tracking method based on GLMB filtering and Gibbs sampling estimates the target number and the shape of the expansion target, provides a multiple expansion target tracking method under a labelled random finite sets (L-RFS) framework, and mainly includes two aspects: dynamic modeling of multiple expansion targets and tracking estimation of multiple expansion targets. The expansion target tracking method based on GLMB filtering and Gibbs sampling includes the steps: combined with a generalized label multi-bernoulli filter, establishing a measurement limit hybrid model of the expansion targets, by means of Gibbs sampling and Bayesian information criterion, deriving the parameters of the limit hybrid model to learn tracking of the state of the multiple expansion targets, using an equivalent measurement method to replace measurement generated from the expansion targets, and performing ellipse approximating modeling on the shape of the expansion targets to realize estimation of the shape of the expansion targets. The simulation experiment shows that the expansion target tracking method based on GLMB filtering and Gibbs sampling can effectively track the multiple expansion targets, can accurately estimate the state and theshape of the expansion targets, and can obtain the track of the targets.

The invention belongs to the technical field of intelligent information processing, and relates to a multi-target tracking method based on a variational Bayesian label multi-Bernoulli superposition model. The noise covariance of the superposition model is estimated. On the basis of an original superposition model, the covariance of measurement noise is unknown, unknown parameters are estimated based on variational Bayes, the prediction and updating process of the superposition model marked with the multi-Bernoulli filter is achieved, state extraction is conducted, and therefore the tracking problem of the superposition model under unknown measurement noise is solved. The method has the characteristics of wide application range, strong robustness, high estimation precision and the like, caneffectively solve the problem of non-cooperation in an actual superposition model scene, realizes multi-target tracking and parameter estimation in a complex scene, can meet design requirements, andhas a good engineering application value.

The invention discloses a random set theory-based multi-sensor adaptive control method. With the random set theory-based multi-sensor adaptive control method, general labeling multi-target Bernoulli filter-based multi-sensor adaptive control can be realized. According to the method, in a filtering stage, each sensor carries out filtering locally and carries out distributed fusion, and therefore, optimal global performance can be realized. In a control stage, firstly, fused multi-target distribution is sampled; secondly, pseudo prediction is carried out, pseudo prediction distribution is obtained, pseudo updated distribution is obtained through a plurality of steps of filtering iteration, and distributed fusion is carried out on the pseudo updated distribution, so that fused pseudo updated distribution can be obtained; and finally, Cauchy-Schwarz divergence between the pseudo updated distribution and the fused pseudo updated distribution is calculated, and therefore, an optimal sensor control decision can be selected out. With the method adopted, problems in multi-sensor optimal control in practical multi-sensor network application can be solved, and thus, multi-sensor adaptive control can be realized.

The invention discloses a maneuvering weak target Track-before-Detect method of interactive multi-mode Bernoulli filtering. The maneuvering weak target Track-before-Detect method of interactive multi-mode Bernoulli filtering is characterized in that on the basis of Bernoulli particle filtering, an interactive multi-mode method is used to predict the sampling particle of each target state, and thena prediction particle is substituted into a traditional Bernoulli TBD (Track-before-Detect) algorithm to perform update estimation of the existing probability and distribution density of the targets.In the low signal to noise ratio situation, the maneuvering weak target Track-before-Detect method of interactive multi-mode Bernoulli filtering can effectively detect and track the weak maneuveringtarget, and can be more accurate in estimation of the target state.

The invention discloses a random set theory-based multi-target tracking method. The method includes the following steps that: a multi-target state space is expanded, model dimension is increased based on original kinetic information, and therefore, characterization of target model information can be realized; a state transfer function and a likelihood function are expanded based on a jump Markov system, so that the state transfer function and the likelihood function can contain the model information; and the prediction and update process of an expanded multi-model generalized label multi-target Bernoulli filter is realized, and target states are extracted, and a target motion model is estimated. With the method adopted, problems in maneuvering multi-target tracking can be solved. The method has the advantages of high robustness, high adaptability and high estimation accuracy. With the method adopted, the problem of high multi-target maneuverability and inconsistency in practical application can be solved, and maneuvering multi-target tracking and target motion model estimation under complex scenes can be realized.

The invention is suitable for the technical field of target tracking, and provides a multi-target tracking method and system under flicker noises, and the method comprises the steps: predicting a prediction distribution function and a prediction label multi-Bernoulli filtering density of an existing target at a current moment through the distribution function and label multi-Bernoulli filtering density of each target at a previous moment; setting a preset distribution function and a preset label multi-Bernoulli filtering density for the new target; combining the two distribution functions andthe label multi-Bernoulli filtering density to obtain a predicted distribution function and a predicted label multi-Bernoulli filtering density of each target at the current moment; and processing thepredicted distribution function and the predicted label multi-Bernoulli filtering density of each target at the current moment to obtain the distribution function and the label multi-Bernoulli filtering density of each target at the current moment, and taking the distribution function and the label multi-Bernoulli filtering density as the input of a filter at the next moment. According to the invention, the filter can accurately extract the target state of each target at the current moment in the flicker noise environment, and the multi-target tracking precision is improved.

The invention discloses a distributed fusion method of a multi-Bernoulli filter, which comprises the steps of carrying out local filtering on sensor nodes in a sensor network by adopting the multi-Bernoulli filter, receiving multi-Bernoulli posterior distribution of adjacent nodes through a communication link, then realizing joint grouping of target tracks among sensors based on a generalized covariance intersection information entropy, and finally calculating parameters of distributed fusion of the multi-Bernoulli filter under each target track group so as to obtain fused posterior multi-Bernoulli distribution parameters. The distributed fusion method can efficiently realize distributed fusion of the multi-Bernoulli filter under the condition of a large quantity of targets, and has the advantages of small approximation error, low implementation complexity and the like.

The invention relates to a multi-target tracking method for solving distributed label fusion, wherein the method solves the technical problem of poor information fusion due to non-uniform labels, andcomprises the steps: step 1, independently operating a label multi-Bernoulli filter on each local sensor to obtain locally estimated LMB posteriori information; and setting a threshold value for the local information, and performing LMB posteriori pruning and truncation operation to reduce the calculation complexity; step 2, performing label matching for inconsistency of posteriori labels of the sensors, so as to enable the labels to be consistent; step 3, sharing the information of each sensor and the adjacent sensor, and performing arithmetic average fusion on the shared information according to a label mode; and step 4, performing target state and target track extraction according to the fusion result. The problem is well solved, and the method can be used for distributed multi-sensor multi-target detection and tracking.

The present invention relates to a uniformly dense clutter sparse method aiming at a finite set tracking filter. In order to overcome the problem that in the traditional algorithms, computing time has exponential growth along with the growth of clutter density, the invention provides the method. The method is operated with the hypothesis testing theory as the norm, with the help of a mixed Gaussian-potential probability density filter and a mixed Gaussian-multi-Bernoulli filter. The hypothesis testing theory is used for verifying a clutter sparse process to overcome the problem that in the traditional algorithms, computing time has exponential growth along with the growth of clutter density. Then, computing efficiency is greatly improved.

The invention discloses a multi-sensor adaptive angle control method based on GLMB filter, which comprises the following steps: S1, using a generalized label multi-Bernoulli filter to perform local filtering; S2, performing distributed fusion based on a generalized covariance cross criterion; S3, sampling multi-target; S4, using the generalized label multi-Bernoulli filter to perform a pseudo prediction; S5, calculating an ideal measurement of each sensor; S6, grouping the sensors into two groups, generating a decision set, and performing distributed fusion processing on the sensor pairs in the decision set; S7, calculating the Cauchy Schwarz divergence under the control decision combination of the pairwise coupling; S8, selecting a locally optimal sensor control decision for each sensor pair; S9, combine all local optimal sensor control decisions into one set. The multi- sensor adaptive angle control method based on GLMB filter can ensure small error loss, and solve the multi-sensor adaptive angle control based on GLMB filter which can be realized with precision and calculation cost.

The invention provides a three-dimensional multi-target tracking algorithm based on Poisson-multi-Bernoulli (PMBM) filtering. The main content of the algorithm comprises target formulation, target detection and target tracking. According to the process of the algorithm, first, target images are represented with parameter feature vectors; second, a deep learning network is adopted to process the target images so as to output detection results (the deep learning network is composed of a feature extractor and three pieces of parallel output header information); and last, a detection set sequenceis processed through a PMBM filter (comprising two random finite sets) in a tracking module, and an estimated sequence of a real target group is extracted and output after processing is completed. Compared with existing target tracking algorithms, the algorithm has the advantages that multiple targets can be tracked simultaneously, and tracking results are more precise; and in terms of data processing, the processing efficiency of the algorithm is higher, and associated data and even overlapping data can be processed.

The invention discloses a multi-target video tracking system based on fractal feature estimation. The multi-target video tracking system comprises an image local fractal feature estimation module, anobservation model establishment and likelihood function calculation module and a multi-Bernoulli filter tracking module. The local fractal feature estimation module calculates local fractal features of each frame of image and forms a new Hurst index image. The observation model establishing and likelihood function calculating module is used for establishing an observation model and calculating a likelihood function by utilizing local fractal characteristics and a Hurst index. The multi-Bernoulli filter tracking module is used for tracking a plurality of targets in the Hurst index image. According to the method, under the condition that the sensor is static or moves, a plurality of targets can be correctly tracked, and good robustness is achieved for complex tracking such as target enteringand leaving, target part shielding and target number changing.

The invention provides an underwater multi-station combined multi-target tracking method and system. The method comprises the steps: dividing observation nodes into pairs at a current sampling moment, carrying out observation pair combination, randomly selecting the observation pair combination at the same probability, carrying out the azimuth measurement cross positioning, and obtaining a new target set; generating target information at the current moment, wherein the target information comprises a track of each target; taking the target information at the current moment and the target information at the previous moment as the input of a multi-station multi-Bernoulli filter, carrying out one-step prediction and measurement updating, and outputting the maximum posteriori state estimation of the target at the current moment; and comparing the existence probability of the target with a first threshold, performing counter accumulation on the detection times exceeding the threshold, and finally comparing an accumulation result with a second threshold to control the real-time output of the target track. According to the method, a stable and continuous target track can be output in real time, and meanwhile, a short track formed by a false target is filtered out.

The invention discloses a DOA tracking method based on multi-Bernoulli filtering. The method comprises the following steps: receiving superposed measurement data through a sensor array; obtaining filtering posteriori information obtained by a multi-Bernoulli filter at the k-1 moment, wherein the filtering posteriori information comprises the existence probability of a Bernoulli component and a spatial distribution probability density function of a target; predicting the multi-Bernoulli components according to a multi-Bernoulli filter to obtain multi-Bernoulli posteriori information at the moment k; extracting a target state according to the predicted multi-Bernoulli components; and performing iteration, k is equal to k + 1 until all moments are processed. Measurement information does not need to be processed, and the calculated amount is reduced; and during tracking, the number of signal sources does not need to be known, and the signal sources are tracked in real time by directly utilizing prediction prior and current measurement information. A simulation result shows the effectiveness of the algorithm.

The invention provides a multi-cell automatic tracking method based on a multi-Bernoullie filter with a label, which comprises a step of cell set initialization by the multi-Bernoullie filter with the label, a step of prediction by the multi-Bernoullie filter with the label and a step of updating by the multi-Bernoullie filter with the label. Cell set initialization adopts the k shortest path, a new cell set is generated according to the newly-appearing cell detection probability, weights and cell probability densities of the former-frame cell set and the newly-detected cell set are used, a predicted cell set in the current frame, the cell predicted probability density in the cell set and the cell set weight are acquired, the posterior probability density of the cells in the cell set and the weight of the cell set are calculated and updated through similarities, and cell tracking can be completed with no need of relevance.

Aiming at the problem of spectrum sensing in cognitive radio, the present invention designs a joint spectrum sensing and link state estimation method device. This method for the first time incorporates a link state information between the primary user and the secondary user as another hidden state in spectrum sensing, and thus establishes a general dynamic state space model; based on Bayesian Based on the sequential estimation framework and Bernoulli random finite set theory, a Bernoulli filtering mechanism is designed, which can obtain the joint blind estimation of the primary-secondary link state information while sensing and detecting the working status of the licensed frequency band (algorithm process has been Attached). The method makes full use of the primary-secondary link state information, thereby significantly improving spectrum sensing performance; at the same time, the obtained primary-secondary link state information can also be used for subsequent resource optimization; in addition, the joint user perception and The method and device for estimating link state information is versatile and can be extended to other primary-secondary link state information.

The invention discloses a multi-maneuvering target tracking method based on random set theory, which is characterized in that firstly, the multi-target state space is augmented, and the model dimension is increased on the basis of the original dynamic information, so as to realize the target model Then, based on the jumping Markov system, the state transition function and the likelihood function are augmented to contain model information; finally, the prediction of the augmented multi-model generalized label multi-objective Bernoulli filter is realized And update process, and extract the target state and estimate the target motion model, so as to solve the tracking problem of maneuvering multiple targets. This method has the characteristics of strong robustness, wide adaptability, and high estimation accuracy. It can effectively solve the problem of strong and inconsistent multi-target maneuverability that often occurs in practical applications, and realizes maneuvering multi-target tracking in complex scenes. Estimate the target motion model.

The invention provides an L-RFS mixed target structure modeling and estimation method with a type probability. The L-RFS mixed target structure modeling and estimation method comprises three aspects of mixed target dynamic modeling, mixed target shape and type analysis and mixed target tracking estimation, and is implemented by the following steps of: firstly, establishing a measurement hybrid model of a hybrid target by combining a generalized label multi-Bernoulli filter, analyzing a target type, utilizing gibbs sampling and a BIC criterion to derive parameters of a finite hybrid model so asto carry out learning tracking on a hybrid target; and then adopting an equivalent measurement method to replace measurement generated by a multi-measurement target composed of an extended target anda group target, and adopting ellipse approximation modeling for the shape of the multi-measurement target to realize estimation of the shape of the multi-measurement target. According to the L-RFS mixed target structure modeling and estimation method, the target type can be effectively judged, and the mixed target is tracked.

The invention provides a fault tracking and positioning method based on the Internet of Things. The method comprises the following steps: constructing a multi-dynamic sensor network; coupling an anti-swarm distributed collaborative search algorithm and a multi-Bernoulli filtering algorithm to realize fault tracking and acquiring real-time sensor data; establishing a weight clustering analysis model for sensor data of a historical normal sample, and calculating to obtain a control limit of a control quantity; performing fault detection on the acquired real-time sensor data according to the established weight clustering analysis model and the control quantity, screening out a fault data segment, and obtaining a control quantity corresponding to the fault data segment; constructing a random forest classification regression algorithm based on the process variable and the control quantity of the fault data segment; acquiring a variable importance measurement of the process variable through the random forest classification regression algorithm, and determining a fault variable according to the variable importance measurement for fault positioning. The method provided by the invention has high tracking and positioning precision and obvious superiority.

The invention discloses a self-adaptive sensor management method for multi-sensor multi-target tracking. The method comprises the following steps of S100, according to sensor network bandwidth limitation and tracking processing real-time requirements, the number of sensors selected at each moment being stipulated to be P; S200, modeling sensor management as a partially observable Markov decision model; S300, estimating the amount of information which can be acquired by each sensor in the sensor network by using the partially observable Markov decision model; S400, P sensors with the most information amount being selected from all the sensors; S500, sorting the selected P sensors according to the sequence of the information amount from small to large; and S600, a control instruction being sent to the sensors, the selected P sensors being activated, target measurement being acquired, and target number and state estimation being realized by using a generalized label multi-Bernoulli filter.

The invention discloses a radar weak fluctuation target track-before-detect algorithm based on multi-Bernoulli filtering, and the algorithm not only considers amplitude information, but also carries out marginalization processing on a phase in MB-TBD, so as to improve the discrimination between a target and noise. And a square modulus likelihood ratio (SLR) is replaced with complex likelihood ratios (CLR) of three Swerling types. In order to adapt to the condition that undulating target new prior information is unknown, a multi-Bernoulli filter self-adaptive new distribution TBD (LABer-STC-TBD) based on the measurement likelihood ratio is provided by referring to the idea of target successive division, and compared with an existing MB-TBD self-adaptive new distribution algorithm, the new algorithm overcomes the defect that when a target undulates, it is difficult to detect a weak target and a strong target which appear at the same time. The Bernoulli components of the same target are combined by an algorithm (DPM) based on distance and particle weight after the updating of the MB-TBD is finished. And finally, the studied estimation and detection performances under different conditions are compared, and the advantages of the LABer-STC-TBD algorithm under target amplitude fluctuation are displayed.

The invention discloses an extended target tracking method based on GLMB filtering and Gibbs sampling, target number estimation, and extended target shape estimation problems, and proposes a multi-extended target tracking method based on a label random finite set framework. The method mainly includes Two aspects: dynamic modeling of multiple extended targets and tracking estimation of multiple extended targets. Firstly, combined with the generalized label multi-Bernoulli filter, the measurement finite mixture model of the extended target is established, and the parameters of the finite mixture model are deduced by using Gibbs sampling and Bayesian information criterion to learn and track the state of the multi-extended target, and then adopt The equivalent measurement method is used to replace the measurement produced by the extended target, and the shape of the extended target is modeled by ellipse approximation to realize the estimation of the extended target shape. The simulation experiment shows that the method provided by the present invention can effectively track multiple extended targets, accurately estimate the state and shape of the extended targets, and can obtain the track trajectory of the target.

The invention provides a fault tracking and positioning method based on the Internet of Things, comprising the following steps: constructing a multi-dynamic sensor network; coupling an anti-swarming distributed cooperative search algorithm and a multi-Bernoulli filtering algorithm to realize fault tracking and obtain real-time sensor data; Establish a weight clustering analysis model for the sensor data of historical normal samples, and calculate the control limit of the control quantity; perform fault detection on the acquired real-time sensor data according to the established weight cluster analysis model and control quantity, filter out the fault data segment and Obtain the corresponding control quantity; build a random forest classification regression algorithm based on the process variable and control quantity of the fault data segment; obtain the variable importance measure of the process variable through the random forest classification regression algorithm, and determine the fault variable according to the variable importance measure. Positioning, the method provided by the present invention has high tracking and positioning accuracy, and has obvious advantages.

The invention discloses a multi-sensor self-adaptive angle control method based on GLMB filtering, comprising the following steps: S1, using a generalized label multi-Bernoulli filter to perform local filtering; S2, performing distributed fusion based on a generalized covariance cross criterion; S3, multi-target sampling processing; S4, use generalized label multi-Bernoulli filter for pseudo-prediction; S5, calculate the ideal measurement of each sensor; S6, group the sensors in pairs, generate a decision set, Perform distributed fusion processing on the sensor pairs; S7, calculate the Cauchy-Schwartz divergence under the control decision combination of paired pairs; S8, select the local optimal sensor control decision for each sensor pair; S9, combine all the local optimal sensor control decisions; The optimal sensor control decisions are combined into one set. The invention can ensure small error loss, and solve the multi-sensor self-adaptive angle control based on GLMB filtering, which can be realized in both precision and calculation cost.

The present invention discloses a Low-computation complexity Bernoulli filter for single-object tracking. The whole Bayes derivation process is simplified, and the target state transfer function and a target measurement likelihood function are optimized. The target measurement and the state independent clutter statistics characteristics are analyzed to derive an improved sequential Monte Carlo realization process so as to effectively extract the real goal in the clutter environment. The filter can effectively track the maneuvering single goal in the clutter environment and has low computation complexity and high tracking precision.