Particle filtering is investigated extensively due to its importance in target tracking for nonlinear and non-Gaussian models. A particle filter can track an arbitrary trajectory only if the target dynamics models are known and the time instant when trajectory switches from one model to another model is known a priori. In real applications, it is unlikely to meet both these conditions. We propose a novel method that overcomes the lack of this knowledge. In the proposed method, an interacting multiple-model-based approach is exploited along with particle filtering. Moreover, we automate the model selection process for tracking an arbitrary trajectory. In the proposed approach, a priori information about the exact model that a target may follow is not required. Another problem with multiple trajectory tracking using a particle filter is data association, namely, observation to track fusion. For data association, we use three methods. In the first case, an implicit observation to track assignment is performed using a nearest neighbor (NN) method for data association; this is fast and easy to implement. In the second method, the uncertainty about the origin of an observation is overcome by using a centroid of measurements to evaluate weights for particles as well as to calculate the likelihood of a model. In the third method, a Markov random field (MRF)-based method is used. The MRF method enables us to exploit the neighborhood concept for data association, i.e., the association of a measurement influences an association of its neighboring measurement.
Data association and model selection are important factors for tracking multiple targets in a dense clutter environment without using a priori information about the target dynamic. We propose an interacting multiple model-expectation maximization (IMM-EM) algorithm by incorporating different dynamic models for the target and using Markov random field (MRF) for data association. In this way we are able to track maneuvering and nonmaneuvering targets simultaneously in a single batch mode (sequential). Moreover, it can be used for real-time application. The proposed method overcomes the problem of data association by incorporating all validated measurements together using an EM algorithm and exploiting MRF. It treats the data association problem as an incomplete data problem and measurement association as missing data. In the proposed method, all validated measurements are used to update the target state, and the probability density function (pdf) of observed data, given a target state and measurement association, is treated as a mixture pdf. This allows us to combine the likelihood of a measurement due to each model. The association process now incorporates IMM, and consequently, it is possible to track any arbitrary trajectory. We also consider two different cases for association of measurement to target: association of each measurement to the target is independent of each other; and association of a measurement influences an association of its neighbor measurement.
Data association and model selection are important factors for tracking
multiple targets in a dense clutter environment without using apriori
information about the target dynamic. We propose a neural network based
tracking algorithm, incorporating interacting multiple model to track
both maneuvering and non-maneuvering targets simultaneously in the
presence of dense clutter. For data association, we use the
Expectation-Maximization (EM) algorithm and Hopfield network to
evaluate assignment weights. All validated measurements are used to
update the target state and hence, it avoids the uncertainty about the
origin of the measurements. In the proposed approach the data
association process is defined to incorporate multiple models for
target dynamics and probability density function (pdf) of an observed
data given target state and measurement association, is treated as a
mixture pdf. This allows to combine the likelihood of a measurement due
to each model, and consequently, it is possible to track any arbitrary
trajectory in the presence of dense clutter.
Particle filtering is being investigated extensively due to its important
feature of target tracking based on nonlinear and non-Gaussian model.
It tracks a trajectory with a known model at a given time. It means that
particle filter tracks an arbitrary trajectory only if the time instant
when trajectory switches from one model to another model is known apriori.
Because of this reason particle filter is not able to track any arbitrary
trajectory where transition from one model to another model is not known.
For real world application, trajectory is always random in nature and may
follow more than one model. Another problem with multiple trajectories
tracking using particle filter is the data association,
i.e. observation to track fusion. In this paper we propose a novel
method, which overcomes the above problems. In a proposed method an
interacting multiple model based approach is used along with particle
filtering, which automates the model selection process for tracking an
arbitrary trajectory. We have utilized nearest neighbor (NN) method for
data association, which is fast and easy to implement.
Data association and model selection are important factors for tracking multiple targets in a dense clutter environment without using apriori information about the target dynamic. We propose Interacting Multiple Model-Expectation Maximization (IMM-EM) algorithm, by incorporating different dynamic models for the target and Markov Random Field (MRF) for data association, and hence it is possible to track maneuvering and non-maneuvering targets simultaneously in a single batch mode (sequential). Moreover it can be used for real time application.
The proposed method overcomes the problem of data association by incooperating all validated measurements together using EM algorithm and exploiting MRF. It treats the data association problem as incomplete data problem. In the proposed method, all validated measurements are used to update the target state. It uses only measurement association as missing data, which simplifies E-step and
M-step of the algorithm. In the proposed approach probability density function (pdf) of an observed data given target state and measurement association, is treated as a mixture pdf. This allows to combine likelihood of a measurement due to each model, and the association process is defined to incorporate IMM and consequently, it is possible to track any arbitrary trajectory. We also consider two different cases for association of measurement to target: Case I:- association of each measurement to target is independent of each other, Case II:- association of a measurement influences an association of its neighbor measurement.
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