Target tracking algorithm based on improved Gaussian mixture particle filter

Kong, Yunbo; Feng, Xinxi; Lu, Chuanguo

doi:10.1109/emeit.2011.6023565

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…29 For non-Gaussian stochastic systems, EKF is recurrently shown to be abortive, where the noises can be shot noise or Gaussian mixture noise. 16,[23][24][25]30,31 In this research paper, the outcome of both shot and mixed-Gaussian noises for the given scenarios is analyzed by performing 100 Monte Carlo runs for both DBEKF and DBUKF algorithms. To enhance stability in state estimation under non-Gaussian environment, Monte Carlo simulation is performed for both nonlinear filters (DBEKF and DBUKF) to get the approximation of any probability distribution.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, [10][11][12][13][14][15][16] the filter performances are evaluated under Gaussian noise environment only. For example, in the literature, [23][24][25][26][27][28] it is simply stated that the performance of EKF and UKF is not satisfactory if the measurements and/or states are contaminated with NGN and therefore, it is suggested to use particle filter (PF). This is also evident from the derivations of the filters.…”

Section: Introductionmentioning

confidence: 99%

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Underwater surveillance in non-Gaussian noisy environment

Jagan

Rao

2020

Measurement and Control

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The aim of this paper is to evaluate the performance of different filtering algorithms in the presence of non-Gaussian noise environment for tracking underwater targets, using Doppler frequency and bearing measurements. The tracking using Doppler frequency and bearing measurements is popularly known as Doppler-bearing tracking. Here the measurements, that is, bearings and Doppler frequency, are considered to be corrupted with two types of non-Gaussian noises namely shot noise and Gaussian mixture noise. The non-Gaussian noise sampled measurements are assumed to be obtained (a) randomly throughout the process and (b) repeatedly at some particular time samples. The efficiency of these filters with the increase in non-Gaussian noise samples is discussed in this paper. The performance of filters is compared with that of Cramer-Rao Lower Bound. Doppler-bearing extended Kalman filter and Doppler-bearing unscented Kalman filter are chosen for this work.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Underwater surveillance in non-Gaussian noisy environment

Jagan

Rao

2020

Measurement and Control

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“…In the literature [21], the Gaussian-sum CKF has been proposed for bearings-only tracking problems, in which the CKF demonstrates better performance than the particle filter does. Furthermore, for highly nonlinear passive tracking systems, an improved Gaussian mixture filter algorithm has been proposed in the literature [22], and the limited Gaussian mixture model has been used to approximate the posterior density of the state and process noises and measurement noises. However, in all these methods, while propagating the uncertainty through a nonlinear system, the weights of the Gaussian components are not regular and are updated only in the measurement update stage.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Weight Update Algorithm for Target Tracking of UUV Based on Improved Gaussian Mixture Cubature Kalman Filter

Wang

Li³

et al. 2020

Complexity

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The Gaussian mixture filter can solve the non-Gaussian problem of target tracking in complex environment by the multimode approximation method, but the weights of the Gaussian component of the conventional Gaussian mixture filter are only updated with the arrival of the measurement value in the measurement update stage. When the nonlinear degree of the system is high or the measurement value is missing, the weight of the Gauss component remains unchanged, and the probability density function of the system state cannot be accurately approximated. To solve this problem, this paper proposes an algorithm to update adaptive weights for the Gaussian components of a Gaussian mixture cubature Kalman filter (CKF) in the time update stage. The proposed method approximates the non-Gaussian noise by splitting the system state, process noise, and observation noise into several Gaussian components and updates the weight of the Gaussian components in the time update stage. The method contributes to obtaining a better approximation of the posterior probability density function, which is constrained by the substantial uncertainty associated with the measurements or ambiguity in the model. The estimation accuracy of the proposed algorithm was analyzed using a Taylor expansion. A series of extensive trials was performed to assess the estimation precision corresponding to various algorithms. The results based on the data pertaining to the lake trial of an unmanned underwater vehicle (UUV) demonstrated the superiority of the proposed algorithm in terms of its better accuracy and stability compared to those of conventional tracking algorithms, along with the associated reasonable computational time that could satisfy real-time tracking requirements.

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“…A Gaussian sum CKF has been proposed for bearings-only tracking problems in the literature [19], and this CKF displays comparable performance to the particle filter. An improved Gaussian mixture filter algorithm has been proposed for highly nonlinear passive tracking systems in the literature [20], and the limited Gaussian mixture model has been used to approximate the posterior density of the state, process noise, and measurement noise. However, in all of these methods, the weights of the Gaussian components are kept constant while propagating the uncertainty through a nonlinear system and are updated only in the stage of measurement update.…”

Section: Introductionmentioning

confidence: 99%

An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise

Wang

2016

Discrete Dynamics in Nature and Society

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In order to solve the problems that the weight of Gaussian components of Gaussian mixture filter remains constant during the time update stage, an improved Gaussian Mixture Cubature Kalman Filter (IGMCKF) algorithm is designed by combining a Gaussian mixture density model with a CKF for target tracking. The algorithm adopts Gaussian mixture density function to approximately estimate the observation noise. The observation models based on Mini RadaScan for target tracking on offing are introduced, and the observation noise is modelled as glint noise. The Gaussian components are predicted and updated using CKF. A cost function is designed by integral square difference to update the weight of Gaussian components on the time update stage. Based on comparison experiments of constant angular velocity model and maneuver model with different algorithms, the proposed algorithm has the advantages of fast tracking response and high estimation precision, and the computation time should satisfy real-time target tracking requirements.

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Target tracking algorithm based on improved Gaussian mixture particle filter

Cited by 4 publications

References 9 publications

Underwater surveillance in non-Gaussian noisy environment

Underwater surveillance in non-Gaussian noisy environment

Adaptive Weight Update Algorithm for Target Tracking of UUV Based on Improved Gaussian Mixture Cubature Kalman Filter

An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise

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