scholarly journals Adaptive Two-Step Bearing-Only Underwater Uncooperative Target Tracking with Uncertain Underwater Disturbances

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 907
Author(s):  
Xianghao Hou ◽  
Jianbo Zhou ◽  
Yixin Yang ◽  
Long Yang ◽  
Gang Qiao
Keyword(s):  
Target Tracking ◽  
Iteration Scheme ◽  
Measurement Noise ◽  
Extended State ◽  
Uncertain Process ◽  
Current State ◽  
Underwater Environment ◽  
Simulation Results ◽  
Underwater Target ◽  
Tracking Filter

The bearing-only tracking of an underwater uncooperative target can protect maritime territories and allows for the utilization of sea resources. Considering the influences of an unknown underwater environment, this work aimed to estimate 2-D locations and velocities of an underwater target with uncertain underwater disturbances. In this paper, an adaptive two-step bearing-only underwater uncooperative target tracking filter (ATSF) for uncertain underwater disturbances is proposed. Considering the nonlinearities of the target’s kinematics and the bearing-only measurements, in addition to the uncertain noise caused by an unknown underwater environment, the proposed ATSF consists of two major components, namely, an online noise estimator and a robust extended two-step filter. First, using a modified Sage-Husa online noise estimator, the uncertain process and measurement noise are estimated at each tracking step. Then, by adopting an extended state and by using a robust negative matrix-correcting method in conjunction with a regularized Newton-Gauss iteration scheme, the current state of the underwater uncooperative target is estimated. Finally, the proposed ATSF was tested via simulations of a 2-D underwater uncooperative target tracking scenario. The Monte Carlo simulation results demonstrated the reliability and accuracy of the proposed ATSF in bearing-only underwater uncooperative tracking missions.

scholarly journals Adaptive Target Birth Intensity Multi-Bernoulli Filter with Noise-Based Threshold

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1120 ◽  
Author(s):  
Xiaolong Hu ◽  
Hongbing Ji ◽  
Long Liu
Keyword(s):  
Target Tracking ◽  
Sequential Monte Carlo ◽  
Measurement Noise ◽  
Tracking Accuracy ◽  
Operation Efficiency ◽  
Processing Step ◽  
Multi Target Tracking ◽  
Filtering Efficiency ◽  
Bernoulli Filter ◽  
Simulation Results

Adaptively modeling the target birth intensity while maintaining the filtering efficiency is a challenging issue in multi-target tracking (MTT). Generally, the target birth probability is predefined as a constant and only the target birth density is considered in existing adaptive birth models, resulting in deteriorated target tracking accuracy, especially in the target appearing cases. In addition, the existing adaptive birth models also give rise to a decline in operation efficiency on account of the extra birth modeling calculations. To properly adapt the real variation of the number of newborn targets and improve the multi-target tracking performance, a novel fast sequential Monte Carlo (SMC) adaptive target birth intensity cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is proposed in this paper. Through adaptively conducting the target birth probability in a pre-processing step, which incorporates the information of current measurements to correct the pre-setting of the target birth probability, the proposed filter can truly adapt target birth cases and achieve better tracking accuracy. Moreover, the implementation efficiency can be improved significantly by employing a measurement noise-based threshold in the likelihood calculations of the multi-target updating. Simulation results verify the effectiveness of the proposed filter.

Research on Localization Principle and Method for Multistation Range-only Target Tracking System

2013 ◽  
Vol 380-384 ◽  
pp. 1091-1095
Author(s):  
Lu Wang ◽  
Zhong Liu
Keyword(s):  
Target Tracking ◽  
Simple Form ◽  
Tracking System ◽  
High Accuracy ◽  
Target Localization ◽  
Localization Principle ◽  
Straight Line ◽  
Simulation Results ◽  
Underwater Target ◽  
Line Movement

Observable condition for multistation range-only system was analyzed according to the characteristics of underwater target localization in the conditions of network warfare. When the target is stationary or travels in straight-line movement, it is able to be observed by range-only system. The target localization parameters are estimated by least squares estimator algorithm, which has simple form and does not need initial values. Simulation results show that the algorithm has fast convergence speed, good stability and high accuracy.

Measure of nonlinearity for underwater target tracking using hull-mounted sensor

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
B. Omkar Lakshmi Jagan ◽  
S. Koteswara Rao
Keyword(s):  
State Estimation ◽  
Target Tracking ◽  
Estimation Accuracy ◽  
Filtering Algorithms ◽  
Content Type ◽  
Underwater Environment ◽  
System Nonlinearity ◽  
Single Sensor ◽  
Underwater Target ◽  
Rms Errors

PurposeDoppler-Bearing Tracking (DBT) is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor (HMS). It is an important and challenging problem in an underwater environment.Design/methodology/approachThe system nonlinearity in an underwater environment increases due to several reasons such as the type of measurements taken, the speeds of target and observer, environmental conditions, number of sensors considered for measurements and so on. Degrees of nonlinearity (DoNL) for these problems are analyzed using a proposed measure of nonlinearity (MoNL) for state estimation.FindingsIn this research, the authors analyzed MoNL for state estimation and computed the conditional MoNL (normalized) using different filtering algorithms where measurements are obtained from a single sensor array (i.e. HMS). MoNL is implemented to find out the system nonlinearity for different filtering algorithms and identified how much nonlinear the system is, that is, to measure nonlinearity of a problem.Originality/valueAlgorithms are evaluated for various scenarios with different angles on the target bow (ATB) in Monte-Carlo simulation. Computation of root mean squared (RMS) errors in position and velocity is carried out to assess the state estimation accuracy using MATLAB.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

scholarly journals Research on underwater target tracking based on Gaussian Hermitian Kalman Particle filter algorithm

2021 ◽  
Vol 1748 ◽  
pp. 032037
Author(s):  
Chengzhen Zhang ◽  
Yang Yang ◽  
Yuanming Ding
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Particle Filter Algorithm ◽  
Underwater Target

A Zigzag Maneuver Target Tracking Algorithm with Colored Glint Measurement Noise

2021 ◽  
Author(s):  
Chenghao Shan ◽  
Weidong Zhou
Keyword(s):  
Target Tracking ◽  
Tracking Algorithm ◽  
Measurement Noise

scholarly journals A Robust SMC-PHD Filter for Multi-Target Tracking with Unknown Heavy-Tailed Measurement Noise

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3611
Author(s):  
Yang Gong ◽  
Chen Cui
Keyword(s):  
Target Tracking ◽  
Sequential Monte Carlo ◽  
Measurement Noise ◽  
Particle Simulation ◽  
Target Number ◽  
Student T Distribution ◽  
Multi Target Tracking ◽  
Heavy Tailed ◽  
Scale Matrix ◽  
T Distribution

In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, the SMC-PHD filter suffers severe performance degradation. In this paper, a robust SMC-PHD (RSMC-PHD) filter is proposed. In the proposed filter, Student-t distribution is introduced to describe the unknown heavy-tailed measurement noise where the degrees of freedom (DOF) and the scale matrix of the Student-t distribution are respectively modeled as a Gamma distribution and an inverse Wishart distribution. Furthermore, the variational Bayesian (VB) technique is employed to infer the unknown DOF and scale matrix parameters while the recursion estimation framework of the RSMC-PHD filter is derived. In addition, considering that the introduced Student- t distribution might lead to an overestimation of the target number, a strategy is applied to modify the updated weight of each particle. Simulation results demonstrate that the proposed filter is effective with unknown heavy-tailed measurement noise.

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