scholarly journals Application of Spherical-Radial Cubature Bayesian Filtering and Smoothing in Bearings Only Passive Target Tracking

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1088 ◽  
Author(s):  
Wasiq Ali ◽  
Yaan Li ◽  
Zhe Chen ◽  
Muhammad Asif Zahoor Raja ◽  
Nauman Ahmed ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Nonlinear Filtering ◽  
Unscented Kalman Filter ◽  
State Space Model ◽  
Moving Target ◽  
Bayesian Filtering ◽  
Tracking Problem ◽  
Cubature Kalman Filter ◽  
Passive Target Tracking

In this paper, an application of spherical radial cubature Bayesian filtering and smoothing algorithms is presented to solve a typical underwater bearings only passive target tracking problem effectively. Generally, passive target tracking problems in the ocean environment are represented with the state-space model having linear system dynamics merged with nonlinear passive measurements, and the system is analyzed with nonlinear filtering algorithms. In the present scheme, an application of spherical radial cubature Bayesian filtering and smoothing is efficiently investigated for accurate state estimation of a far-field moving target in complex ocean environments. The nonlinear model of a Kalman filter based on a Spherical Radial Cubature Kalman Filter (SRCKF) and discrete-time Kalman smoother known as a Spherical Radial Cubature Rauch–Tung–Striebel (SRCRTS) smoother are applied for tracking the semi-curved and curved trajectory of a moving object. The worth of spherical radial cubature Bayesian filtering and smoothing algorithms is validated by comparing with a conventional Unscented Kalman Filter (UKF) and an Unscented Rauch–Tung–Striebel (URTS) smoother. Performance analysis of these techniques is performed for white Gaussian measured noise variations, which is a significant factor in passive target tracking, while the Bearings Only Tracking (BOT) technology is used for modeling of a passive target tracking framework. Simulations based experiments are executed for obtaining least Root Mean Square Error (RMSE) among a true and estimated position of a moving target at every time instant in Cartesian coordinates. Numerical results endorsed the validation of SRCKF and SRCRTS smoothers with better convergence and accuracy rates than that of UKF and URTS for each scenario of passive target tracking problem.

Target Tracking for Robot-Fishes Based on Unscented Kalman Filter

2014 ◽  
Vol 536-537 ◽  
pp. 979-983
Author(s):  
Peng Fei Bai ◽  
Yu Lian Jiang ◽  
Rong Fang Lei
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Water Waves ◽  
Nonlinear Filtering ◽  
Unscented Kalman Filter ◽  
Tracking Error ◽  
Sampling Strategy ◽  
Underwater Robot ◽  
Fast Tracking ◽  
Robot Fish

Because of the nonlinear motion of the robot-fish and the uncertain water waves, it is difficult to track dynamic target accurately and quickly for the robot-fish. To solve this problem, this paper proposed the target tracking strategy of underwater robot-fish based on Unscented Kalman filter (UKF). UKF is a nonlinear filtering on the basis of unscented transformation, which consists of prediction and update recursively, thus estimate the state of the system. To avoid the particle degeneracy, this paper selects sigma points adopting scale symmetric sampling strategy. We apply UKF method to the underwater robot-fish competition in China. Simulations show that the Robot-fish has fast tracking speed, shorter tracking path and smaller tracking error using UKF method than not using the method. It has excellent tracking performance.

Three-Dimensional Submarine-to-Submarine Passive Target Tracking in the Presence of Non-Gaussian Noises

2021 ◽  
Vol 17 (3) ◽  
pp. 1-24
Author(s):  
Kavitha Lakshmi M. ◽  
Koteswara Rao S. ◽  
Subrahmanyam Kodukula
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Gaussian Noise ◽  
Target Location ◽  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Noise Environment ◽  
Cubature Kalman Filter ◽  
Highly Nonlinear ◽  
Non Gaussian

In underwater surveillance, three-dimensional target tracking is a challenging task. The angles-only measurements (i.e., bearing and elevation) obtained by hull mounted sensors are considered to appraise the target motion parameter. Due to noise in measurements and nonlinearity of the system, it is very hard to find out the target location. For many applications, UKF is best estimator that remaining algorithms. Recently, cubature Kalman filter (CKF) is also popular. It is proposed to use UKF (unscented Kalman filter) and CKF (cubature Kalman filter) algorithms that minimize the noise in measurements. So far, researchers carried out this work (target tracking) in Gaussian noise environment, whereas in this paper same work is carried out for non-Gaussian noise environment. The performance evaluation of the filters using Monte-Carlo simulation and Cramer-Rao lower bound (CRLB) is accomplished and the results are analyzed. Result shows that UKF is well suitable for highly nonlinear systems than CKF.

scholarly journals Nonlinear Filtering for Hybrid GPS/GSM Mobile Terminal Tracking

2010 ◽  
Vol 2010 ◽  
pp. 1-17 ◽  
Author(s):  
Carsten Fritsche ◽  
Anja Klein
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Nonlinear Filtering ◽  
Unscented Kalman Filter ◽  
Mobile Terminal ◽  
Localization Method ◽  
Cubature Kalman Filter ◽  
Global Positioning ◽  
Hybrid Localization ◽  
Time Information

The Global Positioning System (GPS) has become one of the state-of-the-art location systems that offers reliable mobile terminal (MT) location estimates. However, there exist situations where GPS is not available, for example, when the MT is used indoors or when the MT is located close to high buildings. In these scenarios, a promising approach is to combine the GPS-measured values with measured values from the Global System for Mobile Communication (GSM), which is known as hybrid localization method. In this paper, three nonlinear filters, namely, an extended Kalman filter, a Rao-Blackwellized unscented Kalman filter, and a modified version of the recently proposed cubature Kalman filter, are proposed that combine pseudoranges from GPS with timing advance and received signal strengths from GSM. The three filters are compared with each other in terms of performance and computational complexity. Posterior Cramér-Rao lower bounds are evaluated in order to assess the theoretical performance. Furthermore, it is investigated how additional GPS reference time information available from GSM influences the performance of the hybrid localization method. Simulation and experimental results show that the proposed hybrid method outperforms the GSM method.

scholarly journals Passive Target Tracking using Unscented Kalman Filter based on Monte Carlo Simulation

2015 ◽  
Vol 8 (29) ◽  
Author(s):  
K. Lova Raju ◽  
S. Koteswara Rao ◽  
Rudra Pratap Das ◽  
M. Nalini Santhosh ◽  
A. Sampath Dakshina Murthy
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Passive Target Tracking

scholarly journals Improving bearings-only target state estimation tracking problem by using adaptive and nonlinear kalman algorithms

2019 ◽  
Vol 15 (1) ◽  
pp. 190
Author(s):  
Tammam Khadour ◽  
Michel Al Saba ◽  
Louay Saleh
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Noisy Data ◽  
Two Dimensions ◽  
Tracking Problem ◽  
Tracking Systems ◽  
Font Size ◽  
Cubature Kalman Filter ◽  
Nonlinear Kalman Filter ◽  
Measurement Variance

<p><span style="font-family: 'Times New Roman'; font-size: 9pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Finding the best estimate of the process state from noisy data is the main problem in tracking systems, many efforts and researches have been done to remove this noise. More useful information about the target’s state can be extracted from observations by using a more appropriate model for the target’s motion or using additional sensors. In this paper, we will introduce two methods to improve the estimation of bearing-only target tracking problem in two dimensions (2D). The first method is by adding a third sensor and making a good alignment of those sensors, and at the same time an extended Kalman filter (EKF), unscented Kalman filter (UKF) and cubature Kalman filter (CKF) are implemented. The second method is by applying an adaptive nonlinear Kalman filter (ANKF) for two sensors to solve the problem of measurement variance uncertainty.</span></p>

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