scholarly journals An Active Contour and Kalman Filter for Underwater Target Tracking and Navigation

10.5772/4699 ◽  
2006 ◽  
Author(s):  
Muhammad Asif ◽  
Mohd Rizal
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Active Contour ◽  
Underwater Target

scholarly journals Modified Gain Bearing-only Extended Kalman Filter for Underwater Target Tracking

2017 ◽  
Vol 10 (8) ◽  
pp. 1-5
Author(s):  
N. Priyanka ◽  
M. Yamini ◽  
S. Koteswara Rao ◽  
A. Jawahar ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Extended Kalman Filter ◽  
Underwater Target

scholarly journals Underwater Bearing-only and Bearing-Doppler Target Tracking Based on Square Root Unscented Kalman Filter

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 740 ◽  
Author(s):  
Li ◽  
Zhao ◽  
Yu ◽  
Wei
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Tracking System ◽  
Positive Definiteness ◽  
Square Root ◽  
Tracking Accuracy ◽  
Underwater Target ◽  
State Observability ◽  
Passive Measurements

Underwater target tracking system can be kept covert using the bearing-only or the bearing-Doppler measurements (passive measurements), which will reduce the risk of been detected. According to the characteristics of underwater target tracking, the square root unscented Kalman filter (SRUKF) algorithm, which is based on the Bayesian theory, was applied to the underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. Aiming at the shortcomings of the unscented Kalman filter (UKF), the SRUKF uses the QR decomposition and the Cholesky factor updating, in order to avoid that the process noise covariance matrix loses its positive definiteness during the target tracking period. The SRUKF uses sigma sampling to avoid the linearization of the nonlinear bearing-only and the bearing-Doppler measurements. To ensure the target state observability in underwater target tracking, the paper uses single maneuvering observer to track the single non-maneuverable target. The simulation results show that the SRUKF has better tracking performance than the extended Kalman filter (EKF) and the UKF in tracking accuracy and stability, and the computational complexity of the SRUKF algorithm is low.

Pervasive underwater passive target tracking for the computation of standard deviation solution in a 3D environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
M. Kavitha Lakshmi ◽  
S. Koteswara Rao ◽  
Kodukula Subrahmanyam
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Kalman Filter ◽  
Standard Deviation ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Content Type ◽  
3D Space ◽  
Underwater Target

PurposeNowadays advancement in acoustic technology can be explored with marine assets. The purpose of the paper is pervasive computing underwater target tracking has aroused military and civilian interest as a key component of ocean exploration. While many pervasive techniques are currently found in the literature, there is little published research on the effectiveness of these paradigms in the target tracking context.Design/methodology/approachThe unscented Kalman filter (UKF) provides good results for bearing and elevation angles only tracking. Detailed methodology and mathematical modeling are carried out and used to analyze the performance of the filter based on the Monte Carlo simulation.FindingsDue to the intricacy of maritime surroundings, tracking underwater targets using acoustic signals, without knowing the range parameter is difficult. The intention is to find out the solution in terms of standard deviation in a three-dimensional (3D) space.Originality/valueA new method is found for the acceptance criteria for range, course, speed and pitch based on the standard deviation for bearing and elevation 3D target tracking using the unscented Kalman filter covariance matrix. In the Monte Carlo simulation, several scenarios are used and the results are shown.

The generalized Kalman filter approach to adaptive underwater target tracking

1992 ◽  
Vol 17 (1) ◽  
pp. 129-137 ◽  
Author(s):  
F. El-Hawary ◽  
F. Aminzadeh ◽  
G.A.N. Mbamalu
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Filter Approach ◽  
Underwater Target ◽  
Generalized Kalman Filter

scholarly journals Underwater Target Tracking using Unscented Kalman Filter

2015 ◽  
Vol 8 (31) ◽  
Author(s):  
Mylapilli Nalini Santhosh ◽  
S. Koteswara Rao ◽  
Rudra Pratap Das ◽  
K. Lova Raju
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Underwater Target

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
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