Two-step optimal estimator for three dimensional target tracking

2003 ◽  
Author(s):  
P. Gurfil ◽  
N.J. Kasdin
Keyword(s):  
Target Tracking ◽  
Three Dimensional ◽  
Optimal Estimator

Three-dimensional target tracking and autonomous positioning using probe-holding robot

Choonpa Igaku ◽  
2018 ◽  
Vol 45 (2) ◽  
pp. 149-157 ◽  
Author(s):  
Ryu NAKADATE ◽  
Makoto HASHIZUME
Keyword(s):  
Target Tracking ◽  
Three Dimensional

A digital approach to dynamic jaw tracking using a target tracking system and a structured-light three-dimensional scanner

2019 ◽  
Vol 63 (1) ◽  
pp. 115-119 ◽  
Author(s):  
Joo Hyun Kwon ◽  
Sungbin Im ◽  
Minho Chang ◽  
Jong-Eun Kim ◽  
June-Sung Shim
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Structured Light ◽  
Three Dimensional

Shifted Rayleigh filter: a novel estimation filtering algorithm for pervasive underwater passive target tracking for computation in 3D by bearing and elevation measurements

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lakshmi M. Kavitha ◽  
Rao S. Koteswara ◽  
K. Subrahmanyam
Keyword(s):  
Target Tracking ◽  
Long Range ◽  
Target Location ◽  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Content Type ◽  
Solution Convergence ◽  
Passive Target Tracking ◽  
Underwater Target ◽  
Target Motion Analysis

Purpose Marine exploration is becoming an important element of pervasive computing underwater target tracking. Many pervasive techniques are found in current literature, but only scant research has been conducted on their effectiveness in target tracking. Design/methodology/approach This research paper, introduces a Shifted Rayleigh Filter (SHRF) for three-dimensional (3 D) underwater target tracking. A comparison is drawn between the SHRF and previously proven method Unscented Kalman Filter (UKF). Findings SHRF is especially suitable for long-range scenarios to track a target with less solution convergence compared to UKF. In this analysis, the problem of determining the target location and speed from noise corrupted measurements of bearing, elevation by a single moving target is considered. SHRF is generated and its performance is evaluated for the target motion analysis approach. Originality/value The proposed filter performs better than UKF, especially for long-range scenarios. Experimental results from Monte Carlo are provided using MATLAB and the enhancements achieved by the SHRF techniques are evident.

A Target Tracking Algorithm Based on Internet of Things Spatial Meshing

2014 ◽  
Vol 513-517 ◽  
pp. 1261-1267
Author(s):  
Jia Hong He ◽  
Xiao Ming Zhang ◽  
Yong Heng Wang
Keyword(s):  
Internet Of Things ◽  
Power Consumption ◽  
Target Tracking ◽  
Spatial Clustering ◽  
Cluster Head ◽  
Three Dimensional ◽  
Tracking Algorithm ◽  
Fitting Method ◽  
Mobile Target ◽  
Network Overhead

The three-dimensional spatial target tracking based on wireless communication technology has attracted more and more attention due to its importance in the field of Internet of Things.However,there are still some problems including calculation overhead is too high and power consumption is too large.Thus,a distributed three-dimensional target tracking mechanism for the environment of the Internet of Things is proposed.The network structure in the algorithm uses spatial clustering structure, which includes two tier sleep scheduling mechanisms and unite cluster head mechanism. A spatial segmental linear fitting method is adopted to track the target,which have effectively reduced the network overhead and improved tracking efficiency. It also provides a scheduling strategy how to wake up the sensor node guarantee to continue tracking it,when the mobile target lost.Simulation results show that the algorithm is better than the existing target tracking algorithm in tracking efficiency and have a lower power consumption.

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.

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