Pseudo Linear Kalman Filter For Underwater Target Location Using Intercept Sonar Measurements

2006 ◽  
Author(s):  
S.K. Rao
Keyword(s):  
Kalman Filter ◽  
Target Location ◽  
Underwater Target

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.

scholarly journals The Nonsequential Fusion Method for Localization from Unscented Kalman Filter by Multistation Array Buoys

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Gou Yanni ◽  
Wang Qi
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filtering ◽  
Target Location ◽  
Unscented Kalman Filter ◽  
Research Field ◽  
Measuring Error ◽  
Engineering Practice ◽  
Relevant Parameter ◽  
Unscented Transformation ◽  
Target Track

Based on special features of array buoy and the research field of location and tracking of underwater target, the research combines the highly adaptive nonlinear filtering algorithm unscented Kalman filter with the nonlinear programming of multistation array buoy positioning system. In accordance with the model of nonsequential target location, the research utilizes Unscented Transformation to update the measuring error and covariance matrix of state error, aiming at estimating the filtering of state variable and acquiring the object’s current state of motion. The research analyzes the positioning performance of algorithm, pursuit path, astringency, and other performance indexes of target-relevant parameter through numerical simulation experiment. From the result, the conclusion that multistation array buoy can complete the task of tracing target track very well can be reached, which provides theoretical foundation for putting the algorithm into engineering practice.

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.

Applied-Information Technology in Adaptive Kalman Filter with Model Error and Noise Error

2014 ◽  
Vol 1046 ◽  
pp. 380-383
Author(s):  
Juan Wang ◽  
Wei Wei Tao ◽  
Chun Ying Wu
Keyword(s):  
Kalman Filter ◽  
Target Location ◽  
Mean Shift ◽  
Model Error ◽  
Adaptive Kalman Filter ◽  
Current Frame ◽  
Mean Shift Algorithm ◽  
System Equation ◽  
Previous Frame ◽  
Noise Error

Kalman filter is successfully used to predict the object position under occlusion in this paper. Firstly, according to the target location in the previous frame, Kalman filter predicts target location in the current frame adaptively.Secondly, find the real target location in the neighborhood by mean shift algorithm. Finally, update the filter parameters. Because the adaptive Kalman filter predicts target location through system equation, it can improve the tracking effect in occlusion in a certain degree.

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 Application of Cubature Information Filter for Underwater Target Path Estimation

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Guduru Naga Divya ◽  
Sanagapallea Koteswara Rao
Keyword(s):  
Kalman Filter ◽  
Order Approximation ◽  
Third Order ◽  
Information Filter ◽  
Cubature Kalman Filter ◽  
System Nonlinearity ◽  
Decentralized Data Fusion ◽  
Target Path ◽  
Underwater Target ◽  
First Order Approximation

Bearings-only tracking plays a pivotal role in passive underwater surveillance. Using noisy sonar bearing measurements, the target motion parameters (TMP) are extensively estimated using the extended Kalman filter (EKF) because of its simplicity and low computational load. The EKF utilizes the first order approximation of the nonlinear system in estimation of the TMP that degrades the accuracy of estimation due to the elimination of the higher order terms. In this paper, the cubature Kalman filter (CKF) that captures the system nonlinearity upto third order is proposed to estimate the TMP. The CKF is further extended using the information filter (IF) to provide decentralized data fusion, hence the filter is termed as cubature information filter (CIF). The results are generated using Matlab with Gaussian assumption of noise in measurements. Monte-Carlo simulation is done and the results demonstrate that the CIF accuracy is same as that of UKF and this indicates the usefulness of the algorithm for state estimation in underwater with the required accuracy.

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.

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