S Koteswara Rao | Koneru Lakshmaiah (original) (raw)

Papers by S Koteswara Rao

Research paper thumbnail of The Fusion of Bearing and Frequency Measurements from Multi-sensor Arrays for Underwater Target Tracking

2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 2021

Estimation is the process of generating information that is not directly available from raw measu... more Estimation is the process of generating information that is not directly available from raw measurements to obtain different target motion parameters. To fulfil this aim, different measurements from various sensors are fused to obtain a more accurate and reliable estimate of the target state. This task is more complex when the sensors operate in passive mode. In this research work, passive measurements i.e., bearings from an array mounted on the hull (HMA) and array sensors towed behind the observer along with doppler shifted frequency measurements from the towed array are fused to obtain kinematic parameters of the desired target. The sub-optimal filter, Unscented Kalman filter is considered for the performance analysis. The results are compared to that of simple doppler-bearing tracking using measurements from HMA. The simulation is carried in MATLAB software using the Monte-Carlo simulation method for better analysis of the results.

Research paper thumbnail of Acceptance Criteria of Bearings-only Passive Target Tracking Solution

IETE Journal of Research, 2021

Research paper thumbnail of Unscented Particle Filter Approach for Underwater Target Tracking

International Journal of e-Collaboration, 2021

This research aims to find an appropriate approach to improve system accuracy in the Doppler-bear... more This research aims to find an appropriate approach to improve system accuracy in the Doppler-bearing tracking (DBT) problem for target estimation. The topic of DBT problem is to achieve a target trajectory using bearing and frequency measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. The unscented particle filter approach is proposed to estimate the accuracy in the target motion parameters (TMP). This approach requires the observer maneuver so that the target trajectory is observable. Although in recent research papers, DBT has been proven to work efficiently without observer maneuver, TMP is unknown to the observer, and consequently, there is a need for observer maneuver. So, the algorithm is simulated with observer following s-maneuver and without any maneuver executed by the observer, and results are compared. The effectiveness of the solution and results are determined by using MATLAB simulation. It is shown that t...

Research paper thumbnail of Estimate-Merge-Technique-based algorithms to track an underwater moving target using towed array bearing-only measurements

Sādhanā

Bearing-only passive target tracking is a well-known underwater defence issue dealt in the recent... more Bearing-only passive target tracking is a well-known underwater defence issue dealt in the recent past with the conventional nonlinear estimators like extended Kalman filter (EKF) and unscented Kalman filter (UKF). It is being treated now-a-days with the derivatives of EKF, UKF and a highly sophisticated particle filter (PF). In this paper, two novel methods based on the Estimate Merge Technique are proposed. The Estimate Merge Technique involves a process of getting a final estimate by the fusion of a posteriori estimates given by different nonlinear estimates, which are in turn driven by the towed array bearing-only measurements. The fusion of the estimates is done with the weighted least squares estimator (WLSE). The two novel methods, one named as Pre-Merge UKF and the other Post-Merge UKF, differ in the way the feedback to the individual UKFs is applied. These novel methods have an advantage of less root mean square estimation error in position and velocity compared with the EKF and UKF and at the same time require much lesser number of computations than that of the PF, showing that these filters can serve as an optimal estimator. A testimony of the aforementioned advantages of the proposed novel methods is shown by carrying out Monte Carlo simulation in MATLAB R2009a for a typical war time scenario.

Research paper thumbnail of Robust adaptive unscented Kalman filter for attitude estimation of pico satellites

Unscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good estimation re... more Unscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good estimation results for the estimation problems of nonlinear systems even when high nonlinearity is in question. However, in case of system uncertainty or measurement malfunctions, the UKF becomes inaccurate and diverges by time. This study introduces a fault-tolerant attitude estimation algorithm for pico satellites. The algorithm uses a robust adaptive UKF, which performs correction for the process noise covariance (Q-adaptation) or measurement noise covariance (R-adaptation) depending on the type of the fault. By the use of a newly proposed adaptation scheme for the conventional UKF algorithm, the fault is detected and isolated, and the essential adaptation procedure is followed in accordance with the fault type. The proposed algorithm is tested as a part of the attitude estimation algorithm of a pico satellite. it with a time-dependent variable. One of the methods for constructing such algorithm is to use a scale factor as a multiplier to the process or measurement noise covariance matrices . This kind of gain correction-based algorithms can be used when the information about the dynamic or measurement process is absent . Hence, the adaptation against both the system uncertainty and the measurement malfunctions is possible.

Research paper thumbnail of The Fusion of Bearing and Frequency Measurements from Multi-sensor Arrays for Underwater Target Tracking

2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 2021

Estimation is the process of generating information that is not directly available from raw measu... more Estimation is the process of generating information that is not directly available from raw measurements to obtain different target motion parameters. To fulfil this aim, different measurements from various sensors are fused to obtain a more accurate and reliable estimate of the target state. This task is more complex when the sensors operate in passive mode. In this research work, passive measurements i.e., bearings from an array mounted on the hull (HMA) and array sensors towed behind the observer along with doppler shifted frequency measurements from the towed array are fused to obtain kinematic parameters of the desired target. The sub-optimal filter, Unscented Kalman filter is considered for the performance analysis. The results are compared to that of simple doppler-bearing tracking using measurements from HMA. The simulation is carried in MATLAB software using the Monte-Carlo simulation method for better analysis of the results.

Research paper thumbnail of Acceptance Criteria of Bearings-only Passive Target Tracking Solution

IETE Journal of Research, 2021

Research paper thumbnail of Unscented Particle Filter Approach for Underwater Target Tracking

International Journal of e-Collaboration, 2021

This research aims to find an appropriate approach to improve system accuracy in the Doppler-bear... more This research aims to find an appropriate approach to improve system accuracy in the Doppler-bearing tracking (DBT) problem for target estimation. The topic of DBT problem is to achieve a target trajectory using bearing and frequency measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. The unscented particle filter approach is proposed to estimate the accuracy in the target motion parameters (TMP). This approach requires the observer maneuver so that the target trajectory is observable. Although in recent research papers, DBT has been proven to work efficiently without observer maneuver, TMP is unknown to the observer, and consequently, there is a need for observer maneuver. So, the algorithm is simulated with observer following s-maneuver and without any maneuver executed by the observer, and results are compared. The effectiveness of the solution and results are determined by using MATLAB simulation. It is shown that t...

Research paper thumbnail of Estimate-Merge-Technique-based algorithms to track an underwater moving target using towed array bearing-only measurements

Sādhanā

Bearing-only passive target tracking is a well-known underwater defence issue dealt in the recent... more Bearing-only passive target tracking is a well-known underwater defence issue dealt in the recent past with the conventional nonlinear estimators like extended Kalman filter (EKF) and unscented Kalman filter (UKF). It is being treated now-a-days with the derivatives of EKF, UKF and a highly sophisticated particle filter (PF). In this paper, two novel methods based on the Estimate Merge Technique are proposed. The Estimate Merge Technique involves a process of getting a final estimate by the fusion of a posteriori estimates given by different nonlinear estimates, which are in turn driven by the towed array bearing-only measurements. The fusion of the estimates is done with the weighted least squares estimator (WLSE). The two novel methods, one named as Pre-Merge UKF and the other Post-Merge UKF, differ in the way the feedback to the individual UKFs is applied. These novel methods have an advantage of less root mean square estimation error in position and velocity compared with the EKF and UKF and at the same time require much lesser number of computations than that of the PF, showing that these filters can serve as an optimal estimator. A testimony of the aforementioned advantages of the proposed novel methods is shown by carrying out Monte Carlo simulation in MATLAB R2009a for a typical war time scenario.

Research paper thumbnail of Robust adaptive unscented Kalman filter for attitude estimation of pico satellites

Unscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good estimation re... more Unscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good estimation results for the estimation problems of nonlinear systems even when high nonlinearity is in question. However, in case of system uncertainty or measurement malfunctions, the UKF becomes inaccurate and diverges by time. This study introduces a fault-tolerant attitude estimation algorithm for pico satellites. The algorithm uses a robust adaptive UKF, which performs correction for the process noise covariance (Q-adaptation) or measurement noise covariance (R-adaptation) depending on the type of the fault. By the use of a newly proposed adaptation scheme for the conventional UKF algorithm, the fault is detected and isolated, and the essential adaptation procedure is followed in accordance with the fault type. The proposed algorithm is tested as a part of the attitude estimation algorithm of a pico satellite. it with a time-dependent variable. One of the methods for constructing such algorithm is to use a scale factor as a multiplier to the process or measurement noise covariance matrices . This kind of gain correction-based algorithms can be used when the information about the dynamic or measurement process is absent . Hence, the adaptation against both the system uncertainty and the measurement malfunctions is possible.