Estimate-Merge-Technique-based algorithms to track an underwater moving target using towed array bearing-only measurements (original) (raw)
Related papers
Data fusion for underwater target tracking
IET Radar, Sonar & Navigation, 2010
Underwater manoeuvring target rarely tracked using bearings-only measurements available from Hull mounted array (HA) without a proper manoeuvre by the observer. This problem is solved by administering data fusion techniques on bearings available from towed array and HA. Song and Speyer's and Galkowski and Islam's modified gain bearings only extended Kalman filter is exploited for estimation of target motion parameters. Online pre-processing is carried out to reduce the amplitude of the noise, compute the estimated bearings if the bearing measurement is not available and to find out variance of the noisy measurement which is used in Kalman filter. The spurious measurements are made invalid. The performance evaluation of the algorithms is done in Monte-Carlo simulation and results obtained for two typical geometries are presented.
IJERT-Underwater Bearings only Passive Target Tracking Using Pseudo Linear Estimator
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/underwater-bearings-only-passive-target-tracking-using-pseudo-linear-estimator https://www.ijert.org/research/underwater-bearings-only-passive-target-tracking-using-pseudo-linear-estimator-IJERTV2IS100024.pdf The passive target tracking using bearings-only measurements is studied for several underwater applications. For submarine (own ship) to submarine (target) application, Pseudo Linear Estimator and its variants are developed for various situations. The algorithm is extended to Electronic Surveillance Measures in Electronic Warfare / Intercept sonar target tracking application, where the measurements are highly aperiodic. In underwater, prior information of the target motion parameters will not be available. Therefore, Pseudo Linear Estimator is developed in such a way to work without initialization of target state vector. The pseudo measurement tracking filter manipulates the original nonlinear measurement algebraically to obtain the linear like structured measurement. The Pseudo Linear Estimator is projected in such a way that it does not require any initial estimate at all and at the same time offers all the features of extended Kalman filter based pseudo linear filter, namely sequential processing, flexibility to adopt the variance of each measurement.
Sensors, 2020
The paper presents an application of the Conditionally-Minimax Nonlinear Filtering (CMNF) algorithm to the online estimation of underwater vehicle movement given a combination of sonar and Doppler discrete-time noisy sensor observations. The proposed filter postulates recurrent “prediction–correction” form with some predefined basic prediction and correction terms, and then they are optimally fused. The CMNF estimates have the following advantageous features. First, the obtained estimates are unbiased. Second, the theoretical covariance matrix of CMNF errors meets the real values. Third, the CMNF algorithm gives a possibility to choose the preliminary observation transform, basic prediction, and correction functions in any specific case of the observation system to improve the estimate accuracy significantly. All the features of conditionally-minimax estimates are demonstrated by the regression example of random position estimate given the noisy bearing observations. The contributio...
International Journal of Engineering Sciences & Research Technology, 2013
Underwater, Passive Target tracking,for a moving observer, observation will be a critical task. Modified Gain Extended Kalman Filter (MGEKF) developed by Song and Speyer [3] was proven to be suitable algorithm for angles only passive target tracking applications in air. In this paper, this improved MGEKF algorithm is explored for underwater applications with some modifications. In underwater, the noise in the measurements is very high, turning rate of the platforms is low and speed of the platforms is also low when compared with the missiles in air. These characteristics of the platform are studied in detail and the algorithm is modified suitably for tracking applications in underwater. Monte-Carlo simulated results for one typical scenario is presented for the purpose of explanation. From the results it is observed that this algorithm is suitable for moving observer in underwater passive target tracking using angles only measurements.
2016
In under water an observer pre‐processes the noisy bearing measurements available from passive sonar and then the data are used by Kalman filter to find out target motion parameters. The pre‐processing reduces the amplitude of the noise, replaces the missed bearings with estimated bearings, supplies the estimated bearings if the bearing measurement is not available or incorrect and finally it finds out mean and variance of the noisy data. The statistical characteristics of the data are used in Kalman filter which finds out the target motion parameters. Online estimation of bearing measurement is carried out using Pseudo‐linear estimator. Finally, the whole algorithm is evaluated in Monte‐ Carlo simulation and the results for one typical scenario are presented.
IJERT-Under Water Active Target Tracking Using Kalman Filter
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/under-water-active-target-tracking-using-kalman-filter https://www.ijert.org/research/under-water-active-target-tracking-using-kalman-filter-IJERTV2IS100768.pdf Tracking is the process of finding the future position of the target. If this is done with the help of the available range and bearing measurements (from a SONAR)then the tracking is called the active tracking. The problem with the technique is ,the received measurements are contaminated by some noise. Based upon these measurements if we try to hit the target , definitely we are going to miss and since there will be no second chance in the war environment, it is essential to remove the noise present in the measurements. Here an attempt is made to solve the mentioned problem using kalman filter and the authors were successful. The implementation was done in MATLAB 7.8.0(R2009a) environment and simulation results are presented.
IJERT-Underwater passive target tracking from a stationary ied gain extended kalman filter IJERTV2IS
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/underwater-passive-target-tracking-from-a-stationary-observer-using-modified-gain-extended-kalman-filter https://www.ijert.org/research/underwater-passive-target-tracking-from-a-stationary-observer-using-modified-gain-extended-kalman-filter-IJERTV2IS90468.pdf Target tracking in underwater ,for a stationary observer, observability is less compared to moving observer. Modified Gain Extended Kalman Filter (MGEKF) developed by Song and Speyer [2] was proven to be suitable algorithm for angles only passive target tracking applications in air. In this paper, this improved MGEKF algorithm is explored for underwater applications with some modifications. In underwater, the noise in the measurements is very high, turning rate of the platforms is low and speed of the platforms is also low when compared with the missiles in air. These characteristics of the platform are studied in detail and the algorithm is modified suitably for tracking applications in underwater. MonteCarlo simulated results for one typical scenario is presented for the purpose of explanation. From the results it is observed that this algorithm is suitable for stationary observer in underwater passive target tracking using angles only measurements
Journal of Engineering and Technology, 2016
Objectives: Modified Gain Extended Kalman Filter (MGEKF) created by Song and Speyer [1] was turned out to be appropriate calculation for points just detached target following applications in air. Methods: As of late, roughly altered increases are displayed, which are numerically steady and exact [2]. In this paper, this enhanced MGEKF calculation is investigated for submerged applications with a few changes. Results: In submerged, the commotion in the estimations is high, turning rate of the stages is low and speed of the stages is likewise low when contrasted and the rockets in air. These attributes of the stage are concentrated on in detail and the calculation is adjusted appropriately to track applications in submerged. Conclusions: Monte-Carlo analysis comes about for two run of the mill situations are introduced with the end goal of clarification. From the outcomes it is watched that this calculation is especially reasonable for this nonlinear edges just detached target following.
Journal of Engineering and Technology, 2016
In our earlier work, data fusion with specific application to underwater tracking environment is explored. The target can be tracked using array bearings, while it is moving with constant velocity and maneuvering occasionally. In this paper, it is shown that if data fusion is carried out using the bearing measurements available from Towed Array (TA) along with hull mounted array‟s bearings, then tracking of a continuously moving target can be carried out easily. This algorithm is independent of ownship maneuver for the observability of the process . Song and Speyer's & Galkowski and Islam‟s modified gain algorithms are utilized with some modifications for estimation. Monte Carlo simulation is performed and results are shown for various typical geometries.