Comparative Analysis of Non Linear Estimation Schemes used for Undersea Sonar Applications

    The performance evaluation of various passive underwater target tracking algorithms like Pseudo Linear Estimator, Maximum Likelihood Estimator, Modified Gain Bearings-only Extended Kalman Filter, Unscented Kalman Filter, Parameterized Modified Gain Bearings-only Extended Kalman Filter and Particle Filter coupled with Modified Gain Bearings-only Extended Kalman Filter using bearings-only measurements is carried out with various scenarios in Monte Carlo Simulation. The performance of Parameterized Modified Gain Bearings-only Extended Kalman Filter is found to be better than all estimates

INVESTIGATION OF ADVANCED NON-LINEAR CONTROL AND ESTIMATION ALGORITHM FOR ROCKET BASED APPLICATIONS

Online Object tracking is an important task in radar and sonar signal processing As it  is a challenging problem due to the presence of noise, and dynamic changes. a variety of Stochastic algorithms for tracking targets have been proposed and implemented to reach  these challenges. Approaches towards   highly nonlinear  applications  is an   advanced   task . In this paper, we devote the effort to use the Particle Filtering with estimation of various states of a vehicle launched from an idealized spherical, airless, non-rotating earth to improve tracking efficiency. The simulation results show that the PF improved the tracking performance compared to the Kalman based Filters (EKF, UKF) for the rocket launch application

Tracking algorithms for multistatic sonar systems

Abstract Activated reconnaissance systems based on target illumination are of high importance for surveillance tasks where targets are nonemitting. Multistatic configurations, where multiple illuminators and multiple receivers are located separately, are of particular interest. The fusion of measurements is a prerequisite for extracting and maintaining target tracks. The inherent ambiguity of the data makes the use of adequate algorithms, such as multiple hypothesis tracking, inevitable. For their design, the understanding of the residual clutter, the sensor resolution and the characteristic impact of the propagation medium is important. This leads to precise sensor models, which are able to determine the performance of the surveillance team. Incorporating these models in multihypothesis tracking leads to a situationally aware data fusion and tracking algorithm. Various implementations of this algorithm are evaluated with the help of simulated and measured data sets. Incorporating model knowledge leads to increased performance, but only if the model is in line with the physical reality: we need to find a compromise between refined and robust tracking models. Furthermore, to implement the model, which is inherently nonlinear for multistatic sonar, approximations have to be made. When engineering the multistatic tracking system, sensitivity studies help to tune model assumptions and approximations

Neural network modeling of the dynamics of autonomous underwater vehicles for Kalman filtering and improved localization

Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles are used for a variety of underwater operations and deep-sea explorations. One of the major challenges faced by these vehicles is localization i.e., the ability of these vehicles to identify their location with respect to a reference point. The kinematic Extended Kalman filters have been used in localization in a method known as dead reckoning. The accuracy of the localization systems can be improved if a dynamic model is used instead of the kinematic model. The previously derived dynamic model was implemented in real time in UUVSim, a simulation environment. The dynamic model was tested against the kinematic model on various test courses and it was found that the dynamic model was more stable and accurate than the kinematic model. One of the major drawbacks of the dynamic model was that it required the use of numerous coefficients. The process of determining these coefficients was extensive, requiring significant experimentation time. This research explores the use of a Neural Network architecture to replace these dynamic equations. Initial experiments have showed promising results for the Neural Network although modifications will be required before the controller can be made universally applicable

SUBMARINE TATICAL GEOMETRIES DURING ENEMY VEHICLE ATTACK USING NOVEL STATISTICAL STOCHASTIC NON LINEAR FILTER

Particle filter is proposed for tracking a torpedo using bearings-only measurements when torpedo is attacking an ownship. Towed array is used to generate torpedo bearing measurements. Ownship evasive maneuver is used for observability of the bearings-only process. Particle filter combined with Modified Gain Bearings-Only Extended Kalman Filter is used to estimate torpedo motion parameters, which are used to calculate optimum ownship evasive maneuver. Monte-Carlo simulation is carried out and the results are presented for typical scenarios

Application of Sigma Point Particle Filter Method for Passive State Estimation in Underwater

Bearings-only tracking (BOT) plays a vital role in underwater surveillance. In BOT, measurement is tangentially related to state of the system. This measurement is also corrupted with noise due to turbulent underwater environment. Hence state estimation process using BOT becomes nonlinear. This necessitates the use of nonlinear filtering algorithms in place of traditional linear filters like Kalman filter. In general, these nonlinear filters utilize the assumption of measurements being corrupted with Gaussian noise for state estimation. The measurements cannot be always corrupted with Gaussian noise because of the highly unstable sea environment. These problems indicate the necessity for development of nonlinear non-Gaussian filters like particle filter (PF) for underwater tracking. However, PF suffers from severe problems like sample degeneracy and impoverishment and also it is tedious to select an appropriate technique for resampling. To overcome these difficulties in PF implementation, the strategy of combining PF with another filter like unscented Kalman filter is proposed for target’s state estimation. The detailed analysis of the same is presented in comparison with other particle filter combinations using the simulation results obtained in Matlab

OPTIMAL RECURSIVE DATA PROCESSING ALGORITHM USING BAYESIAN INFERENCE FOR UNDERWATER VEHICLE LOCALISATION AND NAVIGATION SYSTEMS

In the ocean environment, two dimensional Range & Bearings target motion analysis (TMA) is generally used. In the underwater scenario, the active sonar, positioned on a observer, is capable of sensing the sound waves reflected from the target in water. The sonar sensors in the water pick up the target reflected signal in the active mode. The observer is assumed to be moving in straight line and the target is assumed to be moving mostly in straight line with maneuver occasionally. The observer processes the measurements and estimates the target motion parameters, viz., Range, Bearing, Course and Speed of the target. It also generates the validity of each of these parameters. Here we try to apply Kalman Filter for the sea scenario using the input estimation technique to detect target maneuver, estimate target acceleration and correct the target state vector accordingly.              There are mainly two versions of Kalman Filter – a linearised Kalman Filter (LKF) in which polar measurements are converted into Cartesian coordinates and the well-known Extended Kalman Filter (EKF). Recently S. T. Pork and L. E. Lee presented a detailed theoretical comparative study of the above two methods and stated that both the methods perform well. Here, EKF is used through out

AUV SLAM and experiments using a mechanical scanning forward-looking sonar

Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods