Underwater Passive Target Tracking Research Articles

Evaluation of DB-IEKF Algorithm Using Optimization Methods for Underwater Passive Target Tracking

Evaluation of DB-IEKF Algorithm Using Optimization Methods for Underwater Passive Target Tracking

Passive Sonar Target Tracking Based on Deep Learning

At present, the tracking accuracy of underwater passive target tracking is often limited due to models that are overly simple, with low complexity, poor universality, and an inability to learn. In this paper, a cubature Kalman filter (CKF) algorithm based on a gated recurrent unit (GRU) network is proposed. The filter innovation, prediction error, and filter gain obtained from the CKF are used as the input to the GRU network, and the filter error value is used as the output to train the network. End-to-end online learning is carried out using the designed fully connected network, and the current state of the target is predicted. In this paper, a deep neural network based on the GRU architecture is used to convert the tracking prediction problem into a time series prediction problem in the field of artificial intelligence, and its strong fitting ability is used to resolve the uncertainty of the target motion. Simulation results show that an unmanned underwater vehicle (UUV) state estimation method based on the GRU filter proposed in this paper offers better accuracy and stability than the traditional state estimation method.

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

PurposeMarine 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/approachThis 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).FindingsSHRF 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/valueThe 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.

Pervasive underwater passive target tracking for the computation of standard deviation solution in a 3D environment

PurposeNowadays advancement in acoustic technology can be explored with marine assets. The purpose of the paper is pervasive computing underwater target tracking has aroused military and civilian interest as a key component of ocean exploration. While many pervasive techniques are currently found in the literature, there is little published research on the effectiveness of these paradigms in the target tracking context.Design/methodology/approachThe unscented Kalman filter (UKF) provides good results for bearing and elevation angles only tracking. Detailed methodology and mathematical modeling are carried out and used to analyze the performance of the filter based on the Monte Carlo simulation.FindingsDue to the intricacy of maritime surroundings, tracking underwater targets using acoustic signals, without knowing the range parameter is difficult. The intention is to find out the solution in terms of standard deviation in a three-dimensional (3D) space.Originality/valueA new method is found for the acceptance criteria for range, course, speed and pitch based on the standard deviation for bearing and elevation 3D target tracking using the unscented Kalman filter covariance matrix. In the Monte Carlo simulation, several scenarios are used and the results are shown.

Performance Analysis of Bayesian Filtering and Smoothing Algorithms for Underwater Passive Target Tracking

Passive target tracking deals with nonlinear filtering in which dynamics of the system are considered to be linear, while the target state is built on nonlinear measurements. In this paper, a comparative study is conducted for accurate state estimation of an underwater far-field moving target by exploiting the strength of well known nonlinear variant of Bayesian filter, i.e., extended Kalman filter (EKF) with discrete-time Kalman smoother, called Rauch–Tung–Striebel (RTS) smoother. Analysis is performed with two key parameters in target tracking by mean of variation in the number of sensors and different standard deviations of measured noise in the context of underwater bearings-only tracking technology. Exhaustive experiments are performed for finding the least root-mean square error between true and estimated position of target movement in the trajectory at every time instant. Relatively accurate estimation of the target state is observed from noisy measurements of sensors in case of RTS smoother than that of EKF for each scenario.

Performance Analysis of Gaussian Optimal Filtering for Underwater Passive Target Tracking

The problem of passive target tracking in the underwater environment is usually handled with nonlinear filtering algorithms, in which nonlinear measurement model is combined with linear system dynamics. The primary goal in passive target tracking is to extract accurate information about real-time state of the target from noisy nonlinear observations obtained from sensors. In this study, performance analysis of Gaussian optimal filtering is proposed for accurate state prediction of an underwater dynamic object. This paper delicately analyzes the state estimation performances of nonlinear version of Kalman filter like Gauss Hermite Kalman Filter (GHKF) and discrete-time Kalman smoother, called Gauss Hermite Rauch-Tung-Striebel (GHRTS) smoother. This analysis is done through variation in standard deviation of white Gaussian measurement noise which is a key feature in the target tracking framework. This performance-based study is conducted in the context of Bearings Only Tracking (BOT) phenomena by using two and three acoustics sensors installed on observer base station. All the experiments are performed for finding the Root Mean Square Error (RMSE) among true and predicted state of the object. Independent Monte Carlo simulations based numerical results demonstrate that GHRTS smoother provides better performance from GHKF for given circumstances.

Modified Polar Extended Kalman Filter (MP-EKF) for Bearings - Only Target Tracking

Background/Objectives: Surveillance is most crucial part of maritime environment. The target needs to be tracked within shortest possible time with low complexity and computational cost. Methods/Statistical Analysis: Modified Polar Extended Kalman Filter is well suited for bearing only target tracking. In this paper a mathematical modelling and Monte Carlo simulation has been carried out. Findings: It is found out that MPEKF effectively tracks the underwater target. Therefore, it is suitable estimation algorithm for bearings-only underwater passive target tracking.

Application of Cubature Kalman Filter for Bearingsonly Target Tracking

Background/Objectives: The objective of this paper is to develop a novel estimation algorithm based target tracking simulator for underwater target tracking applications. Methods/Statistical Analysis: An own ship observes corrupted sonar bearings from a radiating target and finds out Target Motion Parameters (TMP) - viz., range, course, bearing and speed of the target. The issue is inherently nonlinear as the bearing measurement is non-linearly related to the target state. CKF is a new nonlinear filter for state estimation. The modeling of target state and measurement vectors is carried out. CKF is integrated into the model to result in evolution of simulator. Extensive performance evaluation of CKF with respect to bearings-only target tracking problem in Monte-Carlo simulation is carried out and the results are presented. Findings: CKF depends on spherical-radial cubature rule that makes it potential to numerically figure variable moment integrals encountered within the nonlinear filter. The underwater passive target tracking following Cubature Kalman filter is explored in this paper. Application/Improvements: The results obtained are satisfactory and UKF can be used in futuristic submarines in Indian Navy owing to its advantages as envisaged in this paper.

Underwater Passive Target Tracking in Constrained Environment

In underwater scenario, observer manoeuvre is required to find out target motion parameters in bearings only passive target tracking. Sometimes, due to tactical constraints, observer is not able to carry out manoeuvre. In this paper, it is shown that target motion parameters can be obtained in such situation, if the knowledge of any one of the target motion parameters is available. There are other practical problems like spurious bearings are generated by sonar, etc. In addition, auto tracking fails often and some bearings will be missed. Pseudo Linear Kalman Filter is made flexible to address these practical problems.

Observability analysis of two closed-loop guidance systems with bearings-only measurements

Utilizing only bearing angle measurement for guidance and target tracking is common in homing missiles with passive sensors and in underwater passive target tracking. Their control and estimation problems are theoretically interesting and practically signi cant. These problems are nontrivial due to the time-varying system dynamics, the nonlinear measurements and the complex target-pursuit geometry when the target is maneuverable. When considering the homing missile guidance with bearings-only measurements, the proportional navigation guidance is the most popular [11,19]. Unfortunately, the closed loop may demonstrate unsatisfactory performance when the missile and the target are in a collision course near the end of interception [17,6]. The problem is caused by the lack of observability in the range and the range rate. This poses considerable di culty as these two state variables are needed to realize the guidance law, and they are estimated by a state lter. It follows that an e ective guidance law should seek to achieve not only terminal accuracy, but also information content enhancement of essential measurements, in order to o er the lter su cient information to generate consistent estimates. Numerous studies dealing with observability-enhanced guidance control laws in homing missiles were conducted during the past decades [17,6,1,8,9]. One approach [8,9] is to formulate the guidance as an optimal quadratic control problem and to