Target tracking enhancement using a Kalman filter in the presence of interference (original) (raw)

Adaptive Extended Kalman Filter For Ballistic Missile Tracking

2017

In the current work, adaptive extended Kalman filter (AEKF) is presented for solution of ground radar based ballistic missile (BM) tracking problem in re-entry phase with unknown ballistic coefficient. The estimation of trajectory of any BM in re-entry phase is extremely difficult, because of highly non-linear motion of BM. The estimation accuracy of AEKF has been tested for a typical test target tracking problem adopted from literature. Further, the approach of AEKF is compared with extended Kalman filter (EKF). The simulation result indicates the superiority of the AEKF in solving joint parameter and state estimation problems.

An Efficient Approach for Target Tracking and Error Minimization by Kalman Filtering Technique

2007 International Conference on Electrical Engineering, 2007

Target tracking and size estimation have always been The only limitation with most of the proposed solutions for an active field of research because of its widely spread target tracking was their over-emphasis on over all dynamic applications in navigational, air defense and missile interception modeling throughout the period of scans irrespective of systems, etc. In this paper error-minimization and to increase hit dynamic changes in target's behaviors. However if target's probability has been focused keeping in view the limitations -on, l imposed by practical constraints. An efficient practical approach positi very aclrtintetcan be asured has been modeled and tested practically in this research work, apredcted fo ver sretiontals ofiteim and pose which tackles a maneuvering target and its dynamics in real time. approach used as the prediction and filtering algorithm, many

TARGET TRACKING: IMPLEMENTING THE KALMAN FILTER

Target tracking is often complicated by the measurement noise. The noise must be "filtered" out in order to predict the true path of a moving target. In this study of linear filtering, the Kalman filter, a recursive linear filtering model, was used to estimate tracks. Various situations were examined, including maneuvering targets, multiple radars, multiple targets, and collision avoidance. Based on the results, the Kalman filter was successful in smoothing random deviations from the true path of the targets, improving in its ability to predict the path of each target as more measurements from the tracker were processed.

Kalman filter based target tracking for track while scan data processing

2015 2nd International Conference on Electronics and Communication Systems (ICECS), 2015

The targets parameter to be measured for tracking are its relative position in range, azimuth angle, elevation angle and velocity. These parameters can be measured by tracking radar systems. Upon keeping the tracking of these measured parameters the tracker predict their future values. Fire control and missile guidance can be assisted through target tracking only. In fact missile guidance cannot be achieved without tracking the target properly. To predict target parameters (future samples) between scans, track while scan radar system sample each target once per scan interval by using sophisticated smoothing and prediction filters among which alpha-beta-gamma () and Kalman filters are commonly used. The principle of recursive tracking and prediction filters are proposed in this paper for two maneuvering targets (lazy and aggressive maneuvering), by implementing the second and third order one dimensional fixed gain polynomial filter trackers. Finally the equations for an n-dimensional multi state kalman filter are implemented and analyzed. In order to evaluate the performance of tracking filters the target considered in this paper is a Novator K100 Indian/Russian air-to-air missile designed to fly at Mach 4. In this paper the main objective of developing these filter tracking algorithmsis to reduce the measurement noise and tracking filter must be capable of tracking maneuvering targets with small residual (tracking errors).

Modeling of Target Tracking System for Homing Missiles and Air Defense Systems

One reason of why the guidance and control systems are imperfect is due to the dynamics of both the tracker and the missile, which appears as an error in the alignment with the LOS and delay in the response of the missile to change its orientation. Other reasons are the bias and disturbances as well as the noise about and within the system such as the thermal noise. This paper deals with the tracking system used in the homing guidance and air defense systems. A realistic model for the tracking system model is developed including the receiver servo dynamics and the possible disturbance and noise that may affect the accuracy of the tracking signals measured by the seeker sensor. Modeling the parameters variability and uncertainty is also examined to determine the robustness margin of the tracking system.

An Adaptive Cubature Kalman filter for Target Tracking

2022

Background and Objectives:The target tracking problem is an essential component of many engineering applications.The extended Kalman filter (EKF) is one of the most well-known suboptimal filter to solve target tracking. However, since EKF uses the first-order terms of the Taylor series nonlinear extension functions, it often makes large errors in the estimates of state. As a result, target tracking based on EKF may diverge. Methods: In this manuscript, an adaptive square root cubature Kalman filter (ASRCKF) is poposed to solve the maneuvering target tracking problem. In the proposed method, the covariance of process and measurement noises is estimated adaptively. Thus, the performance of proposed method does not depend on the noise statistics and its performance is robust with unknown prior knowledge of the noise statistics. Morover, it has a consistently improved numerical stability why the matrices of covariance are guaranteed to remain semi-positive. The performance of the proposed method is compared with EKF, and the unscented Kalman filter (UKF) for target tracking problem. Results:To evaluate the proposed method, many experiments is performed. The proposed method is evaluated on the non-maneuvering and maneuvering target tracking. Conclusion: The results show that the proposed method has lower estimation errors with faster convergence rate than other methods. The proposed method can track the tates of moving target effectively and improve the accuracy of the system.

Kalman filter design for target tracking

IEEE Transactions on Aerospace and Electronic Systems, 1980

The problem of solving the matrix Riccati differential equation in the design of Kalman filters for the target tracking problem is considered. An algebraic transformation method is used to reduce the order of the Riccati differential equation and to obtain explicit expressions for the filter gains (in terms of the interceptor/target separation range) which results in a substantial reduction of the computer burden involved in estimating the target states. The applicability of the transform technique is demonstrated for the receiver thermal noise and the target glint noise cases.

A Constant Gain Kalman Filter Approach to track Maneuvering Targets

Tracking of maneuvering targets is an important area of research with applications in both the military and civilian domains. One of the most fundamental and widely used approaches to target tracking is the Kalman filter. In presence of unknown noise statistics there are difficulties in the Kalman filter yielding acceptable results. In the Kalman filter operation for state variable models with near constant noise and system parameters, it is well known that after the initial transient the gain tends to a steady state value. Hence working directly with Kalman gains it is possible to obtain good tracking results dispensing with the use of the usual covariances. The present work applies an innovations based cost function minimization approach to the target tracking problem of maneuvering targets, in order to obtain the constant Kalman gain. Our numerical studies show that the constant gain Kalman filter gives good performance compared to the standard Kalman filter. This is a significant finding in that the constant gain Kalman filter circumvents or in other words trades the gains with the filter statistics which are more difficult to obtain. The problems associated with using a Kalman filter for tracking a maneuvering target with unknown system and measurement noise statistics can be circumvented by using the constant gain approach which seeks to work only with the gains instead of the state and measurement noise covariances. The approach is applied to a variety of standard maneuvering target models.