| || Handling Out-of-Sequence Data: Kalman Filter Methods or Statistical Imputation?
Author : Twala, Bhekisipho
Source : Defence Science Journal ; Vol:60(1) ; 2010 ; pp 087-099
Subject : 681.3 Computer Science;Defence Science Journal
Keywords : Multi-Sensor Data;Time Delayed Measurements;Out-of-Sequence Measurements;Kalman Filter;Fusion;Imputation;Multi-Sensor Tracking
Abstract : The issue of handling sensor measurements data over single and multiple lag delays also known as outof- sequence measurement (OOSM) has been considered. It is argued that this problem can also be addressed using model-based imputation strategies and their application in comparison to Kalman filter (KF)-based approaches for a multi-sensor tracking prediction problem has also been demonstrated. The effectiveness of two model-based imputation procedures against five OOSM methods was investigated in Monte Carlo simulation experiments. The delayed measurements were either incorporated (or fused) at the time these were finally available (using OOSM methods) or imputed in a random way with higher probability of delays for multiple lags and lower probability of delays for a single lag (using single or multiple imputation). For single lag, estimates of target tracking computed from the observed data and those based on a data set in which the delayed measurements were imputed were equally unbiased; however, the KF estimates obtained using the Bayesian framework (BF-KF) were more precise. When the measurements were delayed in a multiple lag fashion, there were significant differences in bias or precision between multiple imputation (MI) and OOSM methods, with the former exhibiting a superior performance at nearly all levels of probability of measurement delay and range of manoeuvring indices. Researchers working on sensor data are encouraged to take advantage of software to implement delayed measurements using MI, as estimates of tracking are more precise and less biased in the presence of delayed multi-sensor data than those derived from an observed data analysis approach.
| || Target acceleration estimation from radar position data using neural network
Author : Sarkar, A. K.;Vathsal, S.;Sundaram, Suresh;Mukhopadhay, S.
Source : Defence Science Journal ; Vol:55(3) ; 2005 ; pp 313-328
Subject : 629.7 Aeronautics;629.76 Rockets and Missiles ;629.762 Missiles
Keywords : Kalman filter;Artificial neural network;Line of sight ;Feedforward neural network;Target acceleration estimation;Augmented proportional navigation Kalman filter
Abstract : This work is a preliminary investigation on target manoeuvre estimation in real-time from the available measurements of noisy position data from tracking radar using an artificial neural network (ANN. Recently, simulation study of target manoeuvre estimation in real-time from the same position alone measurement using extended Kalman filter has been carried out in a simulated environment using measurements at 100 ms interval. The results reveal that the estimated acceleration consists of substantial error and lag, which is a stumbling block for guidance accuracy in real-time. So, the target acceleration has been estimated using the ANN with less error and lag than the same using Kalman estimator.
| || Sensor/Control Surface Fault Detection and Reconfiguration using Fuzzy Logic
Author : Savanur, Shobha R.;Patel, Ambalal V.
Source : Defence Science Journal ; Vol:60(1) ; 2010 ; pp 076-086
Subject : 681.586 Sensors;Defence Science Journal
Keywords : Fuzzy logic;SFDIR;sensor fault detection isolation and reconfiguration;fault reconfiguration;sensor fault detection;control surface fault detection;Kalman filter
Abstract : In the aircraft flight control systems, a quick detection of the faults, that occur in actuators, control surfaces or sensors, is necessary. In this paper, sensor fault detection and reconfiguration is performed using Kalman filter by estimating the states of the plant and comparing them with respective measured values from the sensors. Sensor fault detection and reconfiguration is carried out using non-model-based fuzzy logic technique. Control surface fault detection and reconfiguration is carried out by identifying the elements of control distribution matrix using extended Kalman filter and fuzzy logic. In estimating the factor of effectiveness of the control surface using fuzzy logic, different implication methods such as Mamadanis minimum, Larsens product, bounded product and drastic product have been used and a comparison is made.
| || Range Safety Real-time System for Satellite Launch Vehicle Missions: Testing Methodologies
Author : Varaprasad, R.;Seshagiri Rao, V.
Source : Defence Science Journal ; Vol:56(5) ; 2006 ; pp 693-700
Subject : 629.7 Aeronautics;533.6 Aerodynamics
Keywords : Real-time system;Range safety;Inertial navigation system;Polynomial filter;Kalman filter;Testing;Network simulations
Abstract : A real-time system plays a critical role in the range safety decision-making in a satellite launch mission. Real-time software, the heart of such systems, is becoming an issue of criticality. Emphasis is being laid on the development of reliable, robust, and operational system. This paper purports to delineate prudent testing methodologies implemented to test the real-time system.
| || Determination of Moving Tank and Missile Impact Forces on a Bridge Structure
Author : Chen Tsung-Chien;Lee, Ming-Hui
Source : Defence Science Journal ; Vol:58(6) ; 2008 ; pp 752-761
Subject : 623 Military Science and Engineering
Keywords : Missile impact;Kalman filter;RLSE;Recursive least square estimator;Bridge structure;Impact studies;Impact forces
Abstract : A method to determine the moving tank and missile impact forces on a bridge is developed. The present method is an online adaptive recursive inverse algorithm, which is composed of the Kalman filter and the recursive least square estimator (RLSE), to estimate the force inputs on the bridge structure. The state equations of the bridge structure were constructed by using the model superposition and orthogonal technique. By adopting this inverse method, the moving tank and missile impact force inputs acting on the bridge structure system can be estimated from the measured dynamic responses. Besides, this work presents an efficient weighting factor applied in the RLSE, which is capable of providing a reasonable estimation results. The results obtained from the simulations show that the method is effective in determining the moving tank and missile impact forces, so that the acceptable results can be obtained.
| || Implementation of image registration algorithms for real time target tracking through video sequences
Author : Majumdar, Jharna ;Dilip, Y.
Source : Defence Science Journal ; Vol:52(3) ; 2002 ; pp 227-242
Subject : 621.396.9 Radars
Keywords : Unmanned aerial vehicles;Image registration ;Real time target tracking ;Target occlsion ;Kalman filter
Abstract : "Automatic detection and tracking of interesting targets from a sequence of images obtained from a reconnaissance platform is an interesting area of research for defence-related applications. Image registration is the basic step used in target tracking application. The paper briefly reviews some of the image registration algorithms, analyse their performance using a suitable image processing hardware, and selects the most suitable algorithm for a real-time target tracking application using cubic-spline model and spline model Kalman filter for the prediction of an occluded target. The algorithms developed are implemented in a ground-based image exploitation system (GIES) developed at the Aeronautical Development Establishment for unmanned aerial vehicle application, and the results presented for the images obtained during actual flight trial. "
| || Fusion of Radar and IRST Sensor Measurements for 3D Target Tracking using Extended Kalman Filter
Author : Naidu, V.P.S.
Source : Defence Science Journal ; Vol:59(2) ; 2009 ; pp 175-182
Subject : 621.396.9 Radars;681.586 Sensors
Keywords : Sensor;IRST sensor measurements;3-D target tracking;Kalman filter;Tracking algorithm;Performance evaluation;Fusion schemes;Target tracking
Abstract : Tracking algorithms for IRST and radar are implemented and their performance is checked with simulated data. Detailed mathematical expressions given could be useful for implementation. Performance evaluation metrics have been presented to check the tracking algorithm performance. Two fusion schemes have been presented and their performances evaluated with simulated data. It is concluded that both fusion schemes performed alike with the second fusion scheme giving slightly better results. From the results, it is also concluded that fusion of IRST and radar would improve the tracking performance and reduce the positional uncertainty compared to individual trackers.
| || Error Model-converted Measurement and Error Model-modified Extended Kalman Filters for Target Tracking
Author : Kashyap, Sudesh K.;Girija, G.;Raol, J. R.
Source : Defence Science Journal ; Vol:56(5) ; 2006 ; pp 679-692
Subject : 629.7 Aeronautics;533.6 Aerodynamics
Keywords : Polar measurements;Target tracking;Radar measurements;Filter scheme;ECMKF;CMKF;EMEKF;Algorithms;Kalman filter;Target algorithms;Cartesian coordinate frames;Polar frame
Abstract : Two-filter schemes have been evaluated to handle the polar measurements using error model (for bias correction and measurement noise covariance computation) for target-tracking application. It is assumed that a good reference source of target information is available. Schemes based on error model converted measurement Kalman filter (ECMKF) and error model modified extended-Kalman filter (EMEKF) algorithms are presented. Also some comparison with CMKF (debiased) is given. It is inferred that EMEKF gives better performance compared to other filters. Features of CMKF (debiased), ECMKF, and EMEKF are highlighted. Also the sensitivity study on the performance of EMEKF is carried out wrt to processing order of radar measurement channels.
| || Blast Load Input Estimation of the Medium Girder Bridge using Inverse Method
Author : Lee, Ming-Hui;Chen, Tsung-Chien
Source : Defence Science Journal ; Vol:58(1) ; 2008 ; pp 46-56
Subject : 623.4 Armaments and Ballistics;620.261 Explosives
Keywords : Input estimation;Kalman filter;Blast loads;Medium girder bridge;Truss structure system;Recursive least square estimator;RLSE;Finite element method
Abstract : Innovative adaptive weighted input estimation inverse methodology for estimating the unknown time-varying blast loads on the truss structure system is presented. This method is based on the Kalman filter and the recursive least square estimator (RLSE). The filter models the system dynamics in a linear set of state equations. The state equations of the truss structure are constructed using the finite element method. The input blast loads of the truss structure system are inverse estimated from the system responses measured at two distinct nodes. This work presents an efficient weighting factor g applied in the RLSE, which is capable of providing a reasonable estimation results. The results obtained from the simulations show that the method is effective in estimating input blast loads, so has great stability and precision.