Modelling and Simulation of Multi-target Multi-sensor Data Fusion for Trajectory Tracking

An implementation of track fusion using various algorthims has been demonstrated . The sensor measurements of these targets are modelled using Kalman filter (KF) and interacting multiple models (IMM) filter. The joint probabilistic data association filter (JPDAF) and neural network fusion (NNF) algorithms were used for tracking multiple man-euvring targets. Track association and fusion algorithm are executed to get the fused track data for various scenarios, two sensors tracking a single target to three sensors tracking three targets, to evaluate the effects of multiple and dispersed sensors for single target, two targets, and multiple targets. The targets chosen were distantly spaced, closely spaced and crossing. Performance of different filters was compared and fused trajectory is found to be closer to the true target trajectory as compared to that for any of the sensor measurements of that target.Defence Science Journal, 2009, 59(3), pp.205-214, DOI:http://dx.doi.org/10.14429/dsj.59.151

A new interacting multiple model particle filter based ballistic missile tracking method

This paper proposes a new method for tracking the whole trajectory of a ballistic missile from launch to impact on the ground. Multiple state models are applied for the ballistic missile movement descriptions during different phases, while the transition probabilities are modelled in a state-dependent way. A radar sensor is applied to obtain the missile range, azimuth angle and elevation angle measurements. Based on the state models and measurements, an interacting multiple model based particle filter method is applied for tracking. Simulation studies show that the proposed method outperforms the widely-applied extended Kalman filtering based interacting multiple model for tracking the ballistic missile

A FAULT TOLERANT, DATA FUSION SYSTEM FOR NAVIGATION APPLICATIONS TO A DUCTED FAN VTOL UAV

A Fault Tolerant, Data Fusion (FTDF) algorithm for a Ducted Fan Unmanned Aerial Vehicle (DFUAV) Navigation System is presented. The algorithm have two parts: Gradient Descent (GD) for the Attitude and Heading Reference System (AHRS) and an Interacting Multiple Model (IMM) for position estimation. The GD methodology was designed to fuse the gyroscope, accelerometer, and geomagnetic sensors. The IMM algorithm is able to identify and compensate for multiple sensors data failures. There are three parts in the presentation. Firstly, system identification and the Allan Variance method is used to build dynamic models and noise models for multiple Sensors and Actuators. Secondly, a GD filter is developed for application to the Inertial Measurement Unit (IMU) consisting of tri-axis gyroscopes, accelerometers and magnetometers. The GD filter implementation incorporates magnetic distortion and gyroscope bias drift compensation. The filter uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimized algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. . Finally, the IMM algorithm is used to combine data from multiple sensors simultaneously. This filter uses multiple models that incorporate sensor failures. The probabilities of these models being correct is generated by the IMM. These probabilities can be used to identify sensor failures and compensate for these failures

Implementation of IMMPDAF Algorithm in LabVIEW for Multi Sensor Single Target Tracking

Real time IMMPDAF algorithm has been implemented and tested in LabVIEW. Single aircraft flight profiles have been simulated and the plot data from multiple radars observing the single aircraft are generated with noise as well as clutter. The performance of the algorithm is evaluated using standard procedures. Since it is implemented and tested in LabVIEW, this algorithm can be easily realized in hardware for real time tracking applications

Dynamic sensor tasking and IMM EKF estimation for tracking impulsively maneuvering satellites

In order to efficiently maintain space situational awareness, care must be taken to optimally allocate expensive observation resources. In most situations the available sensors capable of tracking spacecraft have their time split between many different monitoring responsibilities. Tracking maneuvering spacecraft can be especially difficult as the schedule of maneuvers may not be known and will often throw off previous orbital models. Effectively solving this tasking problem is an ongoing focus of research in the area of space situational awareness. Most methods of automated tasking do not make use of interacting multiple model extended Kalman filter techniques to better track satellites during maneuvers. This paper proposes a modification to a Fisher information gain and estimated state covariance based sensor tasking method to take maneuver probability and multiple model dynamics into account. By incorporating the probabilistic maneuvering model, sensor tasking can be improved during satellite maneuvers using constrained resources. The proposed methods are verified through the use of numerical simulations with multiple maneuvering satellites and both orbital and ground-based sensors

Multi-Object Tracking with Interacting Vehicles and Road Map Information

In many applications, tracking of multiple objects is crucial for a perception of the current environment. Most of the present multi-object tracking algorithms assume that objects move independently regarding other dynamic objects as well as the static environment. Since in many traffic situations objects interact with each other and in addition there are restrictions due to drivable areas, the assumption of an independent object motion is not fulfilled. This paper proposes an approach adapting a multi-object tracking system to model interaction between vehicles, and the current road geometry. Therefore, the prediction step of a Labeled Multi-Bernoulli filter is extended to facilitate modeling interaction between objects using the Intelligent Driver Model. Furthermore, to consider road map information, an approximation of a highly precise road map is used. The results show that in scenarios where the assumption of a standard motion model is violated, the tracking system adapted with the proposed method achieves higher accuracy and robustness in its track estimations

Improved data association and occlusion handling for vision-based people tracking by mobile robots

This paper presents an approach for tracking multiple persons using a combination of colour and thermal vision sensors on a mobile robot. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is then incorporated into the tracker