Abstract: Electro-optical (EO) stabilized platforms have been widely used for surveillance and reconnaissance on various types of vehicles, from surface ships to unmanned air vehicles (UAVs). EO stabilized platforms usually consist of an assembly of structure, bearings, and motors called gimbals in which a gyroscope is installed. EO elements such as a CCD camera and IR camera, are mounted to a gimbal, which has a range of motion in elevation and azimuth and can designate and track a target. In addition, a laser range finder (LRF) can be added to the gimbal in order to acquire the precise slant range from the platform to the target. Recently, a versatile functionality of target localization is needed in order to cooperate with the weapon systems that are mounted on the same platform. The target information, such as its location or velocity, needed to be more accurate. The accuracy of the target information depends on diverse component errors and alignment errors of each component. Specially, the type of moving platform can affect the accuracy of the target information. In the case of flying platforms, or UAVs, the target location error can be increased with altitude so it is important to measure altitude as precisely as possible. In the case of surface ships, target location error can be increased with obliqueness of the elevation angle of the gimbal since the altitude of the EO stabilized platform is supposed to be relatively low. The farther the slant ranges from the surface ship to the target, the more extreme the obliqueness of the elevation angle. This can hamper the precise acquisition of the target information. So far, there have been many studies on EO stabilized platforms of flying vehicles. However, few researchers have focused on ship-borne EO stabilized platforms of the surface ship. In this paper, we deal with a target localization method when an EO stabilized platform is located on the mast of a surface ship. Especially, we need to overcome the limitation caused by the obliqueness of the elevation angle of the gimbal. We introduce a well-known approach for target localization using Unscented Kalman Filter (UKF) and present the problem definition showing the above-mentioned limitation. Finally, we want to show the effectiveness of the approach that will be demonstrated through computer simulations.
Abstract: A model to identify the lifetime of target tracking
wireless sensor network is proposed. The model is a static clusterbased
architecture and aims to provide two factors. First, it is to
increase the lifetime of target tracking wireless sensor network.
Secondly, it is to enable good localization result with low energy
consumption for each sensor in the network. The model consists of
heterogeneous sensors and each sensing member node in a cluster
uses two operation modes–active mode and sleep mode. The
performance results illustrate that the proposed architecture consumes
less energy and increases lifetime than centralized and dynamic
clustering architectures, for target tracking sensor network.
Abstract: In recent years a number of applications with multirobot
systems (MRS) is growing in various areas. But their design
is in practice often difficult and algorithms are proposed for the
theoretical background and do not consider errors and noise in real
conditions, so they are not usable in real environment. These errors
are visible also in task of target localization enough, when robots
try to find and estimate the position of the target by the sensors.
Localization of target is possible also with one robot but as it was
examined target finding and localization with group of mobile robots
can estimate the target position more accurately and faster. The
accuracy of target position estimation is made by cooperation of
MRS and particle filtering. Advantage of usage the MRS with particle
filtering was tested on task of fixed target localization by group of
mobile robots.
Abstract: An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram showed that over 88% of normal mammograms and 80% of abnormal mammograms can be correctly identified. The approach was also successfully applied to Synthetic Aperture Radar (SAR) and Ground Penetrating Radar (GPR) images for target detection.
Abstract: Target tracking and localization are important applications
in wireless sensor networks. In these applications, sensor nodes
collectively monitor and track the movement of a target. They have
limited energy supplied by batteries, so energy efficiency is essential
for sensor networks. Most existing target tracking protocols need to
wake up sensors periodically to perform tracking. Some unnecessary
energy waste is thus introduced. In this paper, an energy efficient
protocol for target localization is proposed. In order to preserve
energy, the protocol fixes the number of sensors for target tracking,
but it retains the quality of target localization in an acceptable
level. By selecting a set of sensors for target localization, the other
sensors can sleep rather than periodically wake up to track the target.
Simulation results show that the proposed protocol saves a significant
amount of energy and also prolongs the network lifetime.