Detection of point targets becomes increasingly more difficult as targets become weak and engagement takes
place in highly dense, varying and complex background like clouds. To detect weak point targets in this
scenario, detection threshold should be sufficiently low. And this leads to high false alarm rate. In order to
make detection system robust to dense clutter (we mean 'clouds') and noise, post processing algorithms are
required. Almost all detection/tracking systems use post processing techniques, but less has been reported in
this area. In this paper, we propose a simple and computationally efficient post processing algorithm to
encounter false alarms due to dense and varying clouds. Models for target and cloud edges are presented.
Results demonstrate that proposed algorithm is able to reduce false alarms to a large extent.
KEYWORDS: Target detection, Signal to noise ratio, Infrared imaging, Detection and tracking algorithms, Image segmentation, Digital filtering, Computer simulations, Image filtering, Electronic filtering, Algorithm development
An integrated algorithm for the detection of dim point/extended size, slow/fast-moving targets has been presented in this
paper. In the proposed algorithm, essentially an innovation over an existing algorithm reported by Nengli Dong et al ,
morphological operations are carried out on the incoming IR data to improve signal to noise ratio (SNR). Methods of
entropy thresholding and conjunction functions are integrated together. Conjunction function based algorithm has been
significantly modified to take care of fast moving targets, a limitation of the method proposed by Nengli Dong et al. Our
proposed algorithm is able to detect point as well extended size targets with low contrast and having frame to frame
movements varying from sub-pixel to tens of pixels.
Inertial stabilization of electro-optical sighting systems and weapon slaving control loops are essential constituents of modern fire control systems for mobile combat vehicles. These systems are used for surveillance, target tracking and engaging the targets under dynamic conditions. Firing accuracy of such systems largely depends on stabilization and weapon slaving accuracies. Accuracy requirements become stringent as the operating range increases. Several other issues such as bore sighting offsets, ballistic offsets and mounting error compensation etc. are also to be considered.
Fuzzy knowledge based controller (FKBC) offers an alternative method to the conventional control synthesis methodologies using root locus, Bode plots or pole placement. Fuzzy control loops are particularly useful when the plant consists of substantial non-linearity due to actuator saturation, stiction, Coulomb friction, digitization etc. Since, the control surface obtained through this method is
non-linear, generally it provides greater flexibility to designer to achieve better damping, lesser control energy even in presence of various constraints.
This work presents the design of weapon slaving loop using a fuzzy controller. The weapon is slaved to a gimbaled electro-optical sight, which has a stabilized line of sight along two axes. The system under consideration is designed for naval platforms. A
two-input (error and rate of change of error) and single output (incremental control) fuzzy controller has been designed to position the weapon at desired position. Implementation of controller has been done using digitized inputs.
Simulations have been carried out to evaluate the performance of the integrated fire control system under the presence of various
non-linearities, sensor inaccuracies and other exogenous inputs like host platform generated disturbances and measurement noise. Stringent requirements of disturbance attenuation, tracking and command following have been met.
KEYWORDS: Fuzzy logic, Line of sight stabilization, Control systems, Picosecond phenomena, Device simulation, Signal attenuation, Electromechanical design, Optical engineering, Analog electronics, Gyroscopes
Line-of-sight (LOS) stabilization systems form part of modern surveillance and fire control systems (FCSs). Conventional controller designs are dependent on the accuracy of the mathematical model of the plant, which usually ignore high order dynamics. Further, plants are linearized around the operating point. Although robust controllers can be designed to overcome uncertainties in plant parameters as well as nonlinearities, the resultant controller becomes complex for implementation. Fuzzy-knowledge-based controller (FKBC) design presents a good methodology to overcome above difficulties. A fuzzy control system is implemented to control inertial rate of LOS. We present the design and implementation aspects of this particular FKBC and the performance achieved with respect to the performance achieved using a conventional controller.