Airborne Multi-sensor System Research Articles

Indian AEW&C System in the Global Perspective

An Airborne Early Warning and Control (AEW&C) System is an airborne multi-sensor system designed to detect Air-and Surface-Targets. Used at a high altitude, the system allows the operators to distinguish between friendly and hostile aircraft hundreds of kilometres away. The AEW&C aircraft is used for defensive and offensive air operations. The system is used offensively to direct fighters to locations of their targets and defensively to counter attacks. It can also be used to carry out surveillance, command and control, and battle management functions. The paper traces the evolution of the AEW&C System in the global scenario and compares the Indian effort to tailor-make an optimal state-of-the-art system to meet specific requirements of the Indian Air Force.

Flight Planning Tool an Aid for Efficient Flight Evaluation

Airborne surveillance systems have multiple sensors and communication links on board a suitable platform. They work in a cohesive manner to provide effective surveillance over the region of interest. The performance proving of such a system is challenging and requires flight trails extending over years. The test results often have to be interpreted using statistical analysis of the flight test data. An efficient way is to carefully design the flight test profiles such that enough samples can be collected during the test and multiple requirements can be tested in a single sortie. Such meticulous test strategies where both own ship platform and test targets are moving with high dynamics call for software based tool for planning of test sorties and the test points. Flight Planning Tool (FPT) plays an important role in pre-flight stage during developmental trials for analysis of the MOEs and MOPs of overall system and of various on-board sensors of an airborne multi-sensor system. The FPT provides statistical & graphical analysis for sensor behaviour for various scenarios (flight trials) before actual flight test is conducted. It provides prior information on number of valid samples for sensor testing during flight trials. In addition, the tool aids in assessing number of profiles to be flown for proving each MOE. The profiles can also be optimised such that valid samples are collected for evaluation.

A cost-effective airborne multisensor system for pipeline monitoring using a quadcopter

A cost-effective airborne multisensor system for pipeline monitoring using a quadcopter

A cost-effective airborne multisensor system for pipeline monitoring using a quadcopter

A cost-effective airborne multisensor system for pipeline monitoring using a quadcopter

A Sequential Aerial Triangulation Algorithm for Real-time Georeferencing of Image Sequences Acquired by an Airborne Multi-Sensor System

Real-time image georeferencing is essential to the prompt generation of spatial information such as orthoimages from the image sequence acquired by an airborne multi-sensor system. It is mostly based on direct georeferencing using a GPS/INS system, but its accuracy is limited by the quality of the GPS/INS data. More accurate results can be acquired using traditional aerial triangulation (AT) combined with GPS/INS data, which can be performed only as a post-processing method due to intense computational requirements. In this study, we propose a sequential AT algorithm that can produce accurate results comparable to those from the simultaneous AT algorithm in real time. Whenever a new image is added, the proposed algorithm rapidly performs AT with minimal computation at the current stage using the computational results from the previous stage. The experimental results show that the georeferencing of an image sequence at any stage took less than 0.1 s and its accuracy was determined within ± 5 cm on the estimated ground points, which is comparable to the results of simultaneous AT. This algorithm may be used for applications requiring real-time image georeferencing such as disaster monitoring and image-based navigation.

Precise Geometric Resigtration of Aerial Imagery and LIDAR Data

In this paper, we develop a registration method to eliminate the geometric inconsistency between the stereo-images and light detection and ranging (LIDAR) data obtained by an airborne multisensor system. This method consists of three steps: registration primitive extraction, correspondence establishment, and exterior orientation parameter (EOP) adjustment. As the primitives, we employ object points and linked edges from the stereo-images and planar patches and intersection edges from the LIDAR data. After extracting these primitives, we establish the correspondence between them, being classified into vertical and horizontal groups. These corresponding pairs are simultaneously incorporated as stochastic constraints into aerial triangulation based on the bundle block adjustment. Finally, the EOPs of the images are adjusted to minimize the inconsistency. The results from the application of our method to real data demonstrate that the inconsistency between both data sets is significantly reduced from the range of 0.5 m to 2 m to less than 0.05 m. Hence, the results show that the proposed method is useful for the data fusion of aerial images and LIDAR data.