Surveillance Of Ground Targets Research Articles

Efficient Bias Estimation in Airborne Video Georegistration for Ground Target Tracking

Accurate georegistration is required in airborne surveillance of ground targets since even a small bias in sensor registration may result in a large error in the converted target geolocation. Thus, this article addresses the problem of estimating and correcting the sensor biases in target geolocation. The effect of sensor biases on target geolocation is modeled and decoupled from the true target geolocation. By assuming that the biases are small, the effect of biases is separately addressed by a linear term in the converted target geolocation. Based on this model, two computationally efficient methods are proposed to estimate the biases. The converted target geolocation can then be corrected based on the bias estimates. Simulation results demonstrate the effectiveness of the proposed methods in estimating sensor biases and correcting target geolocations as well as their high computational efficiency.

Geo-registration and Geo-location Using Two Airborne Video Sensors

Geo-registration and geo-location of data collected by video sensors such as electro-optical and infrared cameras are two fundamental steps in the airborne surveillance of ground targets. With the availability of high-resolution imaging sensors and detailed mapping or terrain data sources, video data plays an increasingly important role in modern surveillance platforms like unmanned aerial vehicles and airborne, ground, or maritime surveillance systems. Surveillance systems without any compensation for the inevitable sensor registration errors, i.e., biases, may make geo-location erroneous and render the surveillance platform less effective for precision targeting. This article deals with the modeling of sensor biases in geo-location and proposes a method to estimate them. The proposed method leads to a minimization problem with a nonlinear cost function. Detailed derivation of the bias model is given along with an algorithm to find the bias parameters. The achievable lower bounds for debiased geo-location are provided and simulations are used to demonstrate the validity of the proposed method.

Unmanned Aerial Vehicle Surveillance Under Visibility and Dwell-Time Constraints

A framework is introduced for planning unmanned aerial vehicle (UAV) flight paths for visual surveillance of ground targets, each having particular viewing requirements. Specifically, the framework is designed for instances in which each target is associated with a set of imaging parameters, including a desired: (i) tilt angle, (ii) azimuth, with the option of a 360 deg view, and (iii) dwell-time. Tours are sought to image the targets, while minimizing both the total mission time and the time required to reach the initial target. An ϵ-constraint scalarization is used to pose the multi-objective problem as a constrained optimization, which, through careful discretization, can be approximated as a discrete graph-search. It is shown that, in many cases, this approximation is equivalent to a generalized traveling salesperson problem (GTSP). A heuristic procedure for solving the discrete approximation and recovering solutions to the full routing problem is presented and illustrated through numerical studies.