In future we expect that UAV platoon based military / civilian missions would require persistent airborne network
support for command, control and communication needs for the mission. Highly-dynamic mobile-wireless sensor
networks operating in a large region present unique challenges in end-to-end communication for sensor data sharing and
data fusion, particularly caused by the time varying connectivity of high-velocity nodes combined with the unreliability
of the wireless communication channel. To establish an airborne communication network, a UAV must maintain a
link(s) with other UAV(s) and/or base stations. A link between two UAVs is deemed to be established when the linked
UAVs are in line of sight as well as within the transmission range of each other. Ideally, all the UAVs as well as the
ground stations involved in command, control and communication operations must be fully connected. However, the
continuous motion of UAVs poses a challenge to ensure full connectivity of the network. In this paper we explore the
dynamic topological network configuration control under mission-related constraints in order to maintain connectivity
among sensors enabling data sharing.
Information assurance is a critical component of any organization's data network. Trustworthiness of the sensor data,
especially in the case of wireless sensor networks (WSNs), is an important metric for any application that requires
situational awareness. In a WSN, information packets are typically not encrypted and the nodes themselves could be
located in the open, leaving them susceptible to tampering and physical degradation. In order to develop a method to
assess trustworthiness in WSNs, we have utilized statistical trustworthiness metrics and have implemented an agentbased
simulation platform that can perform various trustworthiness measurement experiments for various WSN
operating scenarios. Different trust metrics are used against multiple vulnerabilities to detect anomalous behavior and
node failure as well as malicious attacks. The simulation platform simulates WSNs with various topologies, routing
algorithms, battery and power consumption models, and various types of attacks and defense mechanisms. Additionally,
we adopt information entropy based techniques to detect anomalous behavior. Finally, detection techniques are fused to
provide various metrics, and various trustworthiness metrics are fused to provide aggregate trustworthiness for the
purpose of situational awareness.
Persistent surveillance applications require unattended sensors deployed in remote regions to track and monitor some
physical stimulant of interest that can be modeled as output of time varying stochastic process. However, the accuracy or
the trustworthiness of the information received through a remote and unattended sensor and sensor network cannot be
readily assumed, since sensors may get disabled, corrupted, or even compromised, resulting in unreliable information.
The aim of this paper is to develop information theory based metric to determine sensor trustworthiness from the sensor
data in an uncertain and time varying stochastic environment. In this paper we show an information theory based
determination of sensor data trustworthiness using an adaptive stochastic reference sensor model that tracks the sensor
performance for the time varying physical feature, and provides a baseline model that is used to compare and analyze the
observed sensor output. We present an approach in which relative entropy is used for reference model adaptation and
determination of divergence of the sensor signal from the estimated reference baseline. We show that that KL-divergence
is a useful metric that can be successfully used in determination of sensor failures or sensor malice of various types.
The autonomous operations of intelligent unmanned aerial and space access vehicles demand fast online trajectory
computations, which rely heavily upon precise and expedited computation of aerodynamic coefficients. Traditional
methods use tabular data and linear interpolations, which are slow and, even worse, cannot produce smooth aerodynamic
functions that are highly demanded for trajectory computation. In this paper, we introduce neural network and PiecewiseSmooth Function based approaches to approximate these coefficients. Although in the past, neural networks have been
applied to aerodynamic coefficient modeling, they have not been considered for the purpose of trajectory design, which
generate large amounts of data during the flight envelope. In this paper, we present an efficient approach to reduce the
overwhelming amount of data requirements so that the training and testing of the proposed solutions are more
manageable and feasible. The preliminary testing results on the six aerodynamic coefficients show that the pitching
moment coefficient Cm and the axial force coefficient Ca are the most challenging to approximate, while the other four
coefficients are easily approximated. In this paper we have focused on improving approximation models for Cm with
promising results. In the future, we will continue our research on developing models for approximating Ca.
Online aerial vehicle trajectory design and reshaping are crucial for a class of autonomous aerial vehicles such as
reusable launch vehicles in order to achieve flexibility in real-time flying operations. An aerial vehicle is modeled as a
nonlinear multi-input-multi-output (MIMO) system. The inputs include the control parameters and current system states
that include velocity and position coordinates of the vehicle. The outputs are the new system states. An ideal trajectory
control design system generates a series of control commands to achieve a desired trajectory under various disturbances
and vehicle model uncertainties including aerodynamic perturbations caused by geometric damage to the vehicle.
Conventional approaches suffer from the nonlinearity of the MIMO system, and the high-dimensionality of the system
state space. In this paper, we apply a Neural Dynamic Optimization (NDO) based approach to overcome these
difficulties. The core of an NDO model is a multilayer perceptron (MLP) neural network, which generates the control
parameters online. The inputs of the MLP are the time-variant states of the MIMO systems. The outputs of the MLP and
the control parameters will be used by the MIMO to generate new system states. By such a formulation, an NDO model
approximates the time-varying optimal feedback solution.