In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location. 2) Access Point (AP) selection and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

In this paper, we present a cooperative target-centric formation control strategy that maintains the dynamic graph connectivity for a system of unmanned aerial vehicles. The connectivity of a graph plays a critical role in dynamic networks of multiple agents since it represents a level of information sharing capability of a system. The connectivity of a network of unmanned systems changes as the state dependent graph evolves over time, revealing the risk of the system being uncontrollable during null connectivity. We show that the existing formation controller fails to make the formation when the network connectivity is lost. We propose a novel formation control strategy which keeps the multi-agent graph connected throughout the system dynamic process. The convergence of the formation controller is shown with the help of the Lyapunov theory. Simulation results validate the effectiveness of the proposed control law.

In this work, we consider the optimal path of a fixed-wing unmanned aerial vehicle (UAV) tracking a mobile surface target. One of the limitations of fixed-wing UAVs in tracking mobile targets is the lack of hovering capability when the target moves much slower than the minimum UAV speed, requiring the UAV maintain an orbit about the target. In this paper, we propose a method to find the optimal policy for fixed-wing UAVs to minimize the location uncertainty of a mobile target. Using a grid-based Markov Decision Process (MDP), we use an off-line policy iteration algorithm to find an optimal UAV path in a coarse discretized state space, followed by an on-line policy iteration algorithm that applies a finer grid MDP to the region of interest to find the final optimal UAV trajectory. We validate the proposed algorithm using computer simulations. Comparing the simulation results with other methods, we show that the proposed method has up to 13% decrease in error uncertainty than ones resulted using conventional methods.

Our work presents solutions to two related vexing problems in feature-based localization of ground targets in Unmanned Aerial Vehicle (UAV) images: (i) A good initial guess at the pose estimate that would speed up the convergence to the final pose estimate for each image frame in a video sequence; and (ii)Time-bounded estimation of the position of the ground target. We address both these problems within the framework of the Iterative Closest Point (ICP) algorithm that now has a rich tradition of usage in computer vision and robotics applications. We solve the first of the two problems by frame-to-frame propagation of the computed pose estimates for the purpose of the initializations needed by ICP. The second problem is solved by terminating the iterative estimation process at the expiration of the available time for each image frame. We show that when frame-to-frame homography is factored into the iterative calculations, the accuracy of the position calculated at the time of bailing out of the iterations is nearly always sufficient for the goals of UAV vision.

This paper describes our on-going efforts toward developing heterogeneous, cooperative systems technologies. In particular, we present the role of unmanned mobile ground systems (robots) in a heterogeneous sensor network, consisting of two unmanned aircraft, a mobile ground robot, and a set of four stationary ground sensors, performing an intelligence, surveillance, and reconnaissance (ISR) mission. The unmanned mobile ground robot is equipped with an infrared (IR) sensor, the aircraft and the stationary ground sensors use optical cameras and radio frequency (RF) detectors, respectively. The primary responsibility of the mobile ground robot is to verify the identity of a target based on its IR signature. In addition, the mobile ground robot also assists with the sensor network's overall target localization estimation efforts by sharing its IR sensor-based target location measurements with members of the sensor network. Our analysis and field experiments demonstrated scalability and effectiveness of our approach.

Compared with a single platform, cooperative autonomous unmanned aerial vehicles (UAVs) offer efficiency and robustness in performing complex tasks. Focusing on ground mobile targets that intermittently emit radio frequency signals, this paper presents a decentralized control architecture for multiple UAVs, equipped only with rudimentary sensors, to search, detect, and locate targets over large areas. The proposed architecture has in its core a decision logic which governs the state of operation for each UAV based on sensor readings and communicated data. To support the findings, extensive simulation results are presented, focusing primarily on two success measures that the UAVs seek to minimize: overall time to search for a group of targets and the final target localization error achieved. The results of the simulations have provided support for hardware flight tests.

In this paper, we present a control architecture that allows multiple Unmanned Aerial Vehicles (UAVs) to cooperatively detect mobile RF (Radio Frequency) emitting ground targets. The architecture is developed under the premise that UAVs are controlled as a distributed system. The distributed system-based technique maximizes the search and detection capabilities of multiple UAVs. We use a hybrid approach that combines a set of intentional cooperative rules with emerging properties of a swarm to accomplish the objective. The UAVs are equipped only with low-precision RF direction finding sensors and we assume the targets may emit signals randomly with variable duration. Once a target is detected, each UAV optimizes a cost function to determine whether to participate in a cooperative localization task. The cost function balances between the completion of detecting all targets (global search) in the search space and increasing the precision of cooperatively locating already detected targets. A search function for each UAV determines the collective search patterns of collaborating UAVs. Two functions used by each UAV determine (1) the optimal number of UAVs involved in locating targets, (2) the search pattern to detect all targets, and (3) the UAV flight path for an individual UAV. We show the validity of our algorithm using simulation results. Hardware implementation of the strategies is planned for this coming year.

We present a novel cooperative search algorithm for distributed, independent swarm robots. The focus of this paper is three fold: (1) identify fundamental issues associated with searching an area by multiple robots; (2) design a metric to measure the cooperation among multiple robots to complete a search; and (3) propose an effective search algorithm for swarm robots.

In this paper we present a portable fuzzy-logic based intrusion detection system that makes use of behavior profiles and signature matching techniques to detect Hyper Text Transfer Protocol (HTTP) tunnelling activities. The HTTP tunnelling is defined as techniques to use the HTTP protocol to encapsulate illegal and harmful messages within HTTP data. The portable system, whose function is governed by a Motorola 68HC12 microcontroller, is designed to detect both malicious and unauthorized HTTP tunnelling activities: (1) interactive tunnelling sessions, (2) scripted tunnelling sessions, and (3) unauthorized video and audio stream sessions. Preliminary experimental data show the validity of the proposed system.

This paper presents a novel information retrieval system that includes 1) the addition of concepts to facilitate the identification of the correct word sense, 2) a natural language query interface, 3) the inclusion of weights and penalties for proper nouns that build upon the Okapi weighting scheme, and 4) a term clustering technique that exploits the spatial proximity of search terms in a document to further improve the performance. The effectiveness of the system is validated by experimental results.

The paper presents a novel appearance-model based fast object recognition system. The speed is achieved by avoiding much of the image processing required for feature extraction and by compactly representing object models in a subspace. Raw images of an object are transformed into an object model in an eigenvector space after a simple process that compresses the data size using a low pass spatial filter along with an interpolation method. The validity of the proposed scheme is demonstrated by a working model-airplane recognition system.

The progress made so far in the study of legged robots has dealt mostly with the issues of leg coordination, gait control, stability, incorporation of various types of sensors, etc. But what is missing in most of these robots is some perception-based high-level control that would permit a robot to operate intelligently. For the high-level control, a model-based method to recognize a staircase using a single 2D image of a 3D scene is studied. The staircase recognition is achieved by obtaining the pose of a camera coordinate frame which aligns model edges with image edges. The method contains the matching, the pose estimation, and the refinement procedures. A new matching scheme is proposed to reduce the complexity of correspondence search between model and image features. This is accomplished by grouping edges with certain geometric characteristics together. The refinement process uses all matched features to tightly fit the model edges with camera image edges. The resulting recognition is used to guide the robot to climb stairs.

Two major approaches exist for devising gait control for a legged machine: 1) finding a sequence of leg and body movements of a robot on the basis of kinematic and possibly dynamic considerations assuming all foot placements are valid; and 2) selecting leg and body movements first on the basis of feasibility of foot placements and then accepting those that also satisfy kinematic and possibly dynamic constraints. Consider now the problem of a legged robot pursuing a quarry. With the first approach, the robot will be limited to operating over a relatively flat terrain. If the terrain is uneven, however, the robot will have no choice but to take recourse to the second approach even though it is computationally more demanding. The work done so far in the second approach is limited by either the constraint that the overall direction of ambulation of the robot is known in advance, or by the constraint that the foot placements are limited to the points of a grid superimposed on the topographic map of the terrain. In this paper, the authors discuss how a free gait can be generated for following a quarry without invoking such constraints.

Presents a simplified forward gait for a quadruped walking robot. The proposed gait is a straight line, periodic, monotonically forward (SLPMF) gait and can easily be used for adaptive gait control, requiring only simple modifications. The authors show that given a support pattern, meaning a polygon generated by connecting the feet positions that are in contact with the ground, only certain sequences of leg movements will generate the SLPMF gait. The authors also introduce a useful method to determine how to preserve stability of a quadruped robot during the motions called for by an SLPMF gait. The authors also discuss the leg design from a hardware standpoint; the design permits independent joint control.>

An investigation is conducted of the computational aspects of the product of exponentials (POE) formula, and an application to resolved-rate motion control of a six-degree-of-freedom manipulator based on a table lookup scheme is presented. It is shown that the computational requirements for evaluating the Jacobian are comparable to the more standard recursive methods discussed in the work of D. Orin and W. Schrader (1984). In addition to having intuitive geometric appeal, the POE formula adds some measure of device-independence to the kinematic equations of a manipulator.>