Nicholas A. Baine

Dilution of Precision in Vision Navigation Systems

Dilution of Precision (DOP) is a common measure of the quality of measurement geometry in the Global Positioning System. Measurement references that are clustered together in space provide nearly identical measurements and consequently little additional information relative to each other. To have a good measurement geometry, the measurements need to be unique combinations of the navigation states, and DOP is a measure for this. This paper takes this concept of DOP and applies it to optical measurements from a vision navigation system. The DOP for vision measurements is developed by relating noise in measurements of reference location in an image to both position and orientation states. The relationship is demonstrated with a numerical example.

Algorithm for the detection and isolation of systemic measurement errors in vision based/aided navigation systems

This paper introduces a new test statistic that can detect systematic errors in vision systems. This can be used for integrity monitoring of vision navigation systems. Current integrity monitoring methods look for agreement among the measurements from a camera. This method looks for agreement between separate cameras. If the measurements do not agree then a parameter of one of the cameras has changed resulting in a systematic error. An example of the usefulness of this test statistic is shown with a Monte Carlo test.

INS aided by an acoustic wireless sensor network and magnetometer

The objective of this paper is to develop the theory for integrating magnetometer measurements with an existing inertial navigation system (INS) aided by an acoustic wireless sensor network. The system without magnetometer measurements provides position information reliably. However, in the absence of dynamic motion the orientation errors are not observable using the sensor network, which only provides position information. The magnetometer provides consistent observability of the orientation. The measurement model needed to estimate the orientation errors using the magnetometer is derived in this paper. This measurement model is integrated with measurements from the acoustic sensor network into a complimentary Kalman filter that is used to estimate the error states of an INS.

Navigation of robotic system using cricket motes

This paper presents a novel algorithm for self-mapping of the cricket motes that can be used for indoor navigation of autonomous robotic systems. The cricket system is a wireless sensor network that can provide indoor localization service to its user via acoustic ranging techniques. The behavior of the ultrasonic transducer on the cricket mote is studied and the regions where satisfactorily distance measurements can be obtained are recorded. Placing the motes in these regions results fine-grain mapping of the cricket motes. Trilateration is used to obtain a rigid coordinate system, but is insufficient if the network is to be used for navigation. A modified SLAM algorithm is applied to overcome the shortcomings of trilateration. Finally, the self-mapped cricket motes can be used for navigation of autonomous robotic systems in an indoor location.

Indoor localization for global information service using acoustic wireless sensor network

Indoor localization with sensing capabilities is the missing link for a Geospatial Information System and sensor web. The sensor network is capable of environmental monitoring and geo-tagging sensor data. This paper presents a unique algorithm which uses fusion of Radio Signal Strength Indicator and Time Difference of Arrival for centimeter level accurate indoor localization using wireless sensor network motes. The paper also proposes the integration of various environmental sensors with wireless sensor network. The acquired sensor data can be geo-tagged with the translated global coordinates and additional sensory metadata. With the use of semantic sensor web, this sensor information can be utilized in various decision making scenarios for critical situations. The main goal of the paper is to use indoor localization assisted by sensor fusion and semantic web for first responders in emergency scenarios.

Partial compensation of large scale discrete systems

This paper addresses the problem of partial state feedback compensation for large scale discrete systems. The eigenvalues of the closed-loop matrix should lie within a designated region of the z-domain to satisfy both stability and damping requirements. The system is to be compensated in such a way that only the eigenvalues that lie outside the desired region are affected. This is achieved through the use of the fast matrix sector function to decompose the system without solving for the eigenvalues. The decomposed system is then controlled using LQR design techniques.