Smriti Kansal

Use of a sinusoidal series for modeling discrete windowed data as an alternative to interpolation

The use of a sinusoidal series representation as an alternative to interpolation is introduced. The method is applied to both one-dimensional and two-dimensional data. It is noted that the Fourier series can be used here only if the variable of interest has no discontinuities and has the same values at the end boundaries. For situations where this is not the case we introduce the idea of adding to the Fourier series sinusoids at half the fundamental frequency. The procedure is extended to two-dimensional data and applied to terrain modeling where coarse range scanning has been performed and a model of finer resolution is then obtained. Results are shown to be excellent.

Computationally Derived Models of Adversary Organizations

An extension to the lattice algorithm for designing decision-making organizations subject to cultural constraints is presented. Hofstede dimensions have been used to incorporate cultural attributes in the design process in the form of constraints on the allowable interactions within the organization. An example is used to illustrate the approach.

Computational Models of Multi-National Organizations

An algorithm for designing multi-national organizations that takes into account cultural dimensions is presented and an example from the command and control field is used to illustrate the approach.

The use of mobile robots for remote sensing and object localization

This paper describes the methodology and algorithms required for performing remote sensing via a mobile robot. Additionally the means for determining the ground coordinates of any detected object of interest are also presented. Instrumentation for determining the robot position and orientation is complemented by the use of a Kalman Filter. Two kinematic models for a mobile robot are presented, one for a robot steered by turning the front wheels and one for a robot steered by using differential speeds between the left and right wheels. Given that an object of interest has been detected, which may have been accomplished manually or automatically, an algorithm is developed for aiming the boresight of the sensor at this object. This permits a co-aligned ranging laser to be used to determine the distance to the object. A combination of this information along with robot position and attitude allows computation of the ground coordinates of the object of interest.

Modelling uneven terrain for geo-location of mines detected via vehicular mounted sensors

When searching for landmines using vehicular-mounted sensors, it is important that the ground locations of the detected mines be accurately determined. This is useful for data association when one has multiple looks at a mine by a single sensor or if one uses multiple sensors. This knowledge is necessary for neutralizing the detected mines or at least marking them for avoidance. Factors that contribute to errors in geo-location include sensor error, inaccurate knowledge of the vehicle position and/or attitude, and incomplete knowledge about the terrain being searched. This paper addresses the problem of incomplete terrain knowledge and its effects on geo-location accuracy. It presents different techniques for modelling the terrain and reducing the geo-location errors. A variety of uneven surfaces have been simulated. Random noise has been added to the basic surfaces to simulate the terrain roughness. A terrain model is then derived based on measurements one could obtain from a coarse scan of the field of view using a ranging laser. Two types of sensors are considered for the landmine detection: Synthetic Aperture Forward-Looking Radar (SAFLR) and Camera Type Sensors such as Infrared (IR). Relationships between the terrain unevenness and resulting geo-location errors are presented. The geo-location errors are worse for the Camera Type Sensor, but they can be significant for SAFLR also. The results of this analysis demonstrate that there may be significant geo-location errors for situations where the terrain is not so smooth, e.g. off-road searches, and that the problem can be alleviated via better knowledge of the terrain. If the field of view is planar or almost planar, even coarse-range scanning can improve the geo-location accuracy. For more complex surfaces, finer scanning may be required (preliminary results on this topic were presented at the SPIE (International Society for Optical Engineering) Conference, Orlando, FL, April 2001 (G. Cook, S. Jakkidi, S. Kansal, C. Amazeen and K. Sherbondy, “Impact of uneven terrain on geo-locations errors for mines detected via vehicular mounted sensors”, in Proceedings of SPIE, 2001, pp. 594–605.)). The computed geo-location errors, and the conclusions drawn as to the effectiveness of the different models are presented in the paper.

Use of fiducials and unsurveyed landmarks as geolocation tools in vehicular-based landmine search

When searching for land mines using vehicular-mounted sensors, it is important that the ground locations of the detected mines be accurately determined. This is useful for data association when one has multiple looks at a mine by a single sensor or if one uses multiple sensors. It is of ultimate importance for the primary mission, which is to neutralize the detected mines or mark them for avoidance. This paper addresses the use of surveyed landmarks, i.e., fiducials, as well as unsurveyed but visible landmarks within the sensor field of view for improving the geolocation accuracy. Preliminary results show that in fact geolocation accuracy can be improved significantly by using these tools. The method is primarily applicable to combat situations where a road is to be cleared for advancing troops and equipment. This is in contrast to humanitarian demining where one has more time and can approach suspected landmines from different viewpoints.

Exploitation of Complementary Sensors for Precise Ground-Registration of Sensed Objects

In remote sensing, one is often interested in not only ascertaining the presence of certain resources or objects of interest, but also in determining their locations. Ground registration involves locating the target in sensor coordinates and performing a series of coordinate transformations to convert this location to earth coordinates. One application of this would be in preparing a scaled map showing the precise locations of the resources/objects of interest. To improve ground registration accuracy one can combine multiple looks from a single sensor and/or looks from multiple sensors. One advantage in utilizing multiple sensors is that one can fuse the measurements in such a way as to exploit the best characteristics of each sensor. This paper is applied to vehicular mounted remote sensing and presents the benefits obtained when combining radar and IR as a means of determining ground coordinates of the objects of interest.

Impact of uneven terrain on geo-location errors for mines detected via vehicular-mounted sensors

When searching for land mines using vehicular mounted sensors, it is important that the ground locations of the detected mines be accurately determined. This is useful for data association when one has multiple looks at a mine by a single sensor or if one uses multiple sensors. It is of ultimate importance for the primary mission, which is to neutralize the detected mines or at least to mark them for avoidance. Factors that contribute to errors in geo-location include inaccurate knowledge of the vehicle position and/or attitude, and also incomplete knowledge about the terrain being searched. This paper addresses the problem of incomplete terrain knowledge and presents relationships between terrain unevenness and the resulting geo-location errors. The results of this analysis indicate that there may be significant geo-location errors for situations where the terrain is not so smooth, e.g., off road searches. The problem can be alleviated via better knowledge of the terrain. Such knowledge could be acquired via scanning the field of view with a ranging device, recording range as a function of azimuth and elevation. A variety of uneven surfaces have been simulated. Two types of sensors are considered, Linear-Array Radar and Camera Type Sensors. Geo-location is then computed based on: a) no range measurements, b) four range measurements (to the four vertices of the sensor field of view), and c) nine range measurements (to the four vertices and intermediate points at the top and bottom row, as well as three measurements across the center row). The geo-location errors are much worse for the Camera Type Sensor, but they can be significant for the Linear-Array Radar also. If the field of view is planar or almost planar, even coarse range scanning can improve geo-location accuracy. For more complex surfaces fine scanning may be required. The computed geo-location errors, and the conclusions drawn as to the effectiveness of the different models are presented in the paper.

Correction to “Use of a Sinusoidal Series for Modeling Discrete Windowed Data as an Alternative to Interpolation”

The use of a sinusoidal series representation as an alternative to interpolation is introduced. The method is applied to both one-dimensional and two-dimensional data. It is noted that the Fourier series can be used here only if the variable of interest has no discontinuities and has the same values at the end boundaries. For situations where this is not the case we introduce the idea of adding to the Fourier series sinusoids at half the fundamental frequency. The procedure is extended to two-dimensional data and applied to terrain modeling where coarse range scanning has been performed and a model of finer resolution is then obtained. Results are shown to be excellent.

Remote sensing using vehicular mounted sensors and GPS instrumentation

When performing remote sensing, one often uses vehicular mounted sensors. This provides the flexibility of moving around and searching over a large area and can be done via airborne vehicles, ground vehicles or marine vehicles. For this type of sensing, one needs to know the position and orientation of the sensor platform in order to ground register the location of detected objects. The research reported herein is concerned with the use of a ground vehicle as a sensor platform. Digital camera-type sensors such as infra-red are considered. The focus is on requirements for accurate ground registration of detected objects of interest. A four-antenna GPS array has been chosen for vehicle attitude measurement. Relationships between positions of the array elements and vehicle attitude are derived. It is seen that the attitude computations depend on differences in the various measurements. Thus common-mode errors in the measured position of the array elements cancel, enabling quite accurate attitude measurement even when utilizing somewhat imprecise units in the GPS array. A more precise single differential GPS (DGPS) has been chosen for vehicle position measurements. Relationships between pixel coordinates in the image frame and the angle of the corresponding ray from the camera to the object of interest are derived. A series of transformations are used to convert this ray to ground coordinates. Finally the intersection of the ray with the ground is computed based on the assumption that the ground in the field-of-view is flat and at a known elevation. In this manner an object of interest in the image frame may be ground registered. Sensitivity of the ground registration with respect to vehicle attitude measurement errors is developed. It is seen that small errors in pitch, roll or yaw can cause quite large errors in the computed ground coordinates. In the case of multiple looks at the same object of interest, the geo-registration process involves target tracking and data association. This process is facilitated by combining the single-look measurements in an optimal fashion via a Kalman Filter. In fact the accuracy obtained via multiple looks can be significantly greater than for a single look. The results obtained indicate that accurate geo-registration of remotely sensed objects is possible when using vehicular mounted sensors in conjunction with DGPS and that such a scheme is feasible with commercially available GPS and IR cameras. Geo-registration accuracy within a fraction of a meter is attainable for near objects.