Novel Framework for Navigation using Enhanced Fuzzy Approach with Sliding Mode Controller

The reliability of any embedded navigator in advanced vehicular system depends upon correct and precise information of navigational data captured and processed to offer trustworthy path. After reviewing the existing system, a significant trade-off is explored between the existing navigational system and present state of controller design on various case studies and applications. The existing design of controller system for navigation using error-prone GPS/INS data doesn‟t emphasize on sliding mode controller. Although, there has been good number of studies in sliding mode controller, it is less attempted to optimize the navigational performance of a vehicle. Therefore, this paper presents a novel optimized design of a sliding mode controller that can be effectively deployed on advanced navigational system. The study outcome was found to offer higher speed, optimal control signal, and lower error occurances to prove that proposed system offers reliable and optimized navigational services in contrast to existing system

Fusion Of Multiple Inertial Measurements Units And Its Application In Reduced Cost, Size, Weight, And Power Synthetic Aperture Radars

Position navigation and timing (PNT) is the concept of determining where an object is on the Earth (position), the destination of the object (navigation), and when the object is in these positions (timing). In autonomous applications, these three attributes are crucial to determining the control inputs required to control and move the platform through an area. Traditionally, the position information is gathered using mainly a global positioning system (GPS) which can provide positioning sufficient for most PNT applications. However, GPS navigational solutions are limited by slower update rates, limited accuracy, and can be unreliable. GPS solutions update slower due to the signal having to travel a great distance from the satellite to the receiver. Additionally, the accuracy of the GPS solution relies on the environment of the receiver and the effects caused by additional reflections that introduce ambiguity into the positional solution. As result, the positional solution can become unstable or unreliable if the ambiguities are significant and greatly impact the accuracy of the positional solution. A common solution to addressing the shortcomings of the GPS solution is to introduce an additional sensor focused on measuring the physical state of the platform. The sensors popularly used are inertial measurement units (IMU) and can help provide faster positional accuracy as the transmission time is eliminated. Furthermore, the IMU is directly measuring physical forces that contribute to the position of the platform, therefore, the ambiguities caused by additional signal reflections are also eliminated. Although the introduction of the IMU helps mitigate some of the shortcomings of GPS, the sensors introduce a slightly different set of challenges. Since the IMUs directly measure the physical forces experienced by the platform, the position is estimated using these measurements. The estimates of position utilize the previously known position and estimate the changes to the position based on the accelerations measured by the IMUs. As the IMUs intrinsically have sensor noise and errors in their measurements, the noise errors directly impact the accuracy of the position estimated. These inaccuracies are further compounded as the erroneous position estimate is now used as the basis for future position calculations. Inertial navigation systems (INS) have been developed to pair the IMUs with the GPS to overcome the challenges brought by each sensor independently. The data provided from each sensor is processed using a technique known as data fusion where the statistical likelihood of each positional solution is evaluated and used to estimate the most likely position solution given the observations from each sensor. Data fusion allows for the navigation solution to provide a positional solution at the sampling rate of the fastest sensor while also limiting the compounding errors intrinsic to using IMUs. Synthetic aperture radar (SAR) is an application that utilizes a moving radar to synthetically generate a larger aperture to create images of a target scene. The larger aperture allows for a finer spatial resolution resulting in higher quality SAR images. For synthetic aperture radar applications, the PNT solution is fundamental to producing a quality image as the range to a target is only reported by the radar. To form an image, the range to each target must be aligned over the coherent processing interval (CPI). In doing so, the energy reflected from the target as the radar is moving can be combined coherently and resolved to a pixel in the image product. In practice, the position of the radar is measured using a navigational solution utilizing a GPS and IMU. Inaccuracies in these solutions directly contribute to the image quality in a SAR system because the measured range from the radar will not agree with the calculated range to the location represented by the pixel. As a result, the final image becomes unfocused and the target will be blurred across multiple pixels. For INS systems, increasing the accuracy of the final position estimate is dependent on the accuracy of the sensors in the system. An easy way to increase the accuracy of the INS solution is to upgrade to a higher grade IMU. As a result, the errors compounded by the IMU estimations are minimized because the intrinsic noise perturbations are smaller. The trade-off is the IMU sensors increase in cost, size, weight, and power (C-SWAP) as the quality of the sensor increases. The increase in C-SWAP is a challenge of utilizing higher grade IMUs in INS navigational solutions for SAR applications. This problem is amplified when developing miniaturized SAR systems. In this dissertation, a method of leveraging the benefits of data fusion to combine multiple IMUs to produce higher accuracy INS solutions is presented. Specifically, the C-SWAP can be reduced when utilizing lower-quality IMUs. The use of lower quality IMUs presents an additional challenge of providing positional solutions at the rates required for SAR. A method of interpolating the position provided by the fusion algorithm while maintaining positional accuracy is also presented in this dissertation. The methods presented in this dissertation are successful in providing accurate positional solutions from lower C-SWAP INS. The presented methods are verified in simulations of motion paths and the results of the fusion algorithms are evaluated for accuracy. The presented methods are instrumented in both ground and flight tests and the results are compared to a 3rd party accurate position solution for an accuracy metric. Lastly, the algorithms are implemented in a miniaturized SAR system and both ground and airborne SAR tests are conducted to evaluate the effectiveness of the algorithms. In general, the designed algorithms are capable of producing positional accuracy at the rate required to focus SAR images in a miniaturized SAR system

Proceedings of the Augmented VIsual Display (AVID) Research Workshop

The papers, abstracts, and presentations were presented at a three day workshop focused on sensor modeling and simulation, and image enhancement, processing, and fusion. The technical sessions emphasized how sensor technology can be used to create visual imagery adequate for aircraft control and operations. Participants from industry, government, and academic laboratories contributed to panels on Sensor Systems, Sensor Modeling, Sensor Fusion, Image Processing (Computer and Human Vision), and Image Evaluation and Metrics

The Sparse-grid based Nonlinear Filter: Theory and Applications

Filtering or estimation is of great importance to virtually all disciplines of engineering and science that need inference, learning, information fusion, and knowledge discovery of dynamical systems. The filtering problem is to recursively determine the states and/or parameters of a dynamical system from a sequence of noisy measurements made on the system. The theory and practice of optimal estimation of linear Gaussian dynamical systems have been well established and successful, but optimal estimation of nonlinear and non-Gaussian dynamical systems is much more challenging and in general requires solving partial differential equations and intractable high-dimensional integrations. Hence, Gaussian approximation filters are widely used. In this dissertation, three innovative point-based Gaussian approximation filters including sparse Gauss-Hermite quadrature filter, sparse-grid quadrature filter, and the anisotropic sparse-grid quadrature filter are proposed. The relationship between the proposed filters and conventional Gaussian approximation filters is analyzed. In particular, it is proven that the popular unscented Kalman filter and the cubature Kalman filter are subset of the proposed sparse-grid filters. The sparse-grid filters are employed in three aerospace applications including spacecraft attitude estimation, orbit determination, and relative navigation. The results show that the proposed filters can achieve better estimation accuracy than the conventional Gaussian approximation filters, such as the extended Kalman filter, the cubature Kalman filter, the unscented Kalman filter, and is computationally more efficient than the Gauss-Hermite quadrature filter

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Design Options For Low Cost, Low Power Microsatellite Based SAR.

This research aims at providing a system design that reduces the mass and cost of spaceborne Synthetic Aperture Radar (SAR) missions by a factor of two compared to current (TecSAR - 300 kg, ~ £ 127 M) or planned (NovaSAR-S — 400 kg, ~ £ 50 M) mission. This would enable the cost of a SAR constellation to approach that of the current optical constellation such as Disaster Monitoring Constellation (DMC). This research has identified that the mission cost can be reduced significantly by: focusing on a narrow range of applications (forestry and disasters monitoring); ensuring the final design has a compact stowage volume, which facilitates a shared launch; and building the payload around available platforms, rather than the platform around the payload. The central idea of the research has been to operate the SAR at a low instantaneous power level—a practical proposition for a micro-satellite based SAR. The use of a simple parabolic reflector with a single horn at L-band means that a single, reliable and efficient Solid State Power Amplifier (SSPA) can be used to lower the overall system cost, and to minimise the impact on the spacecraft power system. A detailed analysis of basic pulsed (~ 5 - 10 % duty cycle) and Continuous Wave (CW) SAR (100 % duty cycle) payloads has shown their inability to fit directly into existing microsatellite buses without involving major changes, or employing more than one platform. To circumvent the problems of pulsed and CW techniques, two approaches have been formulated. The first shows that a CW SAR can be implemented in a mono-static way with a single antenna on a single platform. In this technique, the SAR works in an Interrupted CW (ICW) mode, but these interruptions introduce periodic gaps in the raw data. On processing, these gapped data result in artefacts in the reconstructed images. By applying data based statistical estimation techniques to “fill in the gaps” in the simulated raw SAR data, this research has shown the possibility of minimising the effects of these artefacts. However, once the same techniques are applied to the real SAR data (in this case derived from RADARSAT-1), the artefacts are shown to be problematic. Because of this the ICW SAR design technique it is—set aside. The second shows that an extended chirp mode pulsed (ECMP) SAR (~ 20 - 54 % duty cycle) can be designed with a lowered peak power level which enables a single SSPA to feed a parabolic Cassegrain antenna. The detailed analysis shows the feasibility of developing a microsatellite based SAR design at a comparable price to those of optical missions