No description available.

Scaled test-beds (STBs) are popular tools to develop and physically test algorithms for advanced driving systems, but often lack automotive-grade radars in their sensor suites. To overcome resolution issues when using a radar at small scale, a high-level sensor fusion approach between the radar and automotive-grade LiDAR was proposed. The sensor fusion approach was expected to leverage the higher spatial resolution of the LiDAR effectively. First, multi object radar tracking software (RTS) was developed to track a maneuvering full-scale vehicle using an extended Kalman filter (EKF) and the joint probabilistic data association (JPDA). Second, a 1/5th scaled vehicle performed the same vehicle maneuvers but scaled to approximately 1/5th the distance and speed. When taking the scaling factor into consideration, the RTS' positional error at small scale was, on average, over 5 times higher than in the full-scale trials. Third, LiDAR object sensor tracks were generated for the small-scale trials using a Velodyne PUCK LiDAR, a simplified point cloud clustering algorithm, and a second EKF implementation. Lastly, the radar sensor tracks and LiDAR sensor tracks served as inputs to a high-level track-to-track fuser for the small-scale trials. The fusion software used a third EKF implementation to track fused objects between both sensors and demonstrated a 30% increase in positional accuracy for a majority of the small-scale trials when compared to using just the radar or just the LiDAR to track the vehicle. The proposed track fuser could be used to increase the accuracy of RTS algorithms when operating in small scale and allow STBs to better incorporate automotive radars into their sensor suites. / Master of Science / Research and development platforms, often supported by robust prototypes, are essential for the development, testing, and validation of automated driving functions. Thousands of hours of safety and performance benchmarks must be met before any advanced driver assistance system (ADAS) is considered production-ready. However, full-scale testbeds are expensive to build, labor-intensive to design, and present inherent safety risks while testing. Scaled prototypes, developed to model system design and vehicle behavior in targeted driving scenarios, can minimize these risks and expenses. Scaled testbeds, more specifically, can improve the ease of safety testing future ADAS systems and help visualize test results and system limitations, better than software simulations, to audiences with varying technical backgrounds. However, these testbeds are not without limitation. Although small-scale vehicles may accommodate similar on-board systems to its full-scale counterparts, as the vehicle scales down the resolution from perception sensors decreases, especially from on board radars. With many automated driving functions relying on radar object detection, the scaled vehicle must host radar sensors that function appropriately at scale to support accurate vehicle and system behavior. However, traditional radar technology is known to have limitations when operating in small-scale environments. Sensor fusion, which is the process of merging data from multiple sensors, may offer a potential solution to this issue. Consequently, a sensor fusion approach is presented that augments the angular resolution of radar data in a scaled environment with a commercially available Light Detection and Ranging (LiDAR) system. With this approach, object tracking software designed to operate in full-scaled vehicles with radars can operate more accurately when used in a scaled environment. Using this improvement, small-scale system tests could confidently and quickly be used to identify safety concerns in ADAS functions, leading to a faster and safer product development cycle.

This investigation is concerned with effective state estimation of a system driven by an unknown nonGaussian input with additive white Gaussian noise, and observed by measurements containing feedthrough of the same nonGaussian input and corrupted by additional white Gaussian noise. A Gaussian sum (GS) approach has previously been developed [6-8] which can cope with the non Gaussian nature of the input signal. Due to a serious growing memory problem in this approach, a modified Gaussian sum (MGS) estimation technique is developed that avoids the growing memory problem while providing effective state estimation. Several differences between the MGS and GS algorithms are examined. An MGS adaptive filter is derived for a general system and a modal system, with simulation examples performed using a non Gaussian input signal. The modal system simulation results are compared to those produced from an augmented Kalman filter based on an augmented modal system model assuming a narrowband Gaussian input signal. A necessary condition for effective MGS estimation is derived. Alternate estimation procedures are developed to compensate for situations when this condition is not met. Several configurations are simulated and their performance results are analyzed and compared. Two methods of monitoring and updating key parameters of the MGS filter are developed. Simulation results are analyzed to investigate the performance of these methods. / Ph. D.

Adaptive algorithms for state estimation are currently of tremendous interest. Such estimation techniques have particular military usefulness in automatic gunfire control systems. The conventional Kalman filter, developed by Kalman and Bucy, optimally solves the state estimation problem concerning linear systems with Gaussian disturbance and error processes. The maneuvering target tracking problem generally involves nonlinear system properties as well as non-Gaussian disturbance processes. The study presented here explores several solutions. to this problem. An adaptive state estimator centered about the familiar Kalman filter has been developed for applications in three-dimensional maneuvering target tracking. Target maneuvers are modeled in a general manner by a semi-Markov process. The semi-Markov modeling is based on very intuitively appealing assumptions. Specifically, target maneuvers are randomly selected from a range (possibly infinite) of maneuver commands. The selected command is sustained for a random holding time before another command is selected. Dynamics of the selection and holding process may be stationary or time varying. By incorporating the semi-Markov modeling into a Baysian estimation scheme, an adaptive state estimator can be designed to identify the particular maneuver command influencing the target. The algorithm has the distinct advantages of requiring only one Kalman filter and non-growing computer storage requirements. Several techniques of implementing the adaptive algorithm have been developed. The merits of rectangular and spherical modeling have been explored. Most importantly, the planar discrete level semi-Markov algorithm, originally developed for sonar applications, has been extended to a continuum of levels, as well as extended to three-dimensional tracking. The developed algorithms have been fully evaluated by computer simulations. Emphasis has been placed on computational burden as well as overall tracking performance. Results are presented that show.that the developed estimators largely eliminate severe tracking errors that occur when more simplistic target models are incorporated. / Ph. D.

A computationally efficient estimation technique is presented for a class of nonlinear systems consisting of memoryless nonlinearities combined with linear dynamic processes. The modeling approach is based on a useful sampled-data method for simulating such systems by adding a system state for each nonlinear element. The nonlinear estimator is next developed along the lines of the Kalman filter, but in contrast to the Extended Kalman Filter (EKF) the present approach does not require the linearization step after each recursive cycle. In addition, it also appears free from the well known divergence problems associated with the EKF. It is demonstrated that this new method is directly applicable to those feedback systems with both major nonlinearities, for example saturating gain blocks, and stochastic disturbances-- an example extremely difficult to handle with EKF techniques. / Master of Science

The simulation and testing of several state tracking techniques over a range of process and measurement noise environments is considered. The problem is placed in the context of tracking a maneuvering vehicle from noisy position data with the vehicle accelerations considered as a random process about which the first and second order statistics are known. The tracking filters under test are the fixed α-β filter, the double α-β filter, the second order Kalman filter, the augmented Kalman filter, and the double Kalman filter. All filters show improved performance as the measurement noise increases and the process noise decreases. The superiority of the Kalman filter over the simpler deterministic digital trackers decreases as the measurement noise increases and the process noise decreases. The double Kalman filter, with the capability of adaptive adjustments of threshold values, indicates the best overall tracking for combined maneuver and non-maneuver tracking. / Master of Science

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2005. / Visually estimating three-dimensional position, orientation and motion, between an observer and a target, is an important problem in computer vision. Solutions which compute threedimensional movement from two-dimensional intensity images, usually rely on stereoscopic vision. Some research has also been done in systems utilising a single (monocular) camera. This thesis investigates methods for estimating position and pose from monocular image sequences. The intended future application is of visual tracking between satellites flying in close formation. The ideas explored in this thesis build on methods developed for use in camera calibration, and structure from motion (SfM). All these methods rely heavily on the use of different variations of the Kalman Filter. After describing the problem from a mathematical perspective we develop different approaches to solving the estimation problem. The different approaches are successfully tested on simulated as well as real-world image sequences, and their performance analysed.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1975 / Vita. / Includes bibliographical references. / Ph. D. / Ph. D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.