For many years, terrain navigation has been successfully used in military airborne applications. Terrain navigation can essentially improve the performance of traditional inertial-based navigation. The latter is typically built around gyros and accelerometers, measuring the kinetic state changes. Although inertial-based systems benefit from their high independence, they, unfortunately, suffer from increasing error-growth due to accumulation of continuous measurement errors. Undersea, the number of options for navigation support is fairly limited. Still, the navigation accuracy demands on autonomous underwater vehicles are increasing. For many military applications, surfacing to receive a GPS position- update is not an option. Lately, some attention has, instead, shifted towards terrain aided navigation. One fundamental aim of this work has been to show what can be done within the field of terrain aided underwater navigation, using relatively simple means. A concept has been built around a narrow-beam altimeter, measuring the depth directly beneath the vehicle as it moves ahead. To estimate the vehicle location, based on the depth measurements, a particle filter algorithm has been implemented. A number of MATLAB simulations have given a qualitative evaluation of the chosen algorithm. In order to acquire data from actual underwater terrain, a small area of the Swedish lake, Lake Vättern has been charted. Results from simulations made on this data strongly indicate that the particle filter performs surprisingly well, also within areas containing relatively modest terrain variation.

I detta examensarbete har ett system för navigering och styrning av ett autonomt fordon implementerats. Syftet med detta arbete är att vidareutveckla fordonet som ska användas vid utvärdering av banplaneringsalgoritmer och studier av andra autonomifunktioner. Med hjälp av olika sensormodeller och sensorkonfigurationer går det även att utvärdera olika strategier för navigering. Arbetet har utförts utgående från en given plattform där fordonet endast använder sig av enkla ultraljudssensorer samt pulsgivare på hjulen för att mäta förflyttningar. Fordonet kan även autonomt navigera samt följa en enklare given bana i en känd omgivning. Systemet använder ett partikelfilter för att skatta fordonets tillstånd med hjälp av modeller för fordon och sensorer. Arbetet är en fortsättning på projektet Collision Avoidance för autonomt fordon som genomfördes vid Linköpings universitet våren 2005. / In this thesis a system for navigation and control of an autonomous ground vehicle has been implemented. The purpose of this thesis is to further develop the vehicle that is to be used in studies and evaluations of path planning algorithms as well as studies of other autonomy functions. With different sensor configurations and sensor models it is also possible to evaluate different strategies for navigation. The work has been performed using a given platform which measures the vehicle’s movement using only simple ultrasonic sensors and pulse encoders. The vehicle is able to navigate autonomously and follow a simple path in a known environment. The state estimation is performed using a particle filter. The work is a continuation of a previous project, Collision Avoidance för autonomt fordon, at Linköpings University in the spring of 2005.

This research focuses on the development of intelligent fastening tool tracking systems for the automotive industry to identify the fastened bolts. In order to accomplish such a task, the position of the tool tip must be identified because the tool tip position coincides with the head of the fastened bolt while the tool fastens the bolt. The proposed systems utilize an inertial measurement unit (IMU) and another sensor to track the position and orientation of the tool tip. To minimize the position and orientation calculation error, an IMU needs to be calibrated as accurately as possible. This research presents a novel triaxial accelerometer calibration technique that offers a high accuracy. The simulation and experimental results of the accelerometer calibration are presented. To identify the fastening action, an expert system is developed based on the sensor measurements. When a fastening action is identified, the system identifies the fastened bolt by using an expert system based on the position and orientation of the tool tip and the position and orientation of the bolt. Since each fastening procedure needs different accuracies and requirements, three different systems are proposed. The first system utilizes a triaxial magnetometer and an IMU to identify the fastened bolt. This system calculates the position and orientation by using an IMU. An expert system is used to identify the initial position, stationary state, and the fastened bolt. When the tool fastens a bolt, the proposed expert system detects the fastening action by triaxial accelerometer and triaxial magnetometer measurements. When the fastening action is detected, the system corrects the velocity and position error using zero velocity update (ZUPT). By using the corrected tool tip position and orientation, the system can identify the fastened bolts. Then, with the fastened bolt position, the position of the IMU is corrected. When the tool is stationary, the system corrects linear velocity error and reduces the position error. The experimental results demonstrate that the proposed system can identify fastened bolts if the angles of the bolts are different or the bolts are not closely placed. This low cost system does not require a line of sight, but has limited position accuracy. The second system utilizes an intelligent system that incorporates Kalman filters (KFs) and a fuzzy expert system to track the tip of a fastening tool and to identify the fastened bolt. This system employs one IMU and one encoder-based position sensor to determine the orientation and the centre of mass location of the tool. When the KF is used, the orientation error increases over time due to the integration step. Therefore, a fuzzy expert system is developed to correct the tilt angle error and orientation error. When the tool fastens a bolt, the system identifies the fastened bolt by applying the fuzzy expert system. When the fastened bolt is identified, the 3D orientation error of the tool is corrected by using the location and the orientation of the fastened bolt and the position sensor outputs. This orientation correction method results in improved reliability in determining the tool tip location. The fastening tool tracking system was experimentally tested in a lab environment, and the results indicate that such a system can successfully identify the fastened bolts. This system not only has a low computational cost but also provides good position and orientation accuracy. The system can be used for most applications because it provides a high accuracy. The third system presents a novel position/orientation tracking methodology by hybridizing one position sensor and one factory calibrated IMU with the combination of a particle filter (PF) and a KF. In addition, an expert system is used to correct the angular velocity measurement errors. The experimental results indicate that the orientation errors of this method are significantly reduced compared to the orientation errors obtained from an EKF approach. The improved orientation estimation using the proposed method leads to a better position estimation accuracy. The experimental results of this system show that the orientation of the proposed method converges to the correct orientation even when the initial orientation is completely unknown. This new method was applied to the fastening tool tracking system. This system provides good orientation accuracy even when the gyroscopes (gyros hereafter) include a small error. In addition, since the orientation error of this system does not grow over time, the tool tip position drift is limited. This system can be applied to the applications where the bolts are closely placed. The position error comparison results of the second system and the third system are presented in this thesis. The comparison results indicate that the position accuracy of the third system is better than that of the second system because the orientation error does not increase over time. The advantages and limitations of all three systems are compared in this thesis. In addition, possible future work on fastening tool tracking system is described as well as applications that can be expanded by using the KF/PF combination method.

In this thesis the radar target tracking problem in Bayesian estimation framework is studied. Traditionally, linear or linearized models, where the uncertainty in the system and measurement models is typically represented by Gaussian densities, are used in this area. Therefore, classical sub-optimal Bayesian methods based on linearized Kalman filters can be used. The sequential Monte Carlo methods, i.e. particle filters, make it possible to utilize the inherent non-linear state relations and non-Gaussian noise models. Given the sufficient computational power, the particle filter can provide better results than Kalman filter based methods in many cases. A survey over relevant radar tracking literature is presented including aspects as estimation and target modeling. In various target tracking related estimation applications, particle filtering algorithms are presented.

Detection and tracking of signals used in sonar applications in noisy environment is the focus of this thesis. We have concentrated on the low Signal-to-Noise Ratio (SNR) case where the conventional detection methods are not applicable. Furthermore, it is assumed that the duty cycle is relatively low. In the problem that is of concern the carrier frequency, pulse repetition interval (PRI) and the existence of the signal are not known. The unknown character of PRI makes the problem challenging since it means that the signal exists at some unknown intervals. A recursive, Bayesian track-before-detect (TBD) filter using particle filter based methods is proposed to solve the concerned problem. The data used by the particle filter is the magnitude of a complex spectrum in complex Gaussian noise. The existence variable is added in the design of the filter to determine the existence of the signal. The evolution of the signal state is modeled by a linear stochastic process. The filter estimates the signal state including the carrier frequency and PRI. Simulations are done under different scenarios where the carrier frequency, PRI and the existence of the signal varies. The results demonstrate that the algorithm presented in this thesis can detect signals which cannot be detected by conventional methods. Besides detection, the tracking performance of the filter is satisfying.

In this thesis a number of improvements have been established for specific methods which utilize sequential Monte Carlo (SMC), aka. Particle filtering (PF) techniques. The first problem is the Bayesian multi-target tracking (MTT) problem for which we propose the use of non-parametric Bayesian models that are based on time varying extension of Dirichlet process (DP) models. The second problem studied in this thesis is an important application area for the proposed DP based MTT method / the tracking of vocal tract resonance frequencies of the speech signals. Lastly, we investigate SMC based parameter estimation problem of nonlinear non-Gaussian state space models in which we provide a performance improvement for the path density based methods by utilizing regularization techniques.

The observability measure based on the mutual information between the last state and the measurement sequence originally proposed by Mohler and Hwang (1988) is analyzed in detail and improved further for linear time invariant discrete-time Gaussian stochastic systems by extending the definition to the observability measure of a state sequence. By using the new observability measure it is shown that the unobservable states of the deterministic system have no effect on this measure and any observable part with no measurement uncertainty makes it infinite. Other distance measures i.e., Bhattacharyya and Hellinger distances are also investigated to be used as observability measures. The relationships between the observability measures and the covariance matrices of Kalman filter and the state sequence conditioned on the measurement sequence are derived. Steady state characteristics of the observability measure based on the last state is examined. The observability measures of a subspace of the state space, an individual state, the modes of the system are investigated. One of the results obtained in this part is that the deterministically unobservable states may have nonzero observability measures. The observability measures based on the mutual information are represented recursively and calculated for nonlinear stochastic systems. Then the measures are applied to a nonlinear stochastic system by using the particle filter methods. The arguments given for the LTI case are also observed for nonlinear stochastic systems. The second moment approximation deviates from the actual values when the nonlinearity in the system increases.

In this study Track Before Detect (TBD) approach will be analysed for tracking of dim moving targets. First, a radar setup is presented in order to introduce the radar range equation and signal models. Then, preliminary information is given about particle filters. As the main algorithm of this thesis, a multi-model particle filter method is developed in order to solve the non-linear non-Gaussian Bayesian estimation problem. Probability of target existence and RMS estimation accuracy are defined as the performance parameters of the algorithm for very low SNR targets. Simulation results are provided and performance analysis is presented as a conclusion.

Short range thrusting projectiles are one of the various threats against armored vehicles and helicopters on the battlefield. Developing a countermeasure for this kind of projectiles is very crucial since they are vast in number and easy to operate on the battlefield. A countermeasure may consist of fire point prediction of the projectile and attack the launcher of it, or it may be the impact point prediction of the projectile and apply a hard-kill counter measure on its way to the ally target. For both of the countermeasure concepts, dynamics and parameters of the projectile must be estimated precisely. In this thesis, dynamic models for thrusting and ballistic flight modes of thrusting projectile are obtained. Three different tracking filters are suggested for precise tracking of the projectiles and their estimation performances are compared. These filters are the Extended Kalman Filter (EKF), the Particle Filter (PF) and the Marginalized Particle Filter (MPF).

<p>In this thesis Sequential Monte Carlo filters, or particle filters, applied to aircraft navigation is considered. This report consists of two parts. The first part is an illustration of the theory behind this thesis project. The second and most important part evaluates the algorithm by using real flight data.</p><p>Navigation is about determining one's own position, orientation and velocity. The sensor fusion studied combines data from an inertial navigation system (INS) with measurements of the ground elevation below in order to form a terrain aided positioning system (TAP). The ground elevation measurements are compared with a height database. The height database is highly non-linear, which is why a marginalized particle filter (MPF) is used for the sensor fusion.</p><p>Tests have shown that the MPF delivers a stable and good estimate of the position, as long as it receives good data. A comparison with Saab's NINS algorithm showed that the two algorithms perform quite similar, although NINS performs better when data is lacking.</p>