Department of Electrical Engineering (ISY)

This article presents a pedestrian tracking methodology using an infrared sensor for surveillance applications. A distinctive feature of this study compared to the existing pedestrian tracking approaches is that the road network information is utilized for performance enhancement. A multiple model particle filter, which uses two different motion models, is designed for enabling the tracking of both road-constrained (on-road) and unconstrained (off-road) targets. The lateral position of the pedestrians on the walkways are taken into account by a specific on-road target model. The overall framework seamlessly integrates the negative information of occlusion events into the algorithm for which the required modifications are discussed. The resulting algorithm is illustrated on real data from a field trial for different scenarios.

A robust, accurate positioning system with seamless outdoor and indoor coverage is a highly needed tool for increasing safety in emergency response and military urban operations. It must be lightweight, small, inexpensive, and power efficient, and still provide meter-level accuracy during extended operations. GPS receivers, inertial sensors, and local radio-based ranging are natural choices for a multisensor positioning system. Inertial navigation with foot-mounted sensors is suitable as the core system in GPS denied environments, since it can yield meter-level accuracies for a few minutes. However, there is still a need for additional supporting sensors to keep the accuracy at acceptable levels during the duration of typical soldier and first responder operations. Suitable aiding sensors are three-axis magnetometers, barometers, imaging sensors, Doppler radars, and ultrasonic sensors. Furthermore, cooperative positioning, where first responders exchange position and error estimates in conjunction with performing radio-based ranging, is deemed a key technology. This article provides a survey on technologies and concepts for high accuracy soldier and first responder positioning systems, with an emphasis on indoor positioning.

Using the parity-space approach, a residual is formed by applying a projection to a batch of observed data and this is a well established approach to fault detection. Based on a stochastic state space model, the parity-space residual can be put into a stochastic framework where conventional hypothesis tests apply. In an on-line application, the batch of data corresponds to a sliding window and in this contribution we develop an improved on-line algorithm that extends the parity-space approach by taking prior information from previous observations into account. For detection of faults, the Generalized Likelihood Ratio (GLR) test is used. This framework allows for including prior information about the initial state, yielding a test statistic with a significantly higher sensitivity to faults. Another key advantage with this approach is that it can be extended to nonlinear systems using an arbitrary nonlinear filter for state estimation, and a linearized model around a nominal state trajectory in the sliding window. We demonstrate the algorithm on data from an Inertial Measurement Unit (IMU), where small and incipient magnetic disturbances are detected using a nonlinear system model.

The problem of estimating heading is central in the indoor positioning problem based on measurements from inertial measurement and magnetic units, Integrating rate of turn angular rate gives the heading with unknown initial condition and a linear drift over time, while the magnetometer gives absolute heading, but m here long segments of data are useless in practice because of magnetic disturbances. A basic Kalman filter approach with outlier rejection has turned out to be difficult to use with high integrity. Here, we propose an approach based on convex optimization, where segments of good magnetometer data are separated from disturbed data and jointly fused with the yaw rate measurements. The optimization framework is flexible with many degrees of freedom in the modeling phase, and we outline one design. A recursive solution to the optimization is derived, which has a computational complexity comparable to the simplest possible Kalman filter. The performance is evaluated using data from a handheld smartphone for a large amount of indoor trajectories, and the result demonstrates that the method effectively resolves the magnetic disturbances.

Airborne surveillance systems equipped with a vision/infrared camera require good knowledge about the position and orientation of the camera for successful tracking of ground targets. In particular, this is essential when incorporating prior information, like road maps, that is expressed relative a global reference system. Usually, it is possible to obtain good positioning with inertial/satellite navigation systems, but estimating the orientation is generally more difficult. It might be possible to use SLAM (Simultaneous Localization and Mapping) or image registration approaches to support the navigation system, but not always since such approaches require stable features in the images. In this paper the problem of simultaneous orientation error estimation and road target tracking is considered by assuming that the target is constrained to a known road network. A particle filter approach is proposed and it is shown that the result of this filter is close to the performance of the ideal case where the orientation error is perfectly known. However, the performance depends on how informative the road path is and in rare cases the orientation error is unobservable.

For linear and Gaussian systems, fault detection over a batch of data is well-studied, and analytical solutions exist in a stochastic framework. The parity space approach handles additive faults and can be shown to be equivalent to estimating the state trajectory and then removing its influence on the output sequence. Multiplicative faults in linear systems can be handled using parameter estimation methods, such as the EM-algorithm in combination with the Kalman smoother. For nonlinear and non-Gaussian systems, we propose to estimate the state trajectory and the faults over the data batch using a particle smoother and the EM-algorithm. The result is a generic fault detection and isolation scheme that applies to arbitrary nonlinear and non-Gaussian systems, where the faults are monitored over a sliding window.

A UAV navigation system relying on GPS is vulnerable to signal failure, making a drift free backup system necessary. We introduce a vision based geo-referencing system that uses pre-existing maps to reduce the long term drift. The system classifies an image according to its environmental content and thereafter matches it to an environmentally classified map over the operational area. This map matching provides a measurement of the absolute location of the UAV, that can easily be incorporated into a sensor fusion framework. Experiments show that the geo-referencing system reduces the long term drift in UAV navigation, enhancing the ability of the UAV to navigate accurately over large areas without the use of GPS.

This paper presents a combined Point Mass Filter (PMF) and Particle Filter (PF), which utilizes the support of the PMF and the high particle density in the PF close to the current estimate. The result is a filter robust to unexpected process events but still with low error covariance. This filter is especially useful for target tracking applications, where target maneuvers suddenly can change unpredictably.

Using prior information about the road network will improve the estimation performance for a road constrained target significantly. Several estimation methods have been proposed to handle the multi-modality that arise in a road target tracking application. One popular filter suitable for this kind of non-linear problems is the Particle Filter, but a major drawback is that the Particle filter requires a large amount of particles as the state dimension increases to maintain a good approximation of the filtering distribution. In this paper a Rao-Blackwellized Particle Filter based approach is proposed to reduce the dimension of the state space in road target tracking applications. Furthermore, it is also shown how prior information about the probability of detection can be used to improve the estimation performance further.

This contribution aims at unifying two recent trends in applied particle filtering (PF). The first trend is the major impact in simultaneous localization and mapping (SLAM) applications, utilizing the FastSLAM algorithm. Thesecond one is the implications of the marginalized particle filter (MPF) or the Rao-Blackwellized particle filter (RBPF) in positioning and tracking applications. Using the standard FastSLAM algorithm, only low-dimensional vehicle modelsare computationally feasible. In this work, an algorithm is introduced which merges FastSLAM and MPF, and the result is an algorithm for SLAM applications, where state vectors of higher dimensions can be used. Results using experimental data from a UAV (helicopter) are presented. The algorithmfuses measurements from on-board inertial sensors (accelerometer and gyro) and vision in order to solve the SLAM problem, i.e., enable navigation over a long period of time.

Detection of spurious features is instrumental in many computer vision applications. The standard approach is feature based, where extracted features are matched between the image frames. This approach requires only vision, but is computer intensive and not yet suitable for real-time applications. We propose an alternative based on algorithms from the statistical fault detection literature. It is based on image data and an inertial measurement unit (IMU). The principle of analytical redundancy is applied to batches of measurements from a sliding time window. The resulting algorithm is fast and scalable, and requires only feature positions as inputs from the computer vision system. It is also pointed out that the algorithm can be extended to also detect nonstationary features (moving targets for instance). The algorithm is applied to real data from an unmanned aerial vehicle in a navigation application.

Computational complexity is a major concern for practical use of the versatile particle filter (PF) for nonlinear filtering applications. Previous work to mitigate the inherent complexity includes the marginalized particle filter (MPF), with the fastSLAM algorithm as one important case. MPF utilizes a linear Gaussian sub-structure in the problem, where the Kalman filter (KF) can be applied. While this reduces the state dimension in the PF, the present work aims at reducing the sampling rate of the PF. The algorithm is derived for a class of models with linear Gaussian dynamic model and two multirate sensors, with different sampling rates, one slow with a nonlinear and/or non-Gaussian measurement relation and one fast with a linear Gaussian measurement relation. For this case, the KF is used to process the information from the fast sensor and the information from the slow sensor is processed using the PF. The problem formulation covers the important special case of fast dynamics and one slow sensor, which appears in many navigation and tracking problems.

Fault detection based on comparing a batch of data with a model of the system using the generalized likelihood ratio test is considered. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. There are two standard approaches to this problem. One is based on a parity space, where the influence ofinitial state is removed by projection, and the other on using prior information obtained by Kalman filtering past data. A new idea of anti-causal Kalman filtering in the present data batch is introduced and compared to the previous methods. An efficient parameterization of incipient faults is given. It is shown in simulations of torque disturbances on a DCmotor that efficient fault profile parameterization and using smoothed estimates of the initial state increase performance considerably. (Copyright 2006 IFAC)

The Generalized Likelihood Ratio (GLR) test for fault detection as derived by Willsky and Jones is a recursive method to detect additive changes in linear systems in a Kalman filter framework. Here, we evaluate the GLR test on a sliding window and compare it to stochastic parity space approaches. Robust fault detection defined as being insensitive to faults in the signal space is also studied in the GLR framework.

Centralized control and coordination of the connections in a wireless network is not possible in practice. To keep the delay from measurement instants to actuating the decisions, distributed control is required. This paper focuses on the uplink (from mobiles to base stations) and discusses distributing the decision of when and when not to transmit data (distributed scheduling) to the mobiles. The scheme, uplink transmission timing, utilizes mobile transmitter power control feedback from the base station receiver to determine whether the channel is favorable or not compared to the average channel condition. Thereby, the battery consumption and disturbing power to other connections are reduced. The algorithm can be described as a feedback control system. Some transient behaviors are analyzed using systems theory, and supported by wireless network simulations of a system with a WCDMA (Wideband Code Division Multiple Access) radio interface as in most 3G systems.

This thesis deals with the problem of detecting faults in an environment where the measurements are affected by additive noise. To do this, a residual sensitive to faults is derived and statistical methods are used to distinguish faults from noise. Standard methods for fault detection compare a batch of data with a model of the system using the generalized likelihood ratio. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. One method to handle this is the parity-space method which solves the problem by removing the influence of the initial state using a projection.

In this thesis, the case where prior knowledge about the initial state is available is treated. This can be obtained for example from a Kalman filter. Combining the prior estimate with a minimum variance estimate from the data batch results in a smoothed estimate. The influence of the estimated initial state is then removed. It is also shown that removing the influence of the initial state by an estimate from the data batch will result in the parity-space method. To model slowly changing faults, an efficient parameterization using Chebyshev polynomials is given.

The methods described above have been applied to an Inertial Measurement Unit, IMU. The IMU usually consists of accelerometers and gyroscopes, but has in this work been extended with a magnetometer. Traditionally, the IMU has been used to estimate position and orientation of airplanes, missiles etc. Recently, the size and cost has decreased making it possible to use IMU:s for applications such as augmented reality and body motion analysis. Since a magnetometer is very sensitive to disturbances from metal, such disturbances have to be detected. Detection of the disturbances makes compensation possible. Another topic covered is the fundamental question of observability for fault inputs. Given a fixed or linearly growing fault, conditions for observability are given.

The measurements from the IMU show that the noise distribution of the sensors can be well approximated with white Gaussian noise. This gives good correspondence between practical and theoretical results when the sensor is kept at rest. The disturbances for the IMU can be approximated using smooth functions with respect to time. Low rank parameterizations can therefore be used to describe the disturbances. The results show that the use of smoothing to obtain the initial state estimate and parameterization of the disturbances improves the detection performance drastically.

The problem of estimating heading is central in the indoor positioning problem based on mea- surements from inertial measurement and magnetic units. Integrating rate of turn angular rate gives the heading with unknown initial condition and a linear drift over time, while the magnetometer gives absolute heading, but where long segments of data are useless in prac- tice because of magnetic disturbances. A basic Kalman filter approach with outlier rejection has turned out to be difficult to use with high integrity. Here, we propose an approach based on convex optimization, where segments of good magnetometer data are separated from disturbed data and jointly fused with the yaw rate measurements. The optimization framework is flexible with many degrees of freedom in the modeling phase, and we outline one design. A recursive solution to the optimization is derived, which has a computational complexity comparable to the simplest possible Kalman filter. The performance is evaluated using data from a handheld smartphone for a large amount of indoor trajectories, and the result demonstrates that the method effectively resolves the magnetic disturbances.

Airborne surveillance systems equipped with a vision/infrared camera require good knowledge about the position and orientation of the camera for successful tracking of ground targets. In particular, this is essential when incorporating prior information, like road maps, that is expressed relative a global reference system. Usually, it is possible to obtain good positioning with inertial/satellite navigation systems, but estimating the orientation is generally more difficult. It might be possible to use SLAM (Simultaneous Localization and Mapping) or image registration approaches to support the navigation system, but not always since such approaches require stable features in the images. In this paper the problem of simultaneous orientation error estimation and road target tracking is considered by assuming that the target is constrained to a known road network. A particle filter approach is proposed and it is shown that the result of this filter is close to the performance of the ideal case where the orientation error is perfectly known. However, the performance depends on how informative the road path is and in rare cases the orientation error is unobservable.

A UAV navigation system relying on GPS is vulnerable to signal failure, making a drift free backup system necessary. We introduce a vision based geo-referencing system that uses pre-existing maps to reduce the long term drift. The system classifies an image according to its environmental content and thereafter matches it to an environmentally classified map over the operational area. This map matching provides a measurement of the absolute location of the UAV, that can easily be incorporated into a sensor fusion framework. Experiments show that the geo-referencing system reduces the long term drift in UAV navigation, enhancing the ability of the UAV to navigate accurately over large areas without the use of GPS.

Using the parity-space approach, a residual is formed by applying a projection to a batch of observed data and this is a well established approach to fault detection. Based on a stochastic state space model, the parity-space residual can be put into a stochastic framework where conventional hypothesis tests apply. In an on-line application, the batch of data corresponds to a sliding window and in this contribution we develop an improved on-line algorithm that extends the parity-space approach by taking prior information from previous observations into account. For detection of faults, the Generalized Likelihood Ratio (GLR) test is used. This framework allows for including prior information about the initial state, yielding a test statistic with a significantly higher sensitivity to faults. Another key advantage with this approach is that it can be extended to nonlinear systems using an arbitrary nonlinear filter for state estimation, and a linearized model around a nominal state trajectory in the sliding window. We demonstrate the algorithm on data from an Inertial Measurement Unit (IMU), where small and incipient magnetic disturbances are detected using a nonlinear system model.

The contribution of this paper is an experimentally verified real-time algorithm for combined probabilistic search and track using multiple unmanned aerial vehicles (UAVs). Distributed data fusion provides a framework for multiple sensors to search for a target and accurately estimate its position. Vision based sensing is employed, using fixed downward-looking cameras. These sensors are modeled to include vehicle state uncertainty and produce an estimate update regardless of whether the target is detected in the frame or not. This allows for a single framework for searching or tracking, and requires non-linear representations of the target position probability density function (PDF) and the sensor model. While a grid-based system for Bayesian estimation was used for the flight demonstrations, the use of a particle filter solution has also been examined.

Multi-aircraft flight experiments demonstrate vision-based localization of a stationary target with estimated error covariance on the order of meters. This capability for real-time distributed estimation will be a necessary component for future research in information-theoretic control.

Computational complexity is a major concern for practical use of the versatile particle filter (PF) for nonlinear filtering applications. Previous work to mitigate the inherent complexity includes the marginalized particle filter (MPF), with the fastSLAM algorithm as one important case. MPF utilizes a linear Gaussian sub-structure in the problem, where the Kalman filter (KF) can be applied. While this reduces the state dimension in the PF, the present work aims at reducing the sampling rate of the PF. The algorithm is derived for a class of models with linear Gaussian dynamic model and two multirate sensors, with different sampling rates, one slow with a nonlinear and/or non-Gaussian measurement relation and one fast with a linear Gaussian measurement relation. For this case, the KF is used to process the information from the fast sensor and the information from the slow sensor is processed using the PF. The problem formulation covers the important special case of fast dynamics and one slow sensor, which appears in many navigation and tracking problems.

In the CDIO-project course in Automatic Control, an Autonomous Unmanned Aerial vehicle (UAV) is constructed, utilizing an existing radio controlled model aircraft. By adding an inertial sensor measuring acceleration and rotation, together with a Global Positioning System (GPS) sensor, the aim is to construct an accurate positioning system. This is used by an on board computer to calculate rudder control signals to a set of DC-servos in order to follow a predefined way-point trajectory. The project involves 17 students, which is roughly three times as big as previous projects, and it comprises both positioning, control, and hardware design. Since the project is still ongoing some preliminary results and conclusions are presented.

In the Conceive – Design – Implement - Operate (CDIO) project course in automatic control, an autonomous unmanned aerial vehicle (UAV) is constructed, utilising an existing radio controlled model aircraft. By adding an inertial sensor that measures acceleration and rotation, together with a Global Positioning System (GPS) sensor, the aim is to construct an accurate positioning system. This is used by an onboard computer to calculate control surface signals to a set of servos in order to follow a predefined way-point trajectory. The project involves 17 students and comprises both positioning, control and hardware design. The main pedagogical goal is for students to apply their theoretical knowledge within a project framework in order to improve important aspects of their engineering skills in a realistic manner.

The Generalized Likelihood Ratio (GLR) test for fault detection as derived by Willsky and Jones is a recursive method to detect additive changes in linear systems in a Kalman filter framework. Here, we evaluate the GLR test on a sliding window and compare it to stochastic parity space approaches. Robust fault detection defined as being insensitive to faults in the signal space is also studied in the GLR framework.

In wireless network uplink communications, there is a trade off between transmission coordination to avoid overload situations, and distributed transmission decisions to adapt to fast channel variations. Here, uplink transmission timing (UTT) is proposed as a scheme to allow some load control support, while transmitting mainly when the channel is favorable. It utilizes channel state feedback in the form of power control commands, which already are available in the system. Simulations illustrate the transmission timing behavior, and also indicate that UTT is a power and intercell interference efficient scheme to transport data compared to traditional dedicated channels with continuous transmissions and to schemes where transmission decisions are random.