[Show abstract][Hide abstract] ABSTRACT: This paper presents a method for global pose estimation using inertial sensors, monocular vision, and ultra wideband (UWB) sensors. It is demonstrated that the complementary characteristics of these sensors can be exploited to provide improved global pose estimates, without requiring the introduction of any visible infrastructure, such as fiducial markers. Instead, natural landmarks are jointly estimated with the pose of the platform using a simultaneous localization and mapping (SLAM) framework, supported by a small number of easy to hide UWB beacons with known positions. The method is evaluated with data from a controlled indoor experiment, with high precision ground truth. The results show the benefit of the suggested sensor combination, and suggest directions for further work.
[Show abstract][Hide abstract] ABSTRACT: A skew-t variational Bayes filter (STVBF) is applied to indoor positioning with time-of-arrival (TOA) based distance measurements and pedestrian dead reckoning (PDR). The proposed filter accommodates large positive outliers caused by occasional non-line-of-sight (NLOS) conditions by using a skew-t model of measurement errors. Real-data tests using the fusion of inertial sensors based PDR and ultra-wideband based TOA ranging show that the STVBF clearly outperforms the extended Kalman filter (EKF) in positioning accuracy with the computational complexity about three times that of the EKF.
[Show abstract][Hide abstract] ABSTRACT: In this paper, a Bayesian inference technique based on Taylor series
approximation of the logarithm of the likelihood function is presented. The
proposed approximation is devised for the case, where the prior distribution
belongs to the exponential family of distributions. The logarithm of the
likelihood function is linearized with respect to the sufficient statistic of
the prior distribution in exponential family such that the posterior obtains
the same exponential family form as the prior. Similarities between the
proposed method and the extended Kalman filter for nonlinear filtering are
illustrated. Furthermore, an extended target measurement update for target
models where the target extent is represented by a random matrix having an
inverse Wishart distribution is derived. The approximate update covers the
important case where the spread of measurement is due to the target extent as
well as the measurement noise in the sensor.
[Show abstract][Hide abstract] ABSTRACT: The Ensemble Kalman filter (EnKF) is a standard algorithm in oceanography and meteorology, where it has got thousands of citations. It is in these communities appreciated since it scales much better with state dimension n than the standard Kalman filter (KF). In short, the EnKF propagates ensembles with N state realizations instead of mean values and covariance matrices and thereby avoids the computational and storage burden of working on n×n matrices. Perhaps surprising,
very little attention has been devoted to the EnKF in the signal
processing community. In an attempt to change this, we
present the EnKF in a Kalman filtering context. Furthermore,
its application to nonlinear problems is compared to sigma
point Kalman filters and the particle filter, so as to reveal new
insights and improvements for high-dimensional filtering algorithms
in general. A simulation example shows the EnKF
performance in a space debris tracking application.
[Show abstract][Hide abstract] ABSTRACT: A ship's roll dynamics is very sensitive to changes in the loading conditions and a worst-case scenario is the loss of stability. This paper proposes an approach for online estimation of a ship's mass and center of mass. Instead of focusing on a sensor-rich environment where all possible signals on a ship can be measured and a complete model of the ship can be estimated, a minimal approach is adopted. A model of the roll dynamics is derived from a well-established model in literature and it is assumed that only motion measurements from an inertial measurement unit together with measurements of the rudder angle are available. Furthermore, identifia-bility properties and disturbance characteristics of the model are presented. Due to the properties of the model, the parameters are estimated with an iterative instrumental variable approach to mitigate the influence of the disturbances and it uses multiple datasets simultaneously to overcome identifiability issues. Finally, a simulation study is presented to investigate the sensitivity to the initial conditions and it is shown that the sensitivity is low for the desired parameters.
[Show abstract][Hide abstract] ABSTRACT: A ship's roll dynamics is sensitive to the mass and mass distribution. Changes in these physical properties might introduce unpredictable behavior of the ship and a worst-case scenario is that the ship will capsize. In this paper, a recently proposed approach for online estimation of mass and center of mass is validated using experimental data. The experiments were performed using a scale model of a ship in a wave basin. The data were collected in free run experiments where the rudder angle was recorded and the ship's motion was measured using an inertial measurement unit. The motion measurements are used in conjunction with a model of the roll dynamics to estimate the desired properties. The estimator uses the rudder angle measurements together with an instrumental variable method to mitigate the influence of disturbances. The experimental study shows that the properties can be estimated with quite good accuracy but that variance and robustness properties can be improved further.
[Show abstract][Hide abstract] ABSTRACT: This paper experimentally and theoretically investigates the fundamental
bounds on radio localization precision of far-field Received Signal Strength
(RSS) measurements. RSS measurements are proportional to power-flow
measurements time-averaged over periods long compared to the coherence time of
the radiation. Our experiments are performed in a novel localization setup
using 2.4GHz quasi-monochromatic radiation, which corresponds to a mean
wavelength of 12.5cm. These experiments show for the first time that
time-averaged far-field RSS measurements are not independent but
cross-correlated over a spatial region. We experimentally and theoretically
show that the minimum radius of the cross-correlated region approaches the
diffraction limit, which equals half the mean wavelength of the radiation.
Measuring RSS beyond a sampling density of one sample per half the mean
wavelength is shown not to increase localization precision, as the
Root-Mean-Squared-Error (RMSE) converges asymptotically to roughly half the
mean wavelength. This adds to the evidence that the diffraction limit
determines the fundamental lower bound on the RMSE rather than the spread in
independent noise as is usually assumed in Cramer-Rao Lower Bound (CRLB)
analyses on RSS and Time-Of-Flight (TOF) signals. For the first time, we
experimentally validate the theoretical relations between Fisher information,
CRLB and uncertainty, where uncertainty is lower bounded by diffraction as
derived from coherence and speckle theory.
[Show abstract][Hide abstract] ABSTRACT: Micro-Doppler radar signatures have great potential for classifying pedestrians and animals, as well as their motion pattern, in a variety of surveillance applications. Due to the many degrees of freedom involved, real data need to be complemented with accurate simulated radar data to be able to successfully design and test radar signal processing algorithms. In many cases, the ability to collect real data is limited by monetary and practical considerations, whereas in a simulated environment, any desired scenario may be generated. Motion capture (MOCAP) has been used in several works to simulate the human micro-Doppler signature measured by radar; however, validation of the approach has only been done based on visual comparisons of micro-Doppler signatures. This work validates and, more importantly, extends the exploitation of MOCAP data not just to simulate micro-Doppler signatures but also to use the simulated signatures as a source of a priori knowledge to improve the classification performance of real radar data, particularly in the case when the total amount of data is small.
[Show abstract][Hide abstract] ABSTRACT: A local series expansion of a received signal is proposed for computing direction of arrival (DOA) in sensor arrays. The advantages compared to classical DOA estimation methods include general sensor configurations, ultra-slow sampling, small dimension of the arrays, and that it applies for both narrowband and wideband signals without prior knowledge of the signals. This makes the method well suited for DOA estimation in sensor
networks where size and energy consumption have to be small. We generalize the common far-field assumption of the target to also include the near-field, which enables target tracking using a network of sensor arrays in one framework.
[Show abstract][Hide abstract] ABSTRACT: Visual animal tracking is a challenging problem generally requiring extended target models, group tracking and
handling of clutter and missed detections. Furthermore, the dolphin tracking problem we consider includes basin constraints, shadows, limited field of view and rapidly changing light conditions. We describe the whole pipeline of a solution based on a ceiling-mounted fisheye camera that includes foreground segmentation and observation extraction in each image, followed by a target tracking framework. A novel contribution is a potential
field model of the basin edges as a part of the motion model, that provides a robust prediction of the dolphin trajectories in phases with long segments of missed detections. The overall performance on real data is quite promising.
[Show abstract][Hide abstract] ABSTRACT: A method for fusing synthetic aperture radar (SAR) images with optical aerial images is presented. This is done in a navigation framework, in which the absolute position and orientation of the flying platform, as computed from the inertial navigation system, is corrected based on the aerial image coordinates taken as ground truth. The method is suitable for new low-price SAR systems for small unmanned vehicles. The primary application is surveillance, and to some extent it can be applied to remote sensing, where the SAR image provides complementary information by revealing reflectivity to microwave frequencies. The method is based on first applying an edge detection algorithm to the images and then optimising the most important navigation states by matching the two binary images. To get a measure of the estimation uncertainty, we embed the optimisation in a least squares framework, in which an explicit method to estimate the (relative) size of the errors is presented. The performance is demonstrated on real SAR and aerial images, leading to an error of only a few pixels (around 4 m in our case), which is a quite satisfactory performance for applications like surveillance and navigation.
No preview · Article · Jul 2015 · IEEE Transactions on Aerospace and Electronic Systems
[Show abstract][Hide abstract] ABSTRACT: Filtering and smoothing algorithms for linear discrete-time state-space
models with skewed and heavy-tailed measurement noise are presented. The
algorithms use a variational Bayes approximation of the posterior distribution
of models that have normal prior and skew-t-distributed measurement noise. The
proposed filter and smoother are compared with conventional low-complexity
alternatives in a simulated pseudorange positioning scenario. In the
simulations the proposed methods achieve better accuracy than the alternative
methods, the computational complexity of the filter being roughly 5 to 10 times
that of the Kalman filter.
Full-text · Article · May 2015 · IEEE Signal Processing Letters
[Show abstract][Hide abstract] ABSTRACT: The discrete time general state-space model is a flexible framework to deal with the nonlinear and/or non-Gaussian time series problems. However, the associated (Bayesian) inference problems are often intractable. Additionally, for many applications of interest, the inference solutions are required to be recursive over time. The particle filter (PF) is a popular class of Monte Carlo based numerical methods to deal with such problems in real time. However, PF is known to be computationally expensive and does not scale well with the problem dimensions. If a part of the state space is analytically tractable conditioned on the remaining part, the Monte Carlo based estimation is then confined to a space of lower dimension, resulting in an estimation method known as the Rao-Blackwellized particle filter (RBPF).
In this chapter, we present a brief review of Rao-Blackwellized particle filtering. Especially, we outline a set of popular conditional tractable structures admitting such Rao-Blackwellization in practice. For some special and/or relatively new cases, we also provide reasonably detailed descriptions.We confine our presentation mostly to the practitioners’ point of view.
[Show abstract][Hide abstract] ABSTRACT: Synthetic aperture radar (SAR) equipment is a radar imaging system that can be used to create high-resolution images of a scene by utilizing the movement of a flying platform. Knowledge of the platform???s trajectory is essential to get good and focused images. An emerging application field is real-time SAR imaging using small and cheap platforms where estimation errors in navigation systems imply unfocused images. This contribution investigates a joint estimation of the trajectory and SAR image. Starting with a nominal trajectory, we successively improve the image by optimizing a focus measure and updating the trajectory accordingly. The method is illustrated using simulations using typical navigation performance of an unmanned aerial vehicle. One real data set is used to show feasibility, where the result indicates that, in particular, the azimuth position error is decreased as the image focus is iteratively improved.
No preview · Article · Apr 2015 · IEEE Transactions on Aerospace and Electronic Systems
[Show abstract][Hide abstract] ABSTRACT: We study cooperative sensor network localization in a realistic scenario where 1) the underlying measurement errors more probably follow a non-Gaussian distribution; 2) the measurement error distribution is unknown without conducting massive offline calibrations; and 3) non-line-of-sight identification is not performed due to the complexity constraint and/or storage limitation. The underlying measurement error distribution is approximated parametrically by a Gaussian mixture with finite number of components, and the expectation–conditional maximization (ECM) criterion is adopted to approximate the maximum-likelihood estimator of the unknown sensor positions and an extra set of Gaussian mixture model parameters. The resulting centralized ECM algorithms lead to easier inference tasks and meanwhile retain several convergence properties with a proof of the “space filling” condition. To meet the scalability requirement, we further develop two distributed ECM algorithms where an average consensus algorithm plays an important role for updating the Gaussian mixture model parameters locally. The proposed algorithms are analyzed systematically in terms of computational complexity and communication overhead. Various computer based tests are also conducted with both simulation and experimental data. The results pin down that the proposed distributed algorithms can provide overall good performance for the assumed scenario even under model mismatch, while the existing competing algorithms either cannot work without the prior knowledge of the measurement error statistics or merely provide degraded localization performance when the measurement error is clearly non-Gaussian.
No preview · Article · Mar 2015 · IEEE Transactions on Signal Processing
[Show abstract][Hide abstract] ABSTRACT: Prediction and filtering of continuous-time stochastic processes often require a solver of a continuous-time differential Lyapunov equation (CDLE), for example the time update in the Kalman filter. Even though this can be recast into an ordinary differential equation (ODE), where standard solvers can be applied, the dominating approach in Kalman filter applications is to discretize the system and then apply the discrete-time difference Lyapunov equation (DDLE). To avoid problems with stability and poor accuracy, oversampling is often used. This contribution analyzes over-sampling strategies, and proposes a novel low-complexity analytical solution that does not involve oversampling. The results are illustrated on Kalman filtering problems in both linear and nonlinear systems.
No preview · Article · Mar 2015 · IEEE Transactions on Automatic Control
[Show abstract][Hide abstract] ABSTRACT: This paper presents a data-driven receding horizon fault estimation method
for additive actuator and sensor faults in unknown linear time-invariant
systems, with enhanced robustness to stochastic identification errors.
State-of-the-art methods construct fault estimators with identified state-space
models or Markov parameters, but they do not compensate for identification
errors. Motivated by this limitation, we first propose a receding horizon fault
estimator parameterized by predictor Markov parameters. This estimator provides
(asymptotically) unbiased fault estimates as long as the subsystem from faults
to outputs has no unstable transmission zeros. When the identified Markov
parameters are used to construct the above fault estimator, zero-mean
stochastic identification errors appear as model uncertainty multiplied with
unknown fault signals and online system inputs/outputs (I/O). Based on this
fault estimation error analysis, we formulate a mixed-norm problem for the
offline robust design that regards online I/O data as unknown. An alternative
online mixed-norm problem is also proposed that can further reduce estimation
errors when the online I/O data have large amplitudes, at the cost of increased
computational burden. Based on a geometrical interpretation of the two proposed
mixed-norm problems, systematic methods to tune the user-defined parameters
therein are given to achieve desired performance trade-offs. Simulation
examples illustrate the benefits of our proposed methods compared to recent
[Show abstract][Hide abstract] ABSTRACT: In this paper, an instrumental variable (IV) method for estimating the mass and center of mass (CM) of a ship using IMU data has been further investigated. Here, this IV method, which was proposed in an earlier paper, has been analyzed from a closed-loop point of view. This new perspective reveals the properties of the system and dependencies of the signals used in the estimation procedure. Due to similarities with closed-loop identification, previous results in the closed-loop identification field have been used as an inspiration to improve the IV estimator. Since the roll dynamics of a ship is well described by a pendulum model, a pendulum experiment has been carried out to validate the performance both of the original and the improved IV estimators. The experiments gave good results for the improved IV estimator with significantly lower variances and relative errors than the previous IV estimator.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we aim to relate different Bayesian Cramér-Rao bounds which appear in the discrete-time nonlinear filtering literature in a single framework. A comparative theoretical analysis of the bounds is provided in order to relate their tightness. The results can be used to provide a lower bound on the mean square error in nonlinear filtering. The findings are illustrated and verified by numerical experiments where the tightness of the bounds are compared.