liu.seSearch for publications in DiVA
Change search
Link to record
Permanent link

Direct link
BETA
Sjanic, Zoran
Publications (10 of 18) Show all publications
Forsling, R., Sjanic, Z., Gustafsson, F. & Hendeby, G. (2019). Consistent Distributed Track Fusion Under Communication Constraints. In: Proceedings of the 22nd International Conference on Information Fusion (FUSION): . Paper presented at 22nd International Conference on Information Fusion (FUSION), Ottawa, Canada, July 2-5, 2019.
Open this publication in new window or tab >>Consistent Distributed Track Fusion Under Communication Constraints
2019 (English)In: Proceedings of the 22nd International Conference on Information Fusion (FUSION), 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the problem of retrieving consistentestimates in a distributed network where the communication between the nodes is constrained such that only the diagonal elements of the covariance matrix are allowed to be exchanged. Several methods are developed for preserving and/or recovering consistency under the constraints imposed by the communication protocol. The proposed methods are used in conjunction with the covariance intersection method and the estimation performance is evaluated based on information usage and consistency. The results show that among the proposed methods, consistency can be preserved equally well at the transmitting node as at the receiving node.

Keywords
distributed estimation, track fusion, communication constraints, covariance intersection, consistency, consistency preservation
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-159102 (URN)
Conference
22nd International Conference on Information Fusion (FUSION), Ottawa, Canada, July 2-5, 2019
Projects
LINK-SIC
Funder
Vinnova, LINK-SICSwedish Research Council, Scalable Kalman filters
Available from: 2019-07-24 Created: 2019-07-24 Last updated: 2019-09-05Bibliographically approved
Sjanic, Z. (2019). Particle Filtering Approach for Data Association. In: : . Paper presented at 22nd International Conference on Information Fusion, Ottawa, Canada, July 2-5, 2019.
Open this publication in new window or tab >>Particle Filtering Approach for Data Association
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

An initial work has been performed to implement a sequential Monte Carlo method to solve the data association problem. The main motivation is to overcome the incorrect association when the state estimates are inaccurate. The solution is based on modeling the data association as a stochastic variable and estimated with a bootstrap particle filter. Two variants of the proposal function are evaluated, one with the uniform distribution over possible associations, and the other one with the distribution depending on the measurements and state estimates. The performance of both proposals is evaluated on the small simulation example, and compared to a purely deterministic approach, Nearest-Neighbour, as well. The obtained initial results are quite promising, and more evaluation and expansion to more examples and real data sets is suggested for the future work.

Keywords
Data Association, Particle Filter, Sequential Monte Carlo
National Category
Engineering and Technology Signal Processing
Identifiers
urn:nbn:se:liu:diva-159327 (URN)
Conference
22nd International Conference on Information Fusion, Ottawa, Canada, July 2-5, 2019
Projects
LINK-SIC
Available from: 2019-08-07 Created: 2019-08-07 Last updated: 2019-08-16Bibliographically approved
Sjanic, Z., Skoglund, M. A. & Gustafsson, F. (2017). EM-SLAM with Inertial/Visual Applications. IEEE Transactions on Aerospace and Electronic Systems, 53(1), 273-285
Open this publication in new window or tab >>EM-SLAM with Inertial/Visual Applications
2017 (English)In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 53, no 1, p. 273-285Article in journal (Refereed) Published
Abstract [en]

The general Simultaneous Localisation and Mapping (SLAM) problem aims at estimating the state of a moving platform simultaneously with building a map of the local environment. There are essentially three classes of algorithms. EKF- SLAM and FastSLAM solve the problem on-line, while Nonlinear Least Squares (NLS) is a batch method. All of them scales badly with either the state dimension, the map dimension or the batch length. We investigate the EM algorithm for solving a generalized version of the NLS problem. This EM-SLAM algorithm solves two simpler problems iteratively, hence it scales much better with dimensions. The iterations switch between state estimation, where we propose an Extended Rauch-Tung-Striebel smoother, and map estimation, where a quasi-Newton method is suggested. The proposed method is evaluated in real experiments and also in simulations on a platform with a monocular camera attached to an inertial measurement unit. It is demonstrated to produce lower RMSE than with a standard Levenberg-Marquardt solver of NLS problem, at a computational cost that increases considerably slower. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
SLAM, Expectation-Maximisation, Sensor Fu- sion, Computer Vision, Inertial Sensors
National Category
Robotics
Identifiers
urn:nbn:se:liu:diva-110371 (URN)10.1109/TAES.2017.2650118 (DOI)000399934000022 ()
Note

Funding agencies: Vinnova Industry Excellence Center LINK-SIC

Available from: 2014-09-09 Created: 2014-09-09 Last updated: 2017-05-18Bibliographically approved
Sjanic, Z. & Skoglund, M. A. (2016). Prediction Error Method Estimation for Simultaneous Localisation and Mapping. In: Proceedings of the 19th International Conference on Information Fusion (FUSION), July 4-8 2016.: . Paper presented at International Conference on Information Fusion (FUSION), Heidelberg, Germany, July 5-8 2016. (pp. 927-934). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Prediction Error Method Estimation for Simultaneous Localisation and Mapping
2016 (English)In: Proceedings of the 19th International Conference on Information Fusion (FUSION), July 4-8 2016., Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 927-934Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a batch estimation method for Simultaneous Localization and Mapping (SLAM) using the Prediction Error Method (PEM). The estimation problem considers landmarks as parameter while treating dynamics using state space models. The gradient needed for parameter estimation is computed recursively using an Extended Kalman Filter (EKF). Results using simulations with a monocular camera and inertial sensors are presented and compared to a Nonlinear Least- Squares (NLS) estimator. The presented method produce both lower RMSE’s and scale better to the batch length. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
SLAM, Optimization
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-130490 (URN)000391273400124 ()
Conference
International Conference on Information Fusion (FUSION), Heidelberg, Germany, July 5-8 2016.
Projects
LINK-SIC
Funder
VINNOVA
Available from: 2016-08-10 Created: 2016-08-10 Last updated: 2017-02-03
Sjanic, Z. & Gustafsson, F. (2015). Navigation and SAR focusing with Map Aiding. IEEE Transactions on Aerospace and Electronic Systems, 51(3), 1652-1663
Open this publication in new window or tab >>Navigation and SAR focusing with Map Aiding
2015 (English)In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 51, no 3, p. 1652-1663Article in journal (Refereed) Published
Abstract [en]

A method for fusing Synthetic Aperture Radar (SAR) images with opticalaerial images is presented. This is done in a navigation framework, where 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 remote sensing, where the SAR image provides one further "colour" channel revealing reflectivity to radio waves. 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, where 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.

Place, publisher, year, edition, pages
IEEE Press, 2015
Keywords
Optimisation, navigation, Synthetic Aperture Radar, image matching, auto-focusing
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-97280 (URN)10.1109/TAES.2015.130397 (DOI)000362015800006 ()
Note

Funding text: Industry Excellence Center, Linkoping Center for Sensor Informatics and Control (LINK-SIC)

Vid tiden för disputation förelåg publikationen endast endast som manuskript

Available from: 2013-09-05 Created: 2013-09-05 Last updated: 2017-12-06
Toss, T., Dammert, P., Sjanic, Z. & Gustafsson, F. (2015). Navigation with SAR and 3D-Map Aiding. In: Proceedings of the 18th International Conference on Information Fusion (Fusion): . Paper presented at 18th International Conference on Information Fusion (Fusion), Washington D.C., USA, July 6-9 2015 (pp. 1505-1510). IEEE
Open this publication in new window or tab >>Navigation with SAR and 3D-Map Aiding
2015 (English)In: Proceedings of the 18th International Conference on Information Fusion (Fusion), IEEE , 2015, p. 1505-1510Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a method for matching spotlight Synthetic Aperture Radar (SAR) images with a georeferenced 3D-map as means for navigational aid. A hypothesis of the flying platform's absolute position, velocity and direction - which later can be used to correct the inertial navigation system - is attained by image matching and optimization. A projective model with 6 DoF is used to create a simulated SAR image from a 3D map. The parameters of the projective model represents the most important of the platform's navigation state, and these are adjusted by Chamfer matching the captured SAR image to simulated ones. The performance is demonstrated on real spotlight SAR images and 3D-map, and the error is shown to be only a few pixels in average, which in our case is about 3 meters.

Place, publisher, year, edition, pages
IEEE, 2015
Keywords
image matching; inertial navigation; optimisation; radar imaging; synthetic aperture radar; 3D map aiding; Chamfer matching; SAR images; image matching; inertial navigation system; navigational aid; optimization; projective model; synthetic aperture radar; Image edge detection; Navigation; Optimization; Radar antennas; Radar imaging; Synthetic aperture radar
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-122005 (URN)978-0-9824-4386-6 (ISBN)
Conference
18th International Conference on Information Fusion (Fusion), Washington D.C., USA, July 6-9 2015
Projects
LINK-SIC
Available from: 2015-10-14 Created: 2015-10-14 Last updated: 2016-05-03Bibliographically approved
Sjanic, Z. & Gustafsson, F. (2015). Simultaneous Navigation and Synthetic Aperture Radar Focusing. IEEE Transactions on Aerospace and Electronic Systems, 51(2), 1253-1266
Open this publication in new window or tab >>Simultaneous Navigation and Synthetic Aperture Radar Focusing
2015 (English)In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 51, no 2, p. 1253-1266Article in journal (Refereed) Published
Abstract [en]

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 platforms 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-120294 (URN)10.1109/TAES.2015.120820 (DOI)000356969500037 ()
Note

Funding Agencies|Industry Excellence Center LINK-SIC

Available from: 2015-07-24 Created: 2015-07-24 Last updated: 2018-01-11
Sjanic, Z., Gunnarsson, F., Fritsche, C. & Gustafsson, F. (2014). Cellular Network Non-Line-of-Sight Reflector Localisation Based on Synthetic Aperture Radar Methods. IEEE Transactions on Antennas and Propagation, 62(4), 2284-2287
Open this publication in new window or tab >>Cellular Network Non-Line-of-Sight Reflector Localisation Based on Synthetic Aperture Radar Methods
2014 (English)In: IEEE Transactions on Antennas and Propagation, ISSN 0018-926X, E-ISSN 1558-2221, Vol. 62, no 4, p. 2284-2287Article in journal (Refereed) Published
Abstract [en]

The dependence of radio signal propagation on the environment is  well known, and both statistical and deterministic methods have been presented in the literature. Such methods are either based on randomised or actual reflectors of radio signals. In this work, we instead aim at estimating the location of the reflectors based on geo-localised radio channel impulse reponse measurements and using methods from synthetic aperture radar (SAR). Radio channel data measurements from 3GPP E-UTRAN have been used to verify the usefulness of the proposed approach. The obtained images show that  the estimated reflectors are well correlated with the aerial map of the environment. Also, which part of the trajectory contributed to different reflectors have been estimated with promising results.

Place, publisher, year, edition, pages
IEEE Press, 2014
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-97279 (URN)10.1109/TAP.2014.2300531 (DOI)000334744700057 ()
Available from: 2013-09-09 Created: 2013-09-05 Last updated: 2017-12-06
Skoglund, M., Sjanic, Z. & Gustafsson, F. (2013). Initialisation and Estimation Methods for Batch Optimisation of Inertial/Visual SLAM.
Open this publication in new window or tab >>Initialisation and Estimation Methods for Batch Optimisation of Inertial/Visual SLAM
2013 (English)Report (Other academic)
Abstract [en]

Simultaneous Localisation and Mapping (SLAM) denotes the problem of jointly localizing a moving platform and mapping the environment. This work studies the SLAM problem using a combination of inertial sensors, measuring the platform's accelerations and angular velocities, and a monocular camera observing the environment. We formulate the SLAM problem on a nonlinear least squares (NLS) batch form, whose solution provides a smoothed estimate of the motion and map. The NLS problem is highly nonconvex in practice, so a good initial estimate is required. We propose a multi-stage iterative procedure, that utilises the fact that the SLAM problem is linear if the platform's rotations are known. The map is initialised with camera feature detections only, by utilising feature tracking and clustering of  feature tracks. In this way, loop closures are automatically detected. The initialization method and subsequent NLS refinement is demonstrated on both simulated and real data.

Publisher
p. 15
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3065
Keywords
Simultaneous localisation and mapping, optimisation, inertial measurement unit, monocular camera
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-97278 (URN)LiTH-ISY-R-3065 (ISRN)
Available from: 2013-09-09 Created: 2013-09-05 Last updated: 2017-01-19Bibliographically approved
Sjanic, Z. (2013). Navigation and Mapping for Aerial Vehicles Based on Inertial and Imaging Sensors. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Navigation and Mapping for Aerial Vehicles Based on Inertial and Imaging Sensors
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Small and medium sized Unmanned Aerial Vehicles (UAV) are today used in military missions, and will in the future find many new application areas such as surveillance for exploration and security. To enable all these foreseen applications, the UAV's have to be cheap and of low weight, which restrict the sensors that can be used for navigation and surveillance. This thesis investigates several aspects of how fusion of navigation and imaging sensors can improve both tasks at a level that would require much more expensive sensors with the traditional approach of separating the navigation system from the applications. The core idea is that vision sensors can support the navigation system by providing odometric information of the motion, while the navigation system can support the vision algorithms, used to map the surrounding environment, to be more efficient. The unified framework for this kind of approach is called  Simultaneous Localisation and Mapping (SLAM) and it will be applied here to inertial sensors, radar and optical camera.

Synthetic Aperture Radar (SAR) uses a radar and the motion of the UAV to provide an image of the microwave reflectivity of the ground. SAR images are a good complement to optical images, giving an all-weather surveillance capability, but they require an accurate navigation system to be focused which is not the case with typical UAV sensors. However, by using the inertial sensors, measuring UAV's motion, and information from the SAR images, measuring how image quality depends on the UAV's motion, both higher navigation accuracy and, consequently, more focused images can be obtained. The fusion of these sensors can be performed in both batch and sequential form. For the first approach, we propose an optimisation formulation of the navigation and focusing problem while the second one results  in a filtering approach. For the optimisation method the measurement of the focus in processed SAR images is performed with the image entropy and with an image matching approach, where SAR images are matched to the map of the area. In the proposed filtering method the motion information is estimated from the raw radar data and it corresponds to the time derivative of the range between UAV and the imaged scene, which can be related to the motion of the UAV.

Another imaging sensor that has been exploited in this framework is  an ordinary optical camera. Similar to the SAR case, camera images and inertial sensors can also be used to support the navigation estimate and simultaneously build a three-dimensional map of the observed environment, so called inertial/visual SLAM. Also here, the problem is posed in optimisation framework leading to batch Maximum Likelihood (ML) estimate of the navigation parameters and the map. The ML problem is solved in both the straight-forward way,  resulting in nonlinear least squares where both map and navigation parameters are considered as parameters, and with the Expectation-Maximisation (EM) approach. In the EM approach, all unknown variables are split into two sets, hidden variables and actual parameters, and in this case the map is considered as parameters and the navigation states are seen as hidden  variables. This split enables the total problem to be solved computationally cheaper then the original ML formulation. Both optimisation problems mentioned above are nonlinear and non-convex requiring good initial solution in order to obtain good parameter estimate. For this purpose a method for initialisation of inertial/visual SLAM is devised where the conditional linear structure of the problem is used to obtain the initial estimate of the parameters. The benefits and performance improvements of the methods are illustrated on both simulated and real data.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. p. 64
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1533
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-97317 (URN)10.3384/diss.diva-97317 (DOI)978-91-7519-553-7 (ISBN)
Public defence
2013-10-18, Visionen, B-huset, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2013-09-30 Created: 2013-09-09 Last updated: 2019-12-03Bibliographically approved
Organisations

Search in DiVA

Show all publications