17,945 research outputs found
Radar-based Feature Design and Multiclass Classification for Road User Recognition
The classification of individual traffic participants is a complex task,
especially for challenging scenarios with multiple road users or under bad
weather conditions. Radar sensors provide an - with respect to well established
camera systems - orthogonal way of measuring such scenes. In order to gain
accurate classification results, 50 different features are extracted from the
measurement data and tested on their performance. From these features a
suitable subset is chosen and passed to random forest and long short-term
memory (LSTM) classifiers to obtain class predictions for the radar input.
Moreover, it is shown why data imbalance is an inherent problem in automotive
radar classification when the dataset is not sufficiently large. To overcome
this issue, classifier binarization is used among other techniques in order to
better account for underrepresented classes. A new method to couple the
resulting probabilities is proposed and compared to others with great success.
Final results show substantial improvements when compared to ordinary
multiclass classificationComment: 8 pages, 6 figure
Observability analysis and optimal sensor placement in stereo radar odometry
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Localization is the key perceptual process closing the loop of autonomous navigation, allowing self-driving vehicles to operate in a deliberate way. To ensure robust localization, autonomous vehicles have to implement redundant estimation processes, ideally independent in terms of the underlying physics behind sensing principles. This paper presents a stereo radar odometry system, which can be used as such a redundant system, complementary to other odometry estimation processes, providing robustness for long-term operability. The presented work is novel with respect to previously published methods in that it contains: (i) a detailed formulation of the Doppler error and its associated uncertainty; (ii) an observability analysis that gives the minimal conditions to infer a 2D twist from radar readings; and (iii) a numerical analysis for optimal vehicle sensor placement. Experimental results are also detailed that validate the theoretical insights.Peer ReviewedPostprint (author's final draft
Integration of LIDAR and IFSAR for mapping
LiDAR and IfSAR data is now widely used for a number of applications, particularly those needing a digital elevation model. The data is often complementary to other data such as aerial imagery and high resolution satellite data. This paper will review the current data sources and the products and then look at the ways in which the data can be integrated for particular applications. The main
platforms for LiDAR are either helicopter or fixed wing aircraft, often operating at low altitudes, a digital camera is frequently included on the platform, there is an interest in using other sensors such as 3 line cameras of hyperspectral scanners. IfSAR is used from satellite platforms, or from aircraft, the latter are more compatible with LiDAR for integration. The paper will examine the advantages and disadvantages of LiDAR and IfSAR for DEM generation and discuss the issues which still need to be dealt with. Examples of applications will be given and particularly those involving the integration of different types of data. Examples will be given from various sources and future trends examined
Mapping ionospheric backscatter measured by the SuperDARN HF radars - Part 1: A new empirical virtual height model
Accurately mapping the location of ionospheric backscatter targets (density irregularities) identified by the Super Dual Auroral Radar Network (SuperDARN) HF radars can be a major problem, particularly at far ranges for which the radio propagation paths are longer and more uncertain. Assessing and increasing the accuracy of the mapping of scattering locations is crucial for the measurement of two-dimensional velocity structures on the small and meso-scale, for which overlapping velocity measurements from two radars need to be combined, and for studies in which SuperDARN data are used in conjunction with measurements from other instruments. The co-ordinates of scattering locations are presently estimated using a combination of the measured range and a model virtual height, assuming a straight line virtual propagation path. By studying elevation angle of arrival information of backscatterred signals from 5 years of data (1997-2001) from the Saskatoon SuperDARN radar we have determined the actual distribution of the backscatter target locations in range-virtual height space. This has allowed the derivation of a new empirical virtual height model that allows for a more accurate mapping of the locations of backscatter targets
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
- …