352 research outputs found
Radar networks: A review of features and challenges
Networks of multiple radars are typically used for improving the coverage and
tracking accuracy. Recently, such networks have facilitated deployment of
commercial radars for civilian applications such as healthcare, gesture
recognition, home security, and autonomous automobiles. They exploit advanced
signal processing techniques together with efficient data fusion methods in
order to yield high performance of event detection and tracking. This paper
reviews outstanding features of radar networks, their challenges, and their
state-of-the-art solutions from the perspective of signal processing. Each
discussed subject can be evolved as a hot research topic.Comment: To appear soon in Information Fusio
Joint Registration and Fusion of an Infra-Red Camera and Scanning Radar in a Maritime Context
The number of nodes in sensor networks is continually increasing, and maintaining accurate track estimates inside their common surveillance region is a critical necessity. Modern sensor platforms are likely to carry a range of different sensor modalities, all providing data at differing rates, and with varying degrees of uncertainty. These factors complicate the fusion problem as multiple observation models are required, along with a dynamic prediction model. However, the problem is exacerbated when sensors are not registered correctly with respect to each other, i.e. if they are subject to a static or dynamic bias. In this case, measurements from different sensors may correspond to the same target, but do not correlate with each other when in the same Frame of Reference (FoR), which decreases track accuracy. This paper presents a method to jointly estimate the state of multiple targets in a surveillance region, and to correctly register a radar and an Infrared Search and Track (IRST) system onto the same FoR to perform sensor fusion. Previous work using this type of parent-offspring process has been successful when calibrating a pair of cameras, but has never been attempted on a heterogeneous sensor network, nor in a maritime environment. This article presents results on both simulated scenarios and a segment of real data that show a significant increase in track quality in comparison to using incorrectly calibrated sensors or single-radar only
Novel methods for multi-target tracking with applications in sensor registration and fusion
Maintaining surveillance over vast volumes of space is an increasingly important
capability for the defence industry. A clearer and more accurate picture of a surveillance region could be obtained through sensor fusion between a network of sensors.
However, this accurate picture is dependent on the sensor registration being resolved. Any inaccuracies in sensor location or orientation can manifest themselves
into the sensor measurements that are used in the fusion process, and lead to poor
target tracking performance. Solutions previously proposed in the literature for the
sensor registration problem have been based on a number of assumptions that do
not always hold in practice, such as having a synchronous network and having small,
static registration errors. This thesis will propose a number of solutions to resolving
the sensor registration and sensor fusion problems jointly in an efficient manner.
The assumptions made in previous works will be loosened or removed, making the
solutions more applicable to problems that we are likely to see in practice. The
proposed methods will be applied to both simulated data, and a segment of data
taken from a live trial in the field
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