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The gains in surveillance information that can be provided by fusion of multiple sensors have been demonstrated in theoretical and practical terms. However, the use of additional sensors quickly reaches a point where the marginal benefits outweigh the marginal costs. In part, this is due to an increasing probability of misassociation. Additionally, the probability of finding an available sensor with...
Networked systems that gather sensor data in order to react to phenomena in their surroundings are faced with a growing need for adaptive behavior to operate in dynamically changing environments. In designing a networked system the data processing chain can be decomposed into functional components. These functional components interact by requesting information they need and fulfilling requests received...
This paper presents a methodology for multi sensor data fusion that uses the accumulation grid idea for the representation of data. A generalized grid framework is introduced to represent measurement data and fusion results in a common way. This allows the definition of standardized prediction and fusion operations and includes the variation between Cartesian and polar grids as well as the extension...
The aim of this paper is to present a multiple object tracking data fusion technique, which fuses radar, image, and ego vehicle odometry. The data are fused at a high level, which leads to reliable and stable tracking results providing also additional features as width estimation and the detection of stationary objects. A ldquorealrdquo application of these algorithms is illustrated on a specific...
Many practical frequency-modulated continuous-wave (FMCW) radars utilize consecutive upchirps and/or downchirps of the same ramp slope to extract the desired range and velocity information of the targets. In this contribution it is demonstrated that consecutive ramp sequences provide only little more information compared to a non-consecutive sequence, but lead to a huge calculation complexity. Additional...
We propose an object classification system that incorporates information from a video camera and an automotive radar. The system implements three processes. The first process is attention selection, in which the radar guides a selection of a small number of candidate images for analysis by the camera and our learning method. In the second process, normalized attention windows are processed by orientation-selective...
In this contribution the problem of tracking convoys moving on the ground by means of airborne radar is discussed. A coherent radar with multi-channel array antenna is considered which makes clutter suppression by space-time adaptive processing (STAP) techniques possible. In addition, a technique to estimate the lateral length component of a convoy is used in addition to the conventional range measurement...
Several nonlinear filtering techniques are investigated for nonlinear tracking problems. Experimental results show that for a weakly nonlinear tracking problem, the extended Kalman filter and the unscented Kalman filter are good choices, while a particle filter should be used for problems with strong nonlinearity. To quantitatively determine the nonlinearity of a nonlinear tracking problem, we propose...
Advancements in sensor technology provide new multi-sensor systems with increasing flexibility. The sensor management process aims to perform sensor actions that support the overall goal of the user of a multi-sensor system. Some sensors can support multiple functions. When the different sensor functions utilise shared resources then the sensor actions must be chosen as a compromise between competing...
This paper defines and relates several important concepts in data fusion and natural language understanding: situation, relation, relationship and context. In data fusion - as in other problem-solving applications - contextual reasoning involves inferring desired information (ldquoproblem variablesrdquo) on the basis of other available information (ldquocontext variablesrdquo). Relevant contexts are...
This paper introduces a new approach to solve sensor management problems. Classically sensor management problems can be well formalized as partially-observed Markov decision processes (POMPD). The original approach developped here consists in deriving the optimal parameterized policy based on a stochastic gradient estimation. We assume in this work that it is possible to learn the optimal policy off-line...
In this paper, the problem of target classification from multiple high range resolution (HRR) radars data is studied. The use of multi-sensor angle-diverse data aims at shortening of the required time before a decision is made, as compared to using single-sensor data. In order to avoid the high-dimensional HRR profile databases, involved in the classic automatic target recognition (ATR) approaches,...
In this paper, considering the problem of collaborative sensor management and data fusion for multitarget tracking, authors propose an altered version of a classical Value Iteration algorithm, one of the most commonly used techniques to calculate the optimal policy for Markov decision processes (MDPs). Dynamic element matching (DEM) algorithms, widely used for reducing harmonic distortion in Digital-to-Analog...
In the belief function theory, the concept of conflict appearing while confronting several expertspsila opinions can serve for many purposes, and in particular it can be used as an indicator of the relative reliability of the experts. The traditional definition of conflict as the basic belief assigned to the empty set during the combination has several issues and in particular it may not adequately...
Maintenance of air tracks requires regular track updates to counter effects of aging. In multi-sensor networks, sensors need to cooperate to optimise the time allocation for track maintenance. Early updates lead to waste of time without improving the track quality, while late updates may result in track drop as the target has moved out of sight of the updating sensor. Earlier research has presented...
A promising line of research attempts to bridge the gap between radar detector and radar tracker by means of considering jointly optimal parameter settings for both of these subsystems. This approach, which can also be considered as a form of feedback from the tracker to the detector results in an adaptive radar system. In the present work, we attempt to move in a direction to fulfill this promise...
A simple ADS-B data fusion method to incorporate ADS-B data in traffic control centres already operative is presented. The implementation of this solution has low-impact on the system with a quite acceptable performance. The goal is to make the fusion with the minimal intervention on the tracking filter already tuned and tested.
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