Multiple model bootstrap filter for maneuvering target tracking

The extension of the bootstrap filter to the multiple model target tracking problem is considered. Bayesian bootstrap filtering is a very powerful technique since it represents samples by random samples and is therefore not restricted to linear, Gaussian systems, making it ideal for the multiple model problem where very complex densities fan be generated.

Target Detection and Tracking With Heterogeneous Sensors

We present a multimodal detection and tracking algorithm for sensors composed of a camera mounted between two microphones. Target localization is performed on color-based change detection in the video modality and on time difference of arrival (TDOA) estimation between the two microphones in the audio modality. The TDOA is computed by multiband generalized cross correlation (GCC) analysis. The estimated directions of arrival are then postprocessed using a Riccati Kalman filter. The visual and audio estimates are finally integrated, at the likelihood level, into a particle filter (PF) that uses a zero-order motion model, and a weighted probabilistic data association (WPDA) scheme. We demonstrate that the Kalman filtering (KF) improves the accuracy of the audio source localization and that the WPDA helps to enhance the tracking performance of sensor fusion in reverberant scenarios. The combination of multiband GCC, KF, and WPDA within the particle filtering framework improves the performance of the algorithm in noisy scenarios. We also show how the proposed audiovisual tracker summarizes the observed scene by generating metadata that can be transmitted to other network nodes instead of transmitting the raw images and can be used for very low bit rate communication. Moreover, the generated metadata can also be used to detect and monitor events of interest.

Object tracking using SIFT features and mean shift

A scale invariant feature transform (SIFT) based mean shift algorithm is presented for object tracking in real scenarios. SIFT features are used to correspond the region of interests across frames. Meanwhile, mean shift is applied to conduct similarity search via color histograms. The probability distributions from these two measurements are evaluated in an expectation–maximization scheme so as to achieve maximum likelihood estimation of similar regions. This mutual support mechanism can lead to consistent tracking performance if one of the two measurements becomes unstable. Experimental work demonstrates that the proposed mean shift/SIFT strategy improves the tracking performance of the classical mean shift and SIFT tracking algorithms in complicated real scenarios.

Automated detection and tracking of solar magnetic bright points

Magnetic bright points (MBPs) in the internetwork are among the smallest objects in the solar photosphere and appear bright against the ambient environment. An algorithm is presented that can be used for the automated detection of the MBPs in the spatial and temporal domains. The algorithm works by mapping the lanes through intensity thresholding. A compass search, combined with a study of the intensity gradient across the detected objects, allows the disentanglement of MBPs from bright pixels within the granules. Object growing is implemented to account for any pixels that might have been removed when mapping the lanes. The images are stabilized by locating long-lived objects that may have been missed due to variable light levels and seeing quality. Tests of the algorithm, employing data taken with the Swedish Solar Telescope, reveal that approximate to 90 per cent of MBPs within a 75 x 75 arcsec(2) field of view are detected.

Convergence and tracking analysis of a variable normalised LMF (XE-NLMF) algorithm

The least-mean-fourth (LMF) algorithm is known for its fast convergence and lower steady state error, especially in sub-Gaussian noise environments. Recent work on normalised versions of the LMF algorithm has further enhanced its stability and performance in both Gaussian and sub-Gaussian noise environments. For example, the recently developed normalised LMF (XE-NLMF) algorithm is normalised by the mixed signal and error powers, and weighted by a fixed mixed-power parameter. Unfortunately, this algorithm depends on the selection of this mixing parameter. In this work, a time-varying mixed-power parameter technique is introduced to overcome this dependency. A convergence analysis, transient analysis, and steady-state behaviour of the proposed algorithm are derived and verified through simulations. An enhancement in performance is obtained through the use of this technique in two different scenarios. Moreover, the tracking analysis of the proposed algorithm is carried out in the presence of two sources of nonstationarities: (1) carrier frequency offset between transmitter and receiver and (2) random variations in the environment. Close agreement between analysis and simulation results is obtained. The results show that, unlike in the stationary case, the steady-state excess mean-square error is not a monotonically increasing function of the step size. (c) 2007 Elsevier B.V. All rights reserved.

Unidirectional hybridization at a species' range boundary: implications for habitat tracking

Aim Introgressive hybridization between a locally rare species and a more abundant congener can drive population extinction via genetic assimilation, or the replacement of the rare species gene pool with that of the common species. To date, however, few studies have assessed the effects of such processes at the limits of species' distribution ranges. In this study, we have examined the potential for hybridization between range-edge populations of the wintergreen Pyrola minor and sympatric populations of Pyrola grandiflora. Location Qeqertarsuaq, Greenland and Churchill, Manitoba, Canada. Methods Genetic analysis of samples from Greenland and Canada was carried out using a combination of nuclear and chloroplast single nucleotide polymorphisms (SNPs). Results Analysis of nuclear SNPs confirmed hybridization in populations of morphologically intermediate individuals, as well as revealing the existence of cryptic hybrids in ostensibly morphologically pure P. minor populations. Analysis of chloroplast SNPs revealed that this hybridization is unidirectional and suggests that hybrids originate via pollen swamping of P. minor by the more common P. grandiflora. Main conclusions Extensive unidirectional hybridization may lead to the extinction of peripheral populations of P. minor where the two species grow sympatrically. Extinction could occur as a result of genetic assimilation where F1s are fertile, or via the removal of unidirectionally pollinated sterile F1s, or by a combination of these processes. This could compromise the ability of species to respond to climate change via habitat tracking, although the final outcome of these processes may ultimately depend on the rate of global climate change and its effect on the species' distributions. © 2009 Blackwell Publishing Ltd.

Where are they now? Tracking the longitudinal evolution of environmental businesses from the 1990s

In 1991 Bennett published one of the first major publications on the concept of ecopreneurship, business opportunities resulting from the emerging environmental agenda of the late 1980s and early 1990s. Since then a body of literature has developed that explores the idea of the intersection of entrepreneurship with environmentally and socially responsible behaviour. Many of the business cases presented by Bennett represent early adopters of green products, services and emerging eco-markets. Given the current emphasis on the transformation of business practices towards a more sustainable paradigm it is timely to review these 94 early ecopreneurial examples and consider their status two decades on from the original publication. This paper explores the definitions of environmental and social enterprise, and considers the longitudinal survival of these companies and the emerging trends in consolidation and failure of the sampled companies.