Merging Components Research Articles

Multisensor multitarget mixture reduction algorithms for tracking

A single-sensor, single-target mixture reduction (MR) data association algorithm is extended for use in multisensor, multitarget tracking situations. MR is extended for tracking an arbitrary number of targets using an arbitrary number of sensors under the assumption that the sensor measurement errors are independent across sensors. Like the single-sensor, single-target MR algorithm, which gives better performance than the probabilistic data association (PDA) filter, the multisensor, multitarget MR extensions give similar improvements in performance compared to the joint PDA (JPDA) and multisensor JPDA (MSJPDA) algorithms. Further, in the formulations for the multisensor and multitarget MR algorithms, the equations for the calculation of the data association probabilities have been put in the same form as for the JPDA, thus allowing previously developed fast JPDA computational techniques to be applicable. N the problem of tracking targets in random clutter, the optimal Bayesian solution leads to Gaussian mixture distributions that consist of an exponentially increasing number of components.1 In practice, the number of components in each mixture is kept in check by approximating it with fewer components. The nearest neighbor (NN) approximation reduces each target's mixture to its largest component.13 The joint probabilistic data association (JPDA) approximation merges the components of each target's mixture into a single Gaussian component.1'4 However, when there are several significant, well-spaced components in the original mixture, important information may be lost by using the NN or JPDA methods. A more flexible approximation method for single-sensor, single-target tracking, known as mixture reduction (MR), has been proposed by Salmond.5 This MR algorithm preserves the mean and covariance of the original mixture of components, successively merging components until the number of components is reduced to some userspecified limit. In this paper, we first review Salmond's MR algorithm for single-sensor tracking of a single target in clutter. We then derive extensions to Salmond's MR algorithm for use in tracking multiple targets and when there are measurements available from multiple sensors. Just as the single-sensor, single-target MR algorithm gives better performance5 than the probabilistic data association (PDA) filter, the multisensor, multitarget MR extensions are shown to give similar improvements in performance as compared to the JPDA and multisensor JPDA (MSJPDA)6 algorithms. Extension of the MR algorithm for tracking of multiple targets is necessary for use in realistic tracking scenarios where there are usually many targets that need to be tracked. Moreover, many current tracking systems have been designed with multiple sensors, thus requiring the further extension to a multisensor algorithm. The paper is organized as follows. In Sec. II, the target dynamics and measurement equations are defined. We review the singlesensor, single-target MR algorithm5 in Sec. Ill, and we extend the algorithm to the multitarget and multisensor cases in Sec. IV and V, respectively. In Sec. VI, simulation results evaluating the performances of the MR extensions are presented. Finally, concluding remarks are given in Sec. VII.

On dwarf elliptical galaxies and the faint blue counts

The nature of the faint blue objects seen in deep images of the sky has been a source of great puzzlement. Their high surface density and weak clustering argues against their being either the progenitors or the merging components of the present-day bright galaxies. The faint blue counts are interpreted as being due to dwarf elliptical galaxies undergoing their initial starburst at z∼1. The starburst epoch is determined by the decline in the UV background, which results in the stably confined photoionized gas in dark haloes of M ∼ 10 9 M ⊙ being able to cool and settle in the centre of the haloes and undergo star formation

Major-minor loop, single-level-masking bubble chip

A major-minor loop chip design is presented which requires one high resolution masking step and no critical mask alignments. This design may therefore be implemented with electron-beam, X-ray, or deep-UV conformable-contact lithography to define the submicron linewidths required in ultra-high density devices. Chips with 20-μm period were fabricated with a layered Au-first, NiFe-second structure in a design which provided 6 percent overall margins; substitution of an improved merge component would allow a 10 percent margin overlap of all functions. Tests of components with 8 μm periods show margins of similar percentage values. Current requirements for the devices are low (10 mA for 8 μm period) so that the designs appear extendable to much higher densities.

Merge Component of Growth of Agricultural Cooperatives

A MERICAN economic history is rich with examples of business firms that have combined in some form to achieve growth and other objectives. As a result, the merger component of growth is becoming an area of increasing interest to economists [6, 10]. Growth by merger, in contrast to growth by internal expansion, is of importance because of its capability of drastically and quickly modifying firm, market, and industry structures. Economists and regulatory agencies are concerned with the changes in concentration, conduct, and performance which may occur. In addition, stockholders and management of firms considering mergers are interested in the factors affecting merger success. This paper bears on these issues but is not explicitly a traditional market structure approach. The purpose of this paper is to discuss empirical findings on the extent of merger activity among agricultural cooperatives, the impact on cooperative growth, relative growth of merging and nonmerging cooperatives, and Markov-chain projections of future growth of farmer cooperatives. Studies on the success of cooperative mergers are scarce.' A study by Mueller found that between 1945 and 1955 nearly one-half of the 102 large dairy cooperatives in his study grew, in part, by merger and acquisition [10]. Cooperatives that used merger in this period grew more rapidly than those growing only by internal expansion. Mueller summarizes his study by suggesting that, if growth is a measure of success, then his findings support the hypothesis that merging dairy cooperatives are more successful than those that have relied on internal growth alone. Another study on dairy firms by Hammond and Cook concurs with Mueller's findings that merging firms showed a higher growth rate than nonmerging firms [4].