Confidentiality and Disclosure in Accreditation

The law and the internal policies of accrediting entities have protected the confidentiality of accreditation information, but regulators who rely on accreditation decisions for public purposes are demanding greater access to this information. The litigation involving access to accrediting information is examined

The American Commitment to Public Propoganda

Random set based methods have provided a rigorous Bayesian framework and have been used extensively in the last decade for point object estimation. In this paper, we emphasize that the same methodology offers an equally powerful approach to estimation of so called extended objects, i.e., objects that result in multiple detections on the sensor side. Building upon the analogy between Bayesian state estimation of a single object and random finite set estimation for multiple objects, we give a tutorial on random set methods with an emphasis on multiple extended object estimation. The capabilities are illustrated on a simple yet insightful real life example with laser range data containing several occlusions.CADICSCUA

Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking

In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on a vehicle, and the target state should be represented in a global (absolute) coordinate frame. Then it is important to consider the uncertain location of the vehicle on which the sensor is mounted for MTT. In this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states. Measurements collected by different sensors mounted on multiple vehicles with varying location uncertainty are incorporated sequentially based on the arrival of new sensor measurements. In doing so, targets observed from a sensor mounted on a well-localized vehicle reduce the state uncertainty of other poorly localized vehicles, provided that a common non-empty subset of targets is observed. A low complexity filter is obtained by approximations of the joint sensor-feature state density minimizing the Kullback-Leibler divergence (KLD). Results from synthetic as well as experimental measurement data, collected in a vehicle driving scenario, demonstrate the performance benefits of joint vehicle-target state tracking.Comment: 13 pages, 7 figure

Poisson multi-Bernoulli mixture trackers: continuity through random finite sets of trajectories

The Poisson multi-Bernoulli mixture (PMBM) is an unlabelled multi-target distribution for which the prediction and update are closed. It has a Poisson birth process, and new Bernoulli components are generated on each new measurement as a part of the Bayesian measurement update. The PMBM filter is similar to the multiple hypothesis tracker (MHT), but seemingly does not provide explicit continuity between time steps. This paper considers a recently developed formulation of the multi-target tracking problem as a random finite set (RFS) of trajectories, and derives two trajectory RFS filters, called PMBM trackers. The PMBM trackers efficiently estimate the set of trajectories, and share hypothesis structure with the PMBM filter. By showing that the prediction and update in the PMBM filter can be viewed as an efficient method for calculating the time marginals of the RFS of trajectories, continuity in the same sense as MHT is established for the PMBM filter

Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking

A decentralized Poisson multi-Bernoulli filter is proposed to track multiple vehicles using multiple high-resolution sensors. Independent filters estimate the vehicles' presence, state, and shape using a Gaussian process extent model; a decentralized filter is realized through fusion of the filters posterior densities. An efficient implementation is achieved by parametric state representation, utilization of single hypothesis tracks, and fusion of vehicle information based on a fusion mapping. Numerical results demonstrate the performance.Comment: 14 pages, 5 figure

Avaintekijät onnistuneeseen ja kannattavaan ohjelmistoalihankintaan tietoliikenneteollisuudessa

Tietoliikenneverkkoelementtien valmistajien markkinoilla kilpailu on kiristynyt viime vuosina tiukan markkinatilanteen johdosta. Yritykset joutuvat keskittämään osaamisiaan ydinosaamisalueisiin ja turvautumaan ulkopuolisiin voimiin saadakseen uusimmat tuotteensa kilpailijoitaan aikaisemmin markkinoille. Tämä diplomityö tarkastelee alihankkijoiden käyttöä ohjelmistojen tuotekehityksessä. Työn tavoitteena on tunnistaa tekijöitä, jotka vaikuttavat alihankinnan onnistumiseen ja mitkä ovat alihankintaa hankaloittavia tekijöitä kohdeyrityksessä. Työssä kiinnitetään myös huomiota alihankinnasta aiheutuviin kustannuksiin ja niiden tunnistamiseen sekä kuinka kustannukset voitaisiin ottaa mahdollisimman kattavasti huomioon jo alihankintaa aloitettaessa. Työssä esitellään ulkoistamisen mahdollisuudet ja muodot sekä alihankinnan ominaispiirteet, motivaatiot ja riskit. Myös tämän hetken markkinatilanne ja ohjelmistokehityksen tuotantoprosessit sekä kustannusrakenteet käydään läpi. Kohdeyrityksen tilannetta tarkasteltiin neljällä tapauksella, jotka pohjautuivat henkilöhaastatteluihin. Alihankinta on ollut trendinä viime vuosina ja sitä tullaan käyttämään myös tulevaisuudessa. Saadakseen alihankinnasta parhaan mahdollisen lopputuloksen yrityksen on keskityttävä alihankintatoiminnan hallinnointiin sekä ottamaan huomioon myös oman toimintansa alihankkijan kanssa. Alihankinnan lopputuloksiin voidaan vaikuttaa ennen toiminnan alkua kiinnittäen huomioita oikeisiin asioihin ja seikkoihin

Asymmetric Threat Modeling Using HMMs: Bernoulli Filtering and Detectability Analysis

There is good reason to model an asymmetric threat (a structured action such as a terrorist attack) as an HMM whose observations are cluttered. Within this context, this paper presents two important contributions. The first is a Bernoulli filter that can process cluttered observations and is capable of detecting whether there is an HMM present, and if so, estimate the state of the HMM. The second is an analysis of the problem that, for a given HMM model, is able to make statements regarding the minimum complexity that an HMM would need to involve in order that it be detectable with reasonable fidelity, as well as upper bounds on the level of clutter (expected number of false measurements) and probability of miss of a relevant observation. In a simulation study, the Bernoulli filter is shown to give good performance provided that the probability of observation is larger than the probability of an irrelevant clutter observation. Further, the results show that the longer the delays are between the HMM state transitions, the larger the probability margin must be. The feasibility prediction shows that it is possible to predict the boundary between poor performance and good performance for the Bernoulli filter, i.e., it is possible to predict when the Bernoulli filter will be useful, and when it will not be

Systematic Approach to IMM Mixing for Unequal Dimension States

The interacting multiple model (IMM) estimator outperforms fixed model filters, e.g. the Kalman filter, in scenarios where the targets have periods of disparate behavior. Key to the good performance and low complexity is the mode mixing. Here we propose a systematic approach to mode mixing when the modes have states of different dimensions. The proposed approach is general and encompasses previously suggested solutions. Different mixing approaches are compared, and the proposed methodology is shown to perform very well

Detectability prediction of hidden Markov models with cluttered observation sequences

There is good reason to model an asymmetric threat (a structured action such as a terrorist attack) as an hmm whose observations are cluttered. Recently a Bernoulli filter was presented that can process cluttered observations («transactions») and is capable of detecting if there is an hmm present, and if so, estimate the state of the HMM. An important question in this context is: when is the HMM-in-clutter problem feasible? In other words, what system properties allow for a solvable problem? In this paper we show that, given a Gaussian approximation of the pdf of the log-likelihood, approximate detection error bounds can be derived. These error bounds allow a prediction of the detection performance, i.e. a prediction of the probability of detection given an «operating point» of transaction-level false alarm rate and miss probability. Simulations show that our analysis accurately predicts detectability of such threats. Our purpose here is to make statements about what sort of threats can be detected, and what quality of observations are necessary that this be accomplished