A batch algorithm for estimating trajectories of point targets using expectation maximization

In this paper, we propose a strategy that is based on expectation maximization for tracking multiple point targets. The algorithm is similar to probabilistic multi-hypothesis tracking (PMHT) but does not relax the point target model assumptions. According to the point target models, a target can generate at most one measurement, and a measurement is generated by at most one target. With this model assumption, we show that the proposed algorithm can be implemented as iterations of Rauch-Tung-Striebel (RTS) smoothing for state estimation, and the loopy belief propagation method for marginal data association probabilities calculation. Using example illustrations with tracks, we compare the proposed algorithm with PMHT and joint probabilistic data association (JPDA) and show that PMHT and JPDA exhibit coalescence when there are closely moving targets whereas the proposed algorithm does not. Furthermore, extensive simulations c comparing the mean optimal subpattern assignment (MOSPA) performance of the algorithm for different scenarios averaged over several Monte Carlo iterations show that the proposed algorithm performs better than JPDA and PMHT. We also compare it to benchmarking algorithm: N-scan pruning based track-oriented multiple hypothesis tracking (TOMHT). The proposed algorithm shows a good tradeoff between computational complexity and the MOSPA performance

Improving Detection of Dim Targets: Optimization of a Moment-based Detection Algorithm

Wide area motion imagery (WAMI) sensor technology is advancing rapidly. Increases in frame rates and detector array sizes have led to a dramatic increase in the volume of data that can be acquired. Without a corresponding increase in analytical manpower, much of these data remain underutilized. This creates a need for fast, automated, and robust methods for detecting dim, moving signals of interest. Current approaches fall into two categories: detect-before-track (DBT) and track-before-detect (TBD) methods. The DBT methods use thresholding to reduce the quantity of data to be processed, making real time implementation practical but at the cost of the ability to detect low signal to noise ratio (SNR) targets without acceptance of a high false alarm rate. TBD methods exploit both the temporal and spatial information simultaneously to make detection of low SNR targets possible, but at the cost of computation time. This research seeks to contribute to the near real time detection of low SNR, unresolved moving targets through an extension of earlier work on higher order moments anomaly detection, a method that exploits both spatial and temporal information but is still computationally efficient and massively parallellizable. The MBD algorithm was found to detect targets comparably with leading TBD methods in 1000th the time

Making It Together

An evaluative study of Creative Families, an arts and mental health partnership between South London Gallery and the Southwark Parental Mental Health Tea

2007 Research and Engineering Annual Report

Selected research and technology activities at NASA Dryden Flight Research Center are summarized. These following activities exemplify the Center's varied and productive research efforts: Developing a Requirements Development Guide for an Automatic Ground Collision Avoidance System; Digital Terrain Data Compression and Rendering for Automatic Ground Collision Avoidance Systems; Nonlinear Flutter/Limit Cycle Oscillations Prediction Tool; Nonlinear System Identification Using Orthonormal Bases: Application to Aeroelastic/Aeroservoelastic Systems; Critical Aerodynamic Flow Feature Indicators: Towards Application with the Aerostructures Test Wing; Multidisciplinary Design, Analysis, and Optimization Tool Development Using a Genetic Algorithm; Structural Model Tuning Capability in an Object-Oriented Multidisciplinary Design, Analysis, and Optimization Tool; Extension of Ko Straight-Beam Displacement Theory to the Deformed Shape Predictions of Curved Structures; F-15B with Phoenix Missile and Pylon Assembly--Drag Force Estimation; Mass Property Testing of Phoenix Missile Hypersonic Testbed Hardware; ARMD Hypersonics Project Materials and Structures: Testing of Scramjet Thermal Protection System Concepts; High-Temperature Modal Survey of the Ruddervator Subcomponent Test Article; ARMD Hypersonics Project Materials and Structures: C/SiC Ruddervator Subcomponent Test and Analysis Task; Ground Vibration Testing and Model Correlation of the Phoenix Missile Hypersonic Testbed; Phoenix Missile Hypersonic Testbed: Performance Design and Analysis; Crew Exploration Vehicle Launch Abort System-Pad Abort-1 (PA-1) Flight Test; Testing the Orion (Crew Exploration Vehicle) Launch Abort System-Ascent Abort-1 (AA-1) Flight Test; SOFIA Flight-Test Flutter Prediction Methodology; SOFIA Closed-Door Aerodynamic Analyses; SOFIA Handling Qualities Evaluation for Closed-Door Operations; C-17 Support of IRAC Engine Model Development; Current Capabilities and Future Upgrade Plans of the C-17 Data Rack; Intelligent Data Mining Capabilities as Applied to Integrated Vehicle Health Management; STARS Flight Demonstration No. 2 IP Data Formatter; Space-Based Telemetry and Range Safety (STARS) Flight Demonstration No. 2 Range User Flight Test Results; Aerodynamic Effects of the Quiet Spike(tm) on an F-15B Aircraft; F-15 Intelligent Flight Controls-Increased Destabilization Failure; F-15 Integrated Resilient Aircraft Control (IRAC) Improved Adaptive Controller; Aeroelastic Analysis of the Ikhana/Fire Pod System; Ikhana: Western States Fire Missions Utilizing the Ames Research Center Fire Sensor; Ikhana: Fiber-Optic Wing Shape Sensors; Ikhana: ARTS III; SOFIA Closed-Door Flutter Envelope Flight Testing; F-15B Quiet Spike(TM) Aeroservoelastic Flight Test Data Analysis; and UAVSAR Platform Precision Autopilot Flight Results

Kansakunnan oma selfiekeppi? Poliittisen henkilöbrändin rakentuminen Twitterissä

Twitter julkisena palveluna mahdollistaa suoran viestinnän laajalle yleisölle. Poliittiselle henkilölle Twitter onkin hyvä väline vuorovaikutukseen kansalaisten kanssa, ja se edesauttaa oman brändin rakentamista. Twitterissä on mahdollista myös kontrolloida sanomaansa ja itsestään jakamaansa tietoa, jolloin omaa brändiään voi pyrkiä rakentamaan tietynlaiseksi. Tässä tutkielmassa tarkastellaankin, minkälaisia brändin rakennusaineita Alexander Stubb tarjosi pääministerikaudellaan Twitterissä. Tutkielman tavoitteena on vastata tutkimusongelmaamme, joka on: Millainen brändi Alexander Stubbille rakentuu hänen twiiteissään ja mistä tekijöistä tämä brändi syntyy. Ongelman selvittämiseksi tutkielmassa vastataan kahteen tutkimuskysymykseen: 1. Minkälaista sisältöä Stubbin twiitteihin kuuluu? ja 2. Mitä rakennusaineita twiitit tarjoavat Stubbin henkilöbrändille? Tutkimuksemme aineistona on 223 Stubbin twiittiä ja 203 retwiittiä. Yleiskuvan luomiseksi Alexander Stubbin twiiteistä ja näiden pohjalta syntyvästä brändistä päädyimme käyttämään tutkimuksessamme laadullista aineiston analyysimenetelmää, sisällönanalyysiä. Laadullisen analyysin ohella tutkimuksessamme on määrällisen tutkimuksen piiriin kuuluvaa sisällön erittelyä. Sisällön erittelyyn kuuluvat tutkielmassamme aineiston eli twiittien laskeminen ja niiden lajittelu muun muassa yksityisyyden ja julkisuuden piireihin. Tuloksista kävi ilmi, että Alexander Stubbin brändi rakentuu hänen twiittiensä ja retwiittiensä pohjalta hyvin työpainotteiseksi. Stubb ei tuo twiiteissään esiin esimerkiksi ydinperhettään, joka kuuluu yksityisen henkilökohtaisuuden piiriin, mutta tekee sen sijaan tutuksi urheilullisuuttaan, joka taas on julkisen henkilökohtaisuuden piirissä. Hän twiittaa asiapitoisesti työpäiviensä sisällöstä, ja twiitit luovat hänestä ahkeraa sekä kiireistä kuvaa