Toward a unified PNT, Part 1: Complexity and context: Key challenges of multisensor positioning

The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multi-sensor solution is thus required. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected

Three Essays on U.S. Social Policy’s Impact on the Human Capital Development of Young Adults At-Risk of Poverty

Social welfare programs and policies can have a variety of anticipated and unexpected effects on the human capital investments of young adults at-risk of living in poverty in the United States. My dissertation investigates how three large-scale public programs – means-tested, cash welfare (e.g., Aid to Families with Dependent Children and Temporary Aid to Needy Families), Medicaid health insurance for children, and the Social Security Student Benefit Program – affected the educational attainment and work experience of vulnerable young adults. In the first chapter, I examine how public policies encouraging labor force participation by low-skilled single mothers during welfare reform unintentionally led to labor supply declines by young, less-educated single males. While the labor market woes of low-skilled male workers over the past several decades have been well documented, the academic literature on the identification of causal factors leading to the decline in labor force participation (LFP) by young, low-skilled males is relatively scant. In this paper, I use a fixed-effects, instrumental variable research design to exploit the timing and characteristics of welfare reform policies to explore whether policies targeted to increase LFP rates for low-skilled single mothers inadvertently led to labor force exit of young, low-skilled males. Using data from the Current Population Survey and the series of work inducements enacted by states throughout the 1990s as a source of exogenous variation in a quasi-experimental design, I find that a welfare-reform-generated 10 percentage point (pp) increase in LFP for low-skilled single mothers resulted in a statistically significant 2.6 pp decline in LFP rates by young, low-skilled single males. Furthermore, after a series of alternative model specifications and robustness checks, I find that this result is driven entirely by the decline in labor supply for white males; young black males and other groups of workers appear to be unaffected by the labor supply response of less-educated single mothers to welfare reform. The second essay in my dissertation studies one of the long-term effects of the child Medicaid health insurance expansions. Prompted by the legislative decision to decouple child Medicaid benefits from cash welfare receipt, the number of young children qualifying for public health insurance grew markedly throughout the 1980s and early 1990s. This chapter extends the academic literature examining early childhood investments and longer-term human capital measures by exploring whether public health insurance expansions to low-income children led to a greater number of high school completers in the 2000s. Using a technique developed by Currie and Gruber (1994, 1996) to simulate the generosity of a state’s Medicaid program during early childhood, I find large and significant effects on completion rates, which are examined in two forms: the dropout rate and the traditional four-year high school graduation rate. Intent-to-treat estimates range from a 1.9 to 2.5 pp decrease in the dropout rate for each 10 pp increase in early childhood years covered by the state-level Medicaid program. The same 10 percentage point increase in child Medicaid program generosity reveals increases of 1.0 to 1.3 pp when applied to four-year graduation rates, indicating that dropout reductions are propelled by increases in traditional diplomas. In addition, results appear to be driven by Hispanic and white students, the two groups which experienced the greatest within-group eligibility increases due to the decoupling of child Medicaid from the AFDC cash assistance program. My final dissertation chapter investigates how a particular college fund guarantee affected achievements in higher education. Utilizing data from the National Longitudinal Survey of Youth (1979) and a difference-in-differences model, this work re-examines the impact of the Social Security Student Benefits Program (SSSBP) on post-secondary educational attainment, a topic first studied by Dynarski (2003). By exploiting a larger panel of data and exploring degree attainment at various ages, my coauthor and I find that disadvantaged youth potentially qualifying for SSSBP funds – e.g., those losing a father before they turned 18 – were over 20 pp more likely to obtain higher education degrees beyond their high school diploma than similar students who would have qualified for benefits, but-for the program’s termination in May 1982. Initial program impacts – i.e., those by age 23 – show an increase in Associate’s degree attainment. As these respondents age, however, many go on to obtain four year degrees. Impacts are large and statistically significant, and suggestive that social programs seeking to reduce the financial costs of Associate’s degrees – such as the one announced by President Obama in his 2015 State of the Union Address – could be well-targeted

Alien Registration- Groves, John H. (Islesboro, Waldo County)

https://digitalmaine.com/alien_docs/5225/thumbnail.jp

Do States Prefer Alcohol Over Marijuana? A Look at Labeling Regulatory Differences Between the Alcohol and Edibles Industries

In the children’s book Through the Looking Glass and What Alice Found There , Alice interacts with Humpty Dumpty. During their conversation, Humpty notes that he, alone, can decide the meaning of words. Even Alice, at the young age of seven, casts doubt on this idea. Definitions of words and phrases play an important role in human interactions and even more so when the words and phrases defined are within a statute. In the United States, Congress and state legislatures play the role of Humpty Dumpty by coming up with meanings of important words and phrases found in the laws they write. This is an important role of the legislatures to ensure the clarity of the law. While sometimes it is necessary to give different meanings to the same word, when the legislature uses unique phrases such as “appealing to children,” one would expect the legislature to use the same meaning, given the limited applicability of the phrase. However, this phrase appears to have two different meanings when it comes to states that prohibit labels on marijuana edibles (“edibles”) and alcohol that appeal to children. Regulations in the states that regulate such labels on both alcohol and edibles have been shown to have stricter standards for edible labels, even when the language of the regulations is nearly identical. This note is not advocating for edible manufacturers to target children through advertising. Such advertising would likely lead to more small children accidentally ingesting edibles, which should be avoided. Rather, this note is arguing for state governments to cease violating the constitutional rights of edible manufacturers, given the labeling practices of the alcohol industry

Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone

The signals available for navigation depend on the environment. To operate reliably in a wide range of different environments, a navigation system is required to adopt different techniques based on the environmental contexts. In this paper, an environmental context detection framework is proposed, building the foundation of a context adaptive navigation system. Different land environments are categorized into indoor, urban, and open-sky environments based on how Global Navigation Satellite System (GNSS) positioning performs in these environments. Indoor and outdoor environments are first detected based on the availability and strength of GNSS signals using a hidden Markov model. Then the further classification of outdoor environments into urban and open-sky is investigated. Pseudorange residuals are extracted from raw GNSS measurements in a smartphone and used for classification in a fuzzy inference system alongside the signal strength data. Practical test results under different kinds of environments demonstrate an overall 88.2 percent detection accuracy

Context Determination for Adaptive Navigation using Multiple Sensors on a Smartphone

Navigation and positioning is inherently dependent on the context, which comprises both the operating environment and the behaviour of the host vehicle or user. No single technique is capable of providing reliable and accurate positioning in all contexts. In order to operate reliably across different contexts, a multi-sensor navigation system is required to detect its operating context and reconfigure the techniques accordingly. This paper aims to determine the behavioural and environmental contexts together, building the foundation of a context-adaptive navigation system. Both behavioural and environmental context detection results are presented. A hierarchical behavioural recognition scheme is proposed, within which the broad classes of human activities and vehicle motions are detected using measurements from accelerometers, gyroscopes, magnetometers and the barometer on a smartphone by decision trees (DT) and Relevance Vector Machines (RVM). The detection results are further improved by behavioural connectivity. Environmental contexts (e.g., indoor and outdoor) are detected from GNSS measurements using a hidden Markov model. The paper also investigates context association in order to further improve the reliability of context determination. Practical test results demonstrate improvements of environment detection in context determination

Improving Environment Detection by Behaviour Association for Context Adaptive Navigation

Navigation and positioning systems depend on both the operating environment and the behavior of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning, and the behavior can contribute additional information to the navigation solution. In order to operate across different contexts, a context‐adaptive navigation solution is required to detect the operating contexts and adopt different positioning techniques accordingly. This paper focuses on determining both environments and behaviors from smartphone sensors, serving for a context‐adaptive navigation system. Behavioral contexts cover both human activities and vehicle motions. The performance of behavior recognition in this paper is improved by feature selection and a connectivity‐dependent filter. Environmental contexts are detected from global navigation satellite system (GNSS) measurements. They are detected by using a probabilistic support vector machine, followed by a hidden Markov model for time‐domain filtering. The paper further investigates how behaviors can assist within the processes of environment detection. Finally, the proposed context‐determination algorithms are tested in a series of multicontext scenarios, showing that the proposed context association mechanism can effectively improve the accuracy of environment detection to more than 95% for pedestrian and more than 90% for vehicle

The Limits of In-run Calibration of MEMS and the Effect of New Techniques

Inertial sensors can significantly increase the robustness of an integrated navigation system by bridging gaps in the coverage of other positioning technologies, such as GNSS or Wi-Fi positioning [1]. A full set of chip-scale MEMS accelerometers and gyros can now be bought for less than $10, potentially opening up a wide range of new applications. However, these sensors require calibration before they can be used for navigation[2]. Higher quality inertial sensors may be calibrated “in-run” using Kalman filter-based estimation as part of their integration with GNSS or other position-fixing techniques. However, this approach can fail when applied to sensors with larger errors which break the Kalman filter due to the linearity and small-angle approximations within its system model not being valid. Possible solutions include: replacing the Kalman filter with a non-linear estimation algorithm, a pre-calibration procedure and smart array [3]. But these all have costs in terms of user effort, equipment or processing load. This paper makes two key contributions to knowledge. Firstly, it determines the maximum tolerable sensor errors for any in-run calibration technique using a basic Kalman filter by developing clear criteria for filter failure and performing Monte-Carlo simulations for a range of different sensor specifications. Secondly, it assesses the extent to which pre-calibration and smart array techniques enable Kalman filter-based in-run calibration to be applied to lower-quality sensors. Armed with this knowledge of the Kalman filter’s limits, the community can avoid both the unnecessary design complexity and computational power consumption caused by over-engineering the filter and the poor navigation performance that arises from an inadequate filter. By establishing realistic limits, one can determine whether real sensors are suitable for in-run calibration with simple characterization tests, rather than having to perform time-consuming empirical testing

The Limits of In-Run Calibration of MEMS Inertial Sensors and Sensor Arrays

MEMS accelerometers and gyroscope triads now cost less than $10, potentially opening up many new applications. However, these sensors require calibration prior to navigation use. This paper determines the maximum tolerable sensor errors for in-run calibration techniques using a basic Kalman filter by developing criteria for filter failure and performing Monte Carlo simulations for a range of different sensor specifications, and both car and UAV motion-profiles. Gyroscope bias is found to be the most significant with the maximum tolerable value of its SD varying between 0.75 and 2.6 deg/s depending on the value of the specification of the other sensor sources. The paper shows that pre-calibration and smart array techniques could potentially enable in-run calibration to be applied to lower-quality sensors. However, the estimation of scale-factor cross-coupling and gyroscope g-dependent errors could potentially be critical. Armed with this knowledge, designers can avoid both unnecessary design complexity and computational load of over-engineering and the poor navigation performance of inadequate filters