Mitre Corporation

About: Mitre Corporation is a company organization based out in Bedford, Massachusetts, United States. It is known for research contribution in the topics: Air traffic control & National Airspace System. The organization has 4884 authors who have published 6053 publications receiving 124808 citations. The organization is also known as: Mitre & MITRE.

Problem resolution support for free flight operations

Abstract: The MITRE Corporation's Center for Advanced Aviation System Development (CAASD) has been conducting research into new automation capabilities to support free flight operations. One of these capabilities is a problem resolution function to assist the en route sector controller team in handling the more complex traffic patterns that can result from a less structured free flight environment. This function is termed problem analysis, resolution and ranking (PARR). PARR is envisaged as an enhancement to the user request evaluation tool (URET) free flight phase 1 (FFP1) capability, and has been designated as priority research for the follow-on free flight phase 2 (FFP2) effort. PARR is being developed as a series of incremental enhancements, with the first step focusing on the resolution of aircraft-to-aircraft and aircraft-to-airspace problems. Follow-on enhancements include resolutions for metering and other traffic flow management (TFM) flow initiatives, resolutions for the avoidance of severe weather areas, and the integration into a common en route sector team CHI. A research management plan has been prepared by the Federal Aviation Administration and MITRE/CAASD to support this development process. The paper describes the initial PARR capabilities, along with an extension to incorporate assigned metering time constraints.

Detection of weak random signals in IID non-Gaussian noise

Abstract: This paper considers the detection of weak random signals in circularly symmetric, independent, identically distributed noise. Locally optimum detectors and ad hoc nonlinearities are considered, with asymptotic expressions provided for evaluation of detection performance. The analytical expressions are used to evaluate the robustness of detectors to mismatch in the noise models. Finite-sample Monte Carlo simulation results indicate the reliability of these asymptotic measures in cases of practical interest. The results show that, as has been found for detection of weak known signals in non-Gaussian noise, reasonably configured ad hoc nonlinearities are nearly optimum and robust to modest errors in the noise statistics.

Remora: Sensing resource sharing among smartphone-based body sensor networks

Abstract: In many body sensor network (BSN) applications, such as activity recognition for assisted living residents or physical fitness assessment of a sports team, users spend a significant amount of time with one another while performing many of the same activities. We exploit this physical proximity with Remora, a smartphone-based Body Sensor Network activity recognition system which shares sensing resources among neighboring BSNs. Compared to other resource sharing approaches, Remora provides both increased accuracy and significant energy savings. To increase classification accuracy, Remora BSNs share sensors by overhearing neighbors' sensor data transmissions. When sharing, fewer on-body sensors are needed to achieve high accuracy, resulting in energy savings by turning off unneeded sensors. To save phone energy, neighboring BSNs share classifiers: only one classifier is active at a time classifying activities for all neighbors. Remora addresses three major challenges of sharing with physical neighbors: 1) Sharing only when the energy benefit outweighs the cost, 2) Finding and utilizing the shared sensors and classifiers which produce the best combination of accuracy improvement and energy savings, and 3) Providing a lightweight and collaborative classification approach, without the use of a backend server, which adapts to the dynamics of available neighbors. In a two week evaluation with 6 subjects, we show that Remora provides up to a 30% accuracy increase while extending phone battery lifetime by over 65%.

Disruption tolerant networking proxies for on-the-move tactical networks

Abstract: A major tenet of network centric warfare is that information sharing is vital to future battlefield superiority. Many plans for implementing NCW envision using IP as the network infrastructure to support information sharing, from wired and satellite networks to tactical networks. Unfortunately, IP implementations depend on the existence of stable end-to-end paths. If a router receives a packet and has nowhere to send it, the packet is dropped. Many tactical networks exhibit disconnections in connectivity that can severely impair, or completely halt, IP traffic. Disconnection tolerant networking (DTN) provides an end-to-end communications service in the presence of network disruptions, including (non-permanent) network partitions. DTN provides its own application programming interface, which is different from the sockets interface commonly used for TCP/IP. One way to adapt existing applications to use DTN is via application layer proxies. These proxies translate from applications' native use of end-to-end IP-based protocols such as TCP and UDP into DTN messages (bundles) that can traverse disrupted networks. This paper describes how DTN application layer proxies can be used with a CONDOR Jump-C2 class vehicle, using an HTTP (Web)-to-DTN proxy as an example.

Maximum likelihood approach to joint array detection/estimation

Abstract: The problem of detecting the number of (possibly correlated) narrowband sources of energy and estimating the direction of arrival (DOA) of each detected source using data received by an array of sensors is investigated. A combined detection and estimation approach based on the likelihood function (LF) is used. The approach is motivated by detection theoretic considerations instead of information theoretic criteria and uses maximum likelihood (ML) signal-to-noise ratio (SNR) estimates of hypothesized sources as detection statistics rather than maximizing the LF with a penalty function. Performance comparisons are made to unstructured and structured techniques based on Akaike information theoretic criteria (AIC), minimum description length (MDL), and Bayesian predictive density (BPD) approaches as well as the minimum variance distortionless response (MVDR) approach. An important feature that distinguishes this approach is the ability to trade off detection and false alarm performance, which is not possible with the other LF-based approaches, while achieving performance levels comparable to or exceeding the LF-based and MVDR approaches.