Garry M. Jacyna

Inner Bounds on Performance of Radar and Communications Co-Existence

We investigate methods of co-existence between radar and communications systems. Each system typically considers the other system a source of interference. Consequently, the traditional solution is to isolate the two systems spectrally or spatially. By considering a cooperative radar and communications signaling scheme, we derive achievable bounds on performance for a receiver that observes communications and radar return in the same frequency allocation. We assume the radar and communications operations to be a single joint system. Bounds on performance of the joint system are measured in terms of data information rate for communications and a novel radar estimation information rate for the radar.

Vehicle detection and localization using Unattended Ground Magnetometer Sensors

This analysis was completed as part of a larger Modeling and Simulation effort to estimate algorithm-level Measures of Performance (MOP), such as the probability of detection (PD) and the probability of identification (PID) of a vehicle or person transiting through an area of interest. The present work focuses on MOPs for Unattended Ground Magnetometer Sensors, which may be used to detect passing vehicles and estimate their bearing relative to the magnetometer position. In the first phase of the analysis, we concentrate on the probability of detection as a function of vehicle speed and distance (i.e., point of closest approach (CPA)) from the sensor. In the second phase, we try to localize the vehicle by extracting its relative bearing with respect to the magnetometer from the two orthogonal induced magnetic field measurements. The derivations are based on the assumption that a road vehicle may be approximated as a prolate homogeneous ellipsoid, as well as the assumption of uniform linear motion. Results show that, for speeds below 30 MPH, the maximum detection ranges (for PD = 0.5) are on the order of 40 meters for two-axis fluxgate magnetometers and for the operational parameters used in this analysis.

A high-level overview of fundamental limits studies for the DARPA SSPARC program

This paper presents a high-level overview of the Fundamental Limits studies for the DARPA SSPARC program. It focuses on the key techniques and insights that have resulted from this effort and presents possible future research directions that have been suggested by these studies1.

Vehicle acoustic classification in netted sensor systems using Gaussian mixture models

Acoustic vehicle classification is a difficult problem due to the non-stationary nature of the signals, and especially the lack of strong harmonic structure for most civilian vehicles with highly muffled exhausts. Acoustic signatures will also vary largely depending on speed, acceleration, gear position, and even the aspect angle of the sensor. The problem becomes more complicated when the deployed acoustic sensors have less than ideal characteristics, in terms of both the frequency response of the transducers, and hardware capabilities which determine the resolution and dynamic range. In a hierarchical network topology, less capable Tier 1 sensors can be tasked with reasonably sophisticated signal processing and classification algorithms, reducing energy-expensive communications with the upper layers. However, at Tier 2, more sophisticated classification algorithms exceeding the Tier 1 sensor/processor capabilities can be deployed. The focus of this paper is the investigation of a Gaussian mixture model (GMM) based classification approach for these upper nodes. The use of GMMs is motivated by their ability to model arbitrary distributions, which is very relevant in the case of motor vehicles with varying operation modes and engines. Tier 1 sensors acquire the acoustic signal and transmit computed feature vectors up to Tier 2 processors for maximum-likelihood classification using GMMs. In a binary classification task of light-vs-heavy vehicles, the GMM based approach achieves 7% equal error rate, providing an approximate error reduction of 49% over Tier 1 only approaches.

Critical infrastructure network analysis enabled by simulation metamodeling

This paper presents an application of simulation metamodeling to improve the analysis capabilities within a decision support tool for Critical Infrastructure network evaluation. Simulation metamodeling enables timeliness of analysis, which was not achievable by the original large-scale network simulation due to long set-up times and slow run times. We show through a case study that the behavior of a large-scale simulation for Critical Infrastructure analysis can be effectively captured by Neural Network metamodels and Stochastic Kriging metamodels. Within the case study, metamodeling is integrated into the second step of a two-step analysis process for vulnerability assessment of the network. This consists first of an algorithmic exploration of a power grid network to locate the most susceptible links leading to cascading failures. These links represent the riskiest links in the network and were used by the metamodels to visualize how their failure probabilities affect global network performance measures.

Geolocation analysis using Maxent and plant sample data

A study was conducted to assess the feasibility of geolocation based on correctly identifying pollen samples found on goods or people for purposes of compliance with U.S. import laws and criminal forensics. The analysis was based on Neotropical plant data sets from the Global Biodiversity Information Facility. The data were processed through the software algorithm Maxent that calculates plant probability geographic distributions of maximum entropy, subject to constraints. Derivation of single and joint continuous probability densities of geographic points, for single and multiple taxa occurrences, were performed. Statistical metrics were calculated directly from the output of Maxent for single taxon probabilities and were mathematically derived for joint taxa probabilities. Predictions of likeliest geographic regions at a given probability percentage level were made, along with the total corresponding geographic ranges. We found that joint probability distributions greatly restrict the areas of possible provenance of pollen samples.

Automated pollen identification system for forensic geo-historical location applications

The use of pollen grain analysis for forensic geo-historical location has been explored for several decades, yet it is not widely adopted in the United States. We confirmed significant improvement in geographic precision, i.e., from 2.5×107 to 1.2×105 km2, by simultaneously applying flowering plant data from four different taxa at the genus and species levels. Moreover, when we calculated precision using collected pollen data, we found that co-occurring, pairwise genus-level distinctions based on expert-provided indicator taxa resulted in average precision values of 4° and 4.5° in latitude and longitude, respectively - corresponding to roughly 1.8×105 km2. We also applied computer vision techniques to identify morphologically similar pollen grains, which resulted in grain-identification error rates of 2.18% and 6.24% at the genus and species levels, respectively, surpassing previously published records. Collectively, our results demonstrate that algorithmic identification of species-specific pollen morphology, founded on established computer vision techniques, when combined with species-level pollen distribution, has the potential to revolutionize the scope, accuracy, and precision of forensic geographic attribution.

Using Generative Analysis for Homeland Security: Modeling the Possibilities and the Probabilities

Frequently problems of homeland security require systemic solutions. Tactics, techniques and procedures must be innovatively combined with the latest technological advances to meet an emerging and ever changing threat. This paper provides a simulation based systems engineering approach to evaluate the wide variety of combinations that complex solutions require. A briefcase study for the defense of a large venue is provided to illustrate the methodology described herein.

Simulation of vehicle acoustics in support of netted sensor research and development

The MITRE Corporation has initiated a three-year internally-funded research program in netted sensors, the first-year effort focusing on vehicle detection for border monitoring. An important component is developing an understanding of the complex acoustic structure of vehicle noise to aid in netted sensor-based detection and classification. This presentation will discuss the design of a high-fidelity vehicle acoustic simulator to model the generation and transmission of acoustic energy from a moving vehicle to a collection of sensor nodes. Realistic spatially-dependent automobile sounds are generated from models of the engine cylinder firing rates, muffler and manifold resonances, and speed-dependent tire whine noise. Tire noise is the dominant noise source for vehicle speeds in excess of 30 miles per hour (MPH). As a result, we have developed detailed models that successfully predict the tire noise spectrum as a function of speed, road surface wave-number spectrum, tire geometry, and tire tread pattern. We have also included realistic descriptions of the spatial directivity patterns for the engine harmonics, muffler, and tire whine noise components. The acoustic waveforms are propagated to each sensor node using a simple phase-dispersive multi-path model. A brief description of the models and their corresponding outputs is provided.

Netted sensors-based vehicle acoustic classification at Tier 1 nodes

The MITRE Corporation has embarked on a three-year internally-funded research program in netted sensors with applications to border monitoring, situational awareness in support of combat identification, and urban warfare. The first-year effort emphasized a border monitoring application for dismounted personnel and vehicle surveillance. This paper will focus primarily on the Tier 1 acoustic-based vehicle classification component. We discuss the development and implementation of a robust linear-weighted classifier on a Mica2 Crossbow mote using feature extraction algorithms specifically developed by MITRE for mote-based processing applications. These include a block floating point Fast Fourier Transform (FFT) algorithm and an 8-band proportional bandwidth filter bank. Results of in-field testing are compared and contrasted with theoretically-derived performance bounds.