This PDF file contains the front matter associated with SPIE Proceedings Volume 9114, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

Helicopter identification has been an attractive topic. In this paper, we applied radar micro-Doppler signatures to identify helicopter. For identifying the type of a helicopter, besides its shape and size, the number of blades, the length of the blade, and the rotation rate of the rotor are important features, which can be estimated from radar micro-Doppler signatures of the helicopter’s rotating rotor blades. In our study, K-band CW/FMCW radars are used for collecting returned signals from helicopters. By analyzing radar micro-Doppler signatures, we can estimate the number of blades, the length of the blade, the angular rotation rate of the rotating blade, and other necessary parameters for identifying the type of a helicopter.

Micro-Doppler radar signals can be used to separate moving human targets from stationary clutter and also to identify and classify human movements. Traditional micro-Doppler radar systems which use a single sensor, monostatic system, suffer from the drawback that only the radial component of the micro-Doppler signal will be observed by the radar operator. This reduces the sensitivity of human activity recognition if the movements are not directly towards or away with respect to the line-of-sight to the radar antenna. In this paper, we propose the use of two bistatic micro-Doppler sensors to overcome this limitation. By using multiple sensors, the orientation of oscillating targets with respect to the radar line-of-sight can be inferred, thereby providing additional information to the radar operator. This approach can be used to infer the facing direction of the human with respect to the radar beam.

Infrared polarization relies on surface temperature, roughness, material properties, aspect angle to the sensor, sky down-welling and background radiance reflecting from the target. Often times, the polarization signature of a manmade target is different than the surrounding background. Furthermore, that difference is often present even when the thermal signature of the same target blends into the background. This paper will present maritime, airborne and ground data sets of polarization signatures of several objects that allow detection when other methods fall short.

Research and experimental trials have shown that electric-field (E-field) sensors are effective at detecting charged projectiles. E-field sensors can likely complement traditional acoustic sensors, and help provide a more robust and effective solution for bullet detection and tracking. By far, the acoustic sensor is the most prevalent technology in use today for hostile fire defeat systems due to compact size and low cost, yet they come with a number of challenges that include multipath, reverberant environments, false positives and low signal-to-noise. Studies have shown that these systems can benefit from additional sensor modalities such as E-field sensors. However, E-field sensors are a newer technology that is relatively untested beyond basic experimental trials; this technology has not been deployed in any fielded systems. The U.S. Army Research Laboratory (ARL) has conducted live-fire experiments at Aberdeen Proving Grounds (APG) to collect data from E-field sensors. Three types of E-field sensors were included in these experiments: (a) an electric potential gradiometer manufactured by Quasar Federal Systems (QFS), (b) electric charge induction, or "D-dot" sensors designed and built by the Army Research Lab (ARL), and (c) a varactor based E-field sensor prototype designed by University of North Carolina-Charlotte (UNCC). Sensors were placed in strategic locations near the bullet trajectories, and their data were recorded. We analyzed the performance of each E-field sensor type in regard to small-arms bullet detection capability. The most recent experiment in October 2013 allowed demonstration of improved versions of the varactor and D-dot sensor types. Results of new real-time analysis hardware employing detection algorithms were also tested. The algorithms were used to process the raw data streams to determine when bullet detections occurred. Performance among the sensor types and algorithm effectiveness were compared to estimates from acoustics signatures and known ground truth. Results, techniques and configurations that might work best for a given sensor platform are discussed.

The spectral reflectance signature of human skin provides opportunities to advance observations ranging from medical treatment to security applications. In this study 28 volunteers participated in a skin reflectance measurement of the inside of the right forearm. The reflectance measurements were made over the 250 nm to 2500 nm spectral range. The analysis included estimates of the variability attributed to the instrument, variability of the same subject, and variability among subjects. This allowed for determining measures of similarity and differences that indicate the inherent separability within the distribution. While this sample size may not fully represent a full diverse-population, it does provide a provisional reference point for modeling and simulation.

As it is well-known, application of the passive THz camera for the security problems is very promising way. It allows seeing concealed object without contact with a person and this camera is non-dangerous for a person. We demonstrate new possibility of the passive THz camera using for a temperature difference observing on the human skin if this difference is caused by different temperatures inside the body. We discuss some physical experiments, in which a person drinks hot, and warm, and cold water and he eats. After computer processing of images captured by passive THz camera TS4 we may see the pronounced temperature trace on skin of the human body. For proof of validity of our statement we make the similar physical experiment using the IR camera. Our investigation allows to increase field of the passive THz camera using for the detection of objects concealed in the human body because the difference in temperature between object and parts of human body will be reflected on the human skin. However, modern passive THz cameras have not enough resolution in a temperature to see this difference. That is why, we use computer processing to enhance the camera resolution for this application. We consider images produced by THz passive cameras manufactured by Microsemi Corp., and ThruVision Corp.

Blasts and detonations release large amount of energy in short time duration. Some of this energy is released through radiation in the whole optical spectrum. Measurement of this radiation may serve as a base for investigation of the blast phenomena. A fast multispectral radiometer that operates in proper chosen spectral bands provides extensive information on the physical processes that govern the blast. This information includes the time dependence of the temperature, area of the blast as-well-as of the aerosols and gases that are generated. Analysis of this data indicates the order of the detonation and provides good estimation on the masses and types of the high-explosives (HE) materials and their casing. This paper presents the methodology and instrumentation of fast multispectral radiometry in application to the blast measurement and analysis in a Near-ground Explosion Test (NET). In NET, the flash radiation of the blast was measured for two HE materials: TNT and composition B (CB). The investigation includes charges of different masses (0.25 - 20.0 kg) and of various casing materials (steel, Al, PVC), thickness (2 – 6 mm) and various casing type (open on both face ends and hermetically closed). Analysis of the data demonstrates the power of fast radiometry methodology and reveals the governing characteristics of atmospheric blasts.

Risks to the safety of public zones (generally available for people) are related mainly to the presence of hidden dangerous objects (such as knives, guns, bombs etc.) and their usage. Modern system for the monitoring of such zones attempt to detect dangerous tools using multispectral cameras working in different spectral ranges: the visible radiation, near, medium and long range infrared and recently also in terahertz range. In order to develop methods and algorithms to detect hidden objects it is necessary to determine the thermal signatures of such objects of interest. The laboratory measurements were conducted to determine the thermal signatures of dangerous tools hidden under various clothes in different ambient conditions. Cameras used for measurements were working in spectral range 0.6-12.5 µm. An infrared imaging Fourier transform spectroradiometer was also used, working in spectral range 7.7-11.7 µm. Analysis of registered thermograms and hyperspectral datacubes has yielded the thermal signatures for: two types of guns, two types of knives and home-made explosive bombs. The determined thermal signatures will be used in the development of method and algorithms of image analysis implemented in proposed monitoring systems.

We propose and discuss modified correlation criteria for the detection and identification of dangerous substances using pulsed THz signal in the reflection mode. These criteria are integral criteria in time and they are based on the SDA method. We develop various approaches for enhancement of features of a detecting substance in reflected THz signal under investigation. These criteria are applied for the detection of different explosive situations. Among them we consider a mixture of explosives and PWM C4 explosive with very complicated surface. Proposed algorithms show both high probability of the substance identification and a reliability of realization in practice. We discuss modified correlation criteria for the detection and identification of dangerous substances using pulsed THz signal in the reflection mode.

Over the last few decades, we have seen an increase in both quality and quantity of 3D data sets. These data sets primarily come in the form of discrete points that are projected onto the surface of the object (point clouds) and are often derived from either LIDAR data (in which case, the surface points are actively sensed) or stereoscopic pairs (in which case, the surface points are derived using two dimensional (2D) feature matching algorithms). As these data sets become larger and denser, they also become harder to sift through which demands methods for automatic object classification through computer vision processes. In this paper we revisit a method of recognizing objects from their surface features known as Tripod Operators.[1] More specifically, we explore how matching multiple features from an unknown object to a known shape allows us to determine the extent to which the objects are similar using the resultant Digital Elevation Model (DEM) or Surface Elevation Model (SEM) that results from manipulation of point clouds.. We apply this method to determine how to separate objects of various classes.

The U.S. Army Research Laboratory (ARL) Multimodal Signatures Database (MMSDB) consists of a number of colocated relational databases representing a collection of data from various sensors. Role-based access to this data is granted to external organizations such as DoD contractors and other government agencies through a client Web portal. In the current MMSDB system, access control is only at the database and firewall level. In order to offer finer grained security, changes to existing user profile schemas and authentication mechanisms are usually needed. In this paper, we describe a software middleware architecture and implementation that allows fine-grained access control to the MMSDB at a dataset, table, and row level. Result sets from MMSDB queries issued in the client portal are filtered with the use of a policy enforcement proxy, with minimal changes to the existing client software and database. Before resulting data is returned to the client, policies are evaluated to determine if the user or role is authorized to access the data. Policies can be authored to filter data at the row, table or column level of a result set. The system uses various technologies developed in the International Technology Alliance in Network and Information Science (ITA) for policy-controlled information sharing and dissemination¹. Use of the Policy Management Library provides a mechanism for the management and evaluation of policies to support finer grained access to the data in the MMSDB system. The GaianDB is a policy-enabled, federated database that acts as a proxy between the client application and the MMSDB system.

The U.S. Army is interested in developing algorithms to classify weapons systems fire based on their acoustic signatures. To support this effort, an algorithm was developed to extract features from acoustic signatures of weapons systems fire and applied to over 1300 signatures. The algorithm filtered the data using standard techniques then estimated the amplitude and time of the first five peaks and troughs and the location of the zero crossing in the waveform. The results were stored in Excel spreadsheets. The results are being used to develop and test acoustic classifier algorithms.