Brian L. Guidry

Time-reversal processing for an acoustic communications experiment in a highly reverberant environment.

Time-reversal (T/R) communications is a new application area motivated by the recent advances in T/R theory. Although perceived by many in signal processing as simply an application of matched-filter theory, a T/R receiver offers an interesting solution to the communications problem for a reverberant channel. In this paper, the performance of various realizations of the T/R receiver for an acoustic communications experiment in air is described along with its associated processing. The experiment is developed to evaluate the performance of point-to-point T/R receivers designed to extract a transmitted information sequence propagating in a highly reverberant environment. It is demonstrated that T/R receivers are capable of extracting the transmitted coded sequence from noisy microphone sensor measurements with zero-symbol error. The processing required to validate these experimental results is discussed. These results are also compared with those produced by an equivalent linear equalizer or inverse filter, which provides the optimal solution when it incorporates all of the reverberations.

Wideband multichannel time-reversal processing for acoustic communications in highly reverberant environments

The development of multichannel time-reversal (T/R) processing techniques continues to progress rapidly especially when the need to communicate in a reverberant environment is critical. The underlying T/R concept is based on time-reversing the Green’s function characterizing the uncertain communications channel mitigating the deleterious dispersion and multipath effects. In this paper, attention is focused on two major objectives: (1) wideband communications leading to a time-reference modulation technique; and (2) multichannel acoustic communications in two waveguides: a stairwell and building corridors with many obstructions, multipath returns, severe background noise, disturbances, and long propagation paths (∼180ft) including disruptions (bends). It is shown that T/R receivers are easily extended to wideband designs. Acoustic information signals are transmitted with an eight-element array to two receivers with a significant loss in signal levels due to the propagation environment. The results of the n...

Physics-Based Detection of Radioactive Contraband: A Sequential Bayesian Approach

The timely and accurate detection of nuclear contraband is an extremely important problem of national security. The development of a prototype sequential Bayesian processor that incorporates the underlying physics of ¿-ray emissions and the measurement of photon energies and their interarrival times that offers a physics-based approach to attack this challenging problem is described. A basic radionuclide representation in terms of its ¿-ray energies along with photon interarrival times is used to extract the physics information available from the uncertain measurements. It is shown that not only does this approach lead to a physics-based structure that can be used to develop an effective threat detection technique, but also motivates the implementation of this approach using advanced sequential Monte Carlo processors or particle filters to extract the required information. The resulting processor is applied to experimental data to demonstrate its feasibility.

Threat Detection of Radioactive Contraband Incorporating Compton Scattering Physics: A Model-Based Processing Approach

The detection of radioactive contraband is a critical problem in maintaining national security for any country. Gamma-ray emissions from threat materials challenge both detection and measurement technologies significantly. The development of a sequential, model-based Bayesian processor that captures both the underlying transport physics of gamma-ray emissions including Compton scattering and the measurement of photon energies offers a physics-based approach to attack this challenging problem. The inclusion of a basic radionuclide representation of absorbed/scattered photons at a given energy along with interarrival times is used to extract the physics information available from noisy measurements. It is shown that this representation leads to an “extended” physics-based structure that can be used to develop an effective sequential detection technique. The resulting model-based processor is applied to data obtained from a controlled experiment in order to assess its feasibility.

Bayesian Processing for the Detection of Radioactive Contraband from Uncertain Measurements

With the increase in terrorist activities throughout the world, the need to develop techniques capable of detecting radioactive contraband in a timely manner is a critical requirement. The development of Bayesian processors for the detection of contraband stems from the fact that the posterior distribution is clearly multimodal eliminating the usual Gaussian-based processors. The development of a sequential bootstrap processor for this problem is discussed and shown how it is capable of providing an enhanced signal for eventual detection.

A Bayesian sequential processor approach to spectroscopic portal system decisions

The development of faster more reliable techniques to detect radioactive contraband in a portal type scenario is an extremely important problem especially in this era of constant terrorist threats. Towards this goal the development of a model-based, Bayesian sequential data processor for the detection problem is discussed. In the sequential processor each datum (detector energy deposit and pulse arrival time) is used to update the posterior probability distribution over the space of model parameters. The nature of the sequential processor approach is that a detection is produced as soon as it is statistically justified by the data rather than waiting for a fixed counting interval before any analysis is performed. In this paper the Bayesian model-based approach, physics and signal processing models and decision functions are discussed along with the first results of our research.

Model-based failure detection for cylindrical shells from noisy vibration measurements

Model-based processing is a theoretically sound methodology to address difficult objectives in complex physical problems involving multi-channel sensor measurement systems. It involves the incorporation of analytical models of both physical phenomenology (complex vibrating structures, noisy operating environment, etc.) and the measurement processes (sensor networks and including noise) into the processor to extract the desired information. In this paper, a model-based methodology is developed to accomplish the task of online failure monitoring of a vibrating cylindrical shell externally excited by controlled excitations. A model-based processor is formulated to monitor system performance and detect potential failure conditions. The objective of this paper is to develop a real-time, model-based monitoring scheme for online diagnostics in a representative structural vibrational system based on controlled experimental data.

Radioactive threat detection with scattering physics: A model-based application

The detection of radioactive contraband is a critical problem in maintaining national security for any country. Emissions from threat materials challenge both detection and measurement technologies especially when concealed by various types of shielding complicating the transport physics significantly. The development of a model-based sequential Bayesian processor that captures both the underlying transport physics including scattering offers a physics-based approach to attack this challenging problem. It is shown that this processor can be used to develop an effective detection technique.

On‐line failure detection of a vibrating structure: A model‐based approach

Model‐based failure detection is based on the principle that the MBP for a normal or pristine structure is developed first and tuned during the calibration stage assuring a statistically validated processor. Once developed, the MBP is used as the integral part in a sequential detection scheme to decide whether or not the structure under investigation is operating normally. When an abnormality is detected, a failure alarm is activated and the type of failure is classified based on a library of potential failure modes. Here we use high‐order parametric models to capture the essence of the structures over a limited frequency band known to be sensitive to structural changes. These estimated or identified models for normal operations are then used to develop the MBP which in this instance is a recursive Kalman filter. The filter is known to produce zero‐mean/white residuals when optimally tuned to the data. Failure is declared when these properties are no longer valid. Once the detection is accomplished, the n...

Wideband multichannel time‐reversal communications in a tunnel‐like structure

The development of multichannel time‐reversal (T/R) processing continues to progress rapidly, especially when the need to communicate in a highly reverberant environment is critical. One such environment is a tunnel or cave, or even a pipe with many obstructions, multipath returns, severe background noise, disturbances, path disruptions (bends) as well as a long propagation path (∼120 ft.). For this environment, multichannel T/R receiver designs have been extended to include a wideband processor and modulation scheme along with designs to communicate in the highly reverberative tunnel‐like environment that includes high background noise levels and disturbances. The acoustic information signals are transmitted by an 8‐element host or base station array and received some distance away with a significant loss in transmitted signal levels. In this paper the results of the new wideband processor and modulation scheme coupled with the underlying T/R theory are discussed to demonstrate the overall performance fo...