archives@tulane.edu / 1 / Andrew C Markel

The complex visual search involved in baggage screening requires operators to determine quickly whether a bag contains threatening objects that are embedded in a high degree of visual clutter. Methods for calculating visual clutter have been developed, and research has demonstrated the negative impact of clutter on search performance. The current study examined whether leveraging visual clutter information on the display during search could improve baggage screening performance above and beyond the conventional screening process. Ninety undergraduates searched x-ray images of bags for weapon items in a low fidelity baggage screening simulation; two clutter-based preview conditions displayed a limited portion of the bag to the participant before the entire bag was displayed. Eye movement data confirmed that the preview process guided the participant's attention to the corresponding previewed region. However, analysis of the baggage screening performance data showed there were no significant benefits associated with either clutter-based preview conditions compared with a control condition in which the entire bag was displayed for the duration of the trial. Thus, the results suggest that using clutter-based preview to guide visual attention does not substantially improve weapon detection performance. Despite this null effect, the current study provides additional evidence regarding the impact of visual clutter on complex search performance by demonstrating significant reductions in weapon detection accuracy and search efficiency due to increasing levels of visual clutter. Further research should explore methods for improving complex visual search by considering the negative impacts of visual clutter and ensuring that both attention guidance and object recognition processes are facilitated during search.

Conformal arrays possess certain desirable characteristics for deployment on unmanned aerial vehicles and other payload-limited platforms: aerodynamic design, minimal payload weight, increased field of view, and ease of integration with diverse sensor functions. However, the conformal arrays nonplanar geometry causes high adaptive losses in conventional space-time adaptive processing (STAP) algorithms. In this thesis, we develop a conformal array signal model and apply it to evaluate the performance of conventional STAP algorithms on simulated ground clutter data. We find that array-induced clutter nonstationarity leads to high adaptive losses, which greatly burden detection performance. To improve adaptive performance, we investigate the application of existing equivalent-linear-array transformations and develop novel deterministic and adaptive angle-Doppler compensation techniques, which align nonstationary clutter returns. Through the application of these techniques, we are able to nearly fully mitigate the nonstationary behavior yielding performance similar to that of a conventional planar array. Finally, we investigate the impact of array errors on the performance of conformal arrays, and propose several array calibration techniques as ameliorating solutions.

No description available.

Estimation fusion, or data fusion for estimation, is the problem of how to best utilize useful information contained in multiple data sets for the purpose of estimating an unknown quantity — a parameter or a process. Estimation fusion with constraints gives rise to challenging theoretical problems given the observations from multiple geometrically dispersed sensors: Under dimensionality constraints, how to preprocess data at each local sensor to achieve the best estimation accuracy at the fusion center? Under communication bandwidth constraints, how to quantize local sensor data to minimize the estimation error at the fusion center? Under constraints on storage, how to optimally update state estimates at the fusion center with out-of-sequence measurements? Under constraints on storage, how to apply the out-of-sequence measurements (OOSM) update algorithm to multi-sensor multi-target tracking in clutter? The present work is devoted to the above topics by applying the best linear unbiased estimation (BLUE) fusion. We propose optimal data compression by reducing sensor data from a higher dimension to a lower dimension with minimal or no performance loss at the fusion center. For single-sensor and some particular multiple-sensor systems, we obtain the explicit optimal compression rule. For a multisensor system with a general dimensionality requirement, we propose the Gauss-Seidel iterative algorithm to search for the optimal compression rule. Another way to accomplish sensor data compression is to find an optimal sensor quantizer. Using BLUE fusion rules, we develop optimal sensor data quantization schemes according to the bit rate constraints in communication between each sensor and the fusion center. For a dynamic system, how to perform the state estimation and sensor quantization update simultaneously is also established, along with a closed form of a recursion for a linear system with additive white Gaussian noise. A globally optimal OOSM update algorithm and a constrained optimal update algorithm are derived to solve one-lag as well as multi-lag OOSM update problems. In order to extend the OOSM update algorithms to multisensor multitarget tracking in clutter, we also study the performance of OOSM update associated with the Probabilistic Data Association (PDA) algorithm.

<p>Backscatter communication is an emerging wireless technology that recently has gained an increase in attention from both academic and industry circles. The key innovation of the technology is the ability of ultra-low power devices to utilize nearby existing radio signals to communicate. As there is no need to generate their own energetic radio signal, the devices can benefit from a simple design, are very inexpensive and are extremely energy efficient compared with traditional wireless communication. These benefits have made backscatter communication a desirable candidate for distributed wireless sensor network applications with energy constraints. </p><p>The backscatter channel presents a unique set of challenges. Unlike a conventional one-way communication (in which the information source is also the energy source), the backscatter channel experiences strong self-interference and spread Doppler clutter that mask the information-bearing (modulated) signal scattered from the device. Both of these sources of interference arise from the scattering of the transmitted signal off of objects, both stationary and moving, in the environment. Additionally, the measurement of the location of the backscatter device is negatively affected by both the clutter and the modulation of the signal return. </p><p>This work proposes a channel coding framework for the backscatter channel consisting of a bi-static transmitter/receiver pair and a quasi-cooperative transponder. It proposes to use run-length limited coding to mitigate the background self-interference and spread-Doppler clutter with only a small decrease in communication rate. The proposed method applies to both binary phase-shift keying (BPSK) and quadrature-amplitude modulation (QAM) scheme and provides an increase in rate by up to a factor of two compared with previous methods.</p><p>Additionally, this work analyzes the use of frequency modulation and bi-phase waveform coding for the transmitted (interrogating) waveform for high precision range estimation of the transponder location. Compared to previous methods, optimal lower range sidelobes are achieved. Moreover, since both the transmitted (interrogating) waveform coding and transponder communication coding result in instantaneous phase modulation of the signal, cross-interference between localization and communication tasks exists. Phase discriminating algorithm is proposed to make it possible to separate the waveform coding from the communication coding, upon reception, and achieve localization with increased signal energy by up to 3 dB compared with previous reported results.</p><p>The joint communication-localization framework also enables a low-complexity receiver design because the same radio is used both for localization and communication. </p><p>Simulations comparing the performance of different codes corroborate the theoretical results and offer possible trade-off between information rate and clutter mitigation as well as a trade-off between choice of waveform-channel coding pairs. Experimental results from a brass-board microwave system in an indoor environment are also presented and discussed.</p> / Dissertation

Air- and spaceborne radars play an important role for civilian and military use. There are numerous applications such as earth observations, surveillance and others. High performance clutter suppression is a crucial part of many of these radar systems. Space time adaptive processing(STAP)has become a topic of interest for clutter suppression applications. Although for most moving target indication(MTI) radars other applications are used for clutter suppression. This master thesis analyses STAP on two antenna configuration for airborne radar applications. The first configuration is based on auxiliary antennas, the second configuration is based on a multitapering method called discrete prolate spheroidal sequences(DPSS). This theses shows that both antenna configurations are valid choices for STAP applications. Although the later configuration, DPSS, has a higher clutter suppression performance in general. However, there are fundamental limitations with the DPSS configuration. These limitations are shortly discussedin this theses but more work should be done before implementing the DPSS configuration

No description available.

Radar signal processing techniques aim to suppress clutter to enable target detection. Many clutter suppression techniques have been developed to improve the detection performance in literature. Among these methods, the most widely known is MTI plus coherent integrator, which gives sufficient radar performance in various scenarios. However, when the correlation coefficient of clutter is small or the spectral separation between the target and clutter is small, classical approaches to clutter suppression fall short. In this study, we consider the ARMA spectral estimation performance in sea clutter modelled by compound K-distribution through Monte Carlo simulations. The method is applied for varying conditions of clutter spikiness and auto correlation sequences (ACS) depending on the radar operation. The performance of clutter suppression using ARMA spectral estimator, which will be called ARMA-CS in this work, is analyzed under varying ARMA model orders. To compare the clutter suppression of ARMA-CS with that of conventional methods, we use improvement factor (IF) which is the ratio between the output Signal to Interference Ratio (SIR) and input SIR as performance measure. In all cases, the performance of ARMA-CS method is better than conventional clutter suppression methods when the correlation among clutter samples is small or the spectral separation between target and clutter is small.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Systems & Processes Engineering Corporation (SPEC) compared compression and decompression algorithms and developed optimal forms of lossless and lossy compression for hyperspectral data. We examined the relationship between compression-induced distortion and additive noise, determined the effect of errors on the compressed data, and showed that the data could separate targets from clutter after more than 50:1 compression.