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The rapid, accurate determination of the positions and strengths of sources of dangerous radioactivity takes high priority after a catastrophic event to ensure the safety of personnel, civilians, and emergency responders. This thesis presents approaches and algorithms to autonomously investigate radioactive material using an unmanned aerial vehicle. Performing this autonomous analysis comprises five major steps: ingress from a base of operations to the danger zone, initial detection of radioactive material, measurement of the strength of radioactive emissions, analysis of the data to provide position and intensity estimates, and finally egress from the area of interest back to the launch site. In all five steps, time is of critical importance: faster responses promise potentially saved lives. A time-optimal ingress and egress path planning method solves the first and last steps. Vehicle capabilities and instrument sensitivity inform the development of an efficient search path within the area of interest. Two algorithms—a grid-based recursive Bayesian estimator and a novel radiation contour analysis method—are presented to estimate the position of radioactive sources using simple gross gamma ray event count data from a nondirectional radiation detector. The latter procedure also correctly estimates the number of sources present and their intensities. Ultimately, a complete unsupervised mission is developed, requiring minimal initial operator interaction, that provides accurate characterization of the radiation environment of an area of interest as quickly as reasonably possible. / Master of Science

Time-delay estimation (TDE) finds application in a variety of problems, be it locating fractures or steering cameras towards the speaker in a multi-participant conference application. Underwater acoustic OFDM source localization is another important application of TDE. Existing underwater acoustic source localization techniques use a microphone array consisting of three or four sensors in order to effectively locate the source. Analog-to-digital (ADC) converters at these sensors call for a non-nominal investment in terms of circuitry and memory. A relatively inexpensive source localization algorithm is needed that works with the output of a single sensor. Since an inexpensive process for estimating the location of the source is desired, the ADC used at the sensor is capable only of a relatively low sampling rate. For a given delay, a low sampling rate leads to sub-sample interval delays, which the desired algorithm must be able to estimate. Prevailing TDE algorithms make some a priori assumptions about the nature of the received signal, such as Gaussianity, wide-sense stationarity, or periodicity. The desired algorithm must not be restrictive in so far as the nature of the transmitted signal is concerned. A time-delay estimation algorithm based on the time-frequency ratio of mixtures (TFRM) method is proposed. The experimental set-up consists of two microphones/sensors placed at some distances from the source. The method accepts as input the received signal which consists of the sum of the signal received at the nearer sensor and the signal received at the farther sensor and noise. The TFRM algorithm works in the time-frequency domain and seeks to perform successive source cancellation in the received burst. The key to performing source cancellation is to estimate the ratio in which the sources combine and this ratio is estimated by means of taking a windowed mean of the ratio of the spectrograms of any two pulses in the received burst. The variance of the mean function helps identify single-source regions and regions in which the sources mix. The performance of the TFRM algorithm is evaluated in the presence of noise and is compared against the Cramer-Rao lower bound. It is found that the variance of the estimates returned by the estimator diverge from the predictions of the Cramer-Rao inequality as the farther sensor is moved farther away. Conversely, the estimator becomes more reliable as the farther sensor is moved closer. The time-delay estimates obtained from the TFRM algorithm are used for source localization. The problem of finding the source reduces to finding the locus of points such that the difference of its distances to the two sensors equals the time delay. By moving the pair of sensors to a different location, or having a second time delay sensor, an exact location for the source can be determined by finding the point of intersection of the two loci. The TFRM method does not rely on a priori information about the signal. It is applicable to OFDM sources as well as sinusoidal and chirp sources. / Master of Science

In teleconferencing the audio quality is key to conducting successful meetings. The conference room setting imposes various challenges on the speech signal processing, such as noise and interfering signals, reverberation, or participants positioned far from the telephone unit. This work aims at improving the received speech signal of a conference telephone by implementing sound source localization and beamforming. The implemented microphone array signal processing techniques are compared to the performance of an existing multi-microphone solution and evaluated under various conditions using a planar uniform circular array. Recordings of test-sequences for the evaluation were performed using a custom-built array mockup. The implemented algorithms did not show good enough performance to motivate the increased computational complexity compared to the existing solution. Moreover, an increase in number of microphones used was concluded to have little or no effect on the performance of the methods. The type of microphone used was, however, concluded to have impact on the performance and a subjective listening evaluation indicated a preference for omnidirectional microphones which is recommended to investigate further. / God ljudkvalitet är en grundsten för lyckade telefonmöten. Miljön i ett konferens-rum medför ett flertal olika utmaningar för behandlingen av mikrofonsignalerna: det kan t.ex. vara brus och störningar, eller att den som talar befinner sig långt från telefonen. Målet med detta arbete är att förbättra den talsignal som tas upp av en konferenstelefon genom att implementera lösningar för lokalisering av talaren och riktad ljudupptagning med hjälp av ett flertal mikrofoner. De implementerade metoderna jämförs med en befintlig lösning och utvärderas under olika brusscenarion för en likformig cirkulär mikrofonkonstellation. För utvärderingen användes testsignaler som spelades in med en specialbyggd enhet. De implementerade algoritmerna kunde inte uppvisa en tillräcklig förbättring i jämförelse med den befintliga lösningen för att motivera den ökade beräkningskomplexitet de skulle medföra. Dessutom konstaterades att en fördubbling av antalet mikrofoner gav liten eller ingen förbättring på metoderna. Vilken typ av mikrofon som användes konstaterades däremot påverka resultatet och en subjektiv utvärdering indikerade en preferens för de rundupptagande mikrofonerna, en skillnad som föreslås undersökas vidare.

This thesis considers statistical issues in source localization from the received signal strength (RSS) measurements at sensor locations, under the practical assumption of log-normal shadowing. Distance information of source from sensor locations can be estimated from RSS measurements and many algorithms directly use powers of distances to localize the source, even though distance measurements are not directly available. The first part of the thesis considers the statistical analysis of distance estimation from RSS measurments. We show that the underlying problem is inefficient and there is only one unbiased estimator for this problem and its mean square error (MSE) grows exponentially with noise power. Later, we provide the linear minimum mean square error (MMSE) estimator whose bias and MSE are bounded in noise power. The second part of the thesis establishes an isomorphism between estimates of differences between squares of distances and the source location. This is used to completely characterize the class of unbiased estimates of the source location and to show that their MSEs grow exponentially with noise powers. Later, we propose an estimate based on the linear MMSE estimate of distances that has error variance and bias that is bounded in the noise variance.

The ability to correct balance disturbances is essential for maintaining upright stability. Recent literature highlights a potentially important role for the cerebral cortex in controlling compensatory balance reactions. The objective of this research was to provide a more detailed understanding of the specific neurophysiologic events occurring at the cortex following balance disturbances. More specifically, the focus was to determine whether the N1, a cortical potential evoked during balance control, and the error-related negativity (ERN), a cortical potential measured in response to errors during cognitive tasks, have similar cortical representation, revealing a similar link to an error detection mechanism. It was hypothesized that the N1 and ERN would have the same generator located in the anterior cingulate cortex (ACC). Fourteen healthy young adults participated in a balance task (evoked N1) and a flanker task (evoked ERN). Temporally unpredictable perturbations to standing balance were achieved using a lean and release cable system. Electromyography and centre of pressure were measured during the balance task. Reaction times and error rates were measured during the flanker task. Electroencephalography was recorded during both tasks. Source localization was performed in CURRY 6 using a single fixed coherent dipole model to determine the neural generator of the N1 and ERN. The results revealed that the locations of the N1 and ERN dipoles were different. The mean (n=9) distance between N1 and ERN dipoles was 25.46 ± 8.88 mm. The mean Talairach coordinates for the ERN dipole were (6.47 ± 3.08, -4.41 ± 13.15, 41.17 ± 11.63) mm, corresponding to the cingulate gyrus (Brodmann area 24). This represents the ACC, supporting results from previous literature. The mean Talairach coordinates for the N1 dipole were (5.74 ± 3.77, -11.81 ± 10.84, 53.73 ± 7.30) mm, corresponding to the medial frontal gyrus (Brodmann area 6). This is the first work to localize the source of the N1. It is speculated that the generator of the N1 is the supplementary motor area and that it represents the generation of a contingency motor plan to shape the later phases of the compensatory balance response based on sensory feedback from the perturbation.

<p>This thesis addresses the problem of source localization and time-varying spatial spectrum estimation with maneuverable arrays. Two applications, each having different environmental assumptions and array geometries, are considered: 1) passive broadband source localization with a rigid 2-sensor array in a shallow water, multipath environment and 2) time-varying spatial spectrum estimation with a large, flexible towed array. Although both applications differ, the processing scheme associated with each is designed to exploit array maneuverability for improved localization and detection performance.</p><p>In the first application considered, passive broadband source localization is accomplished via time delay estimation (TDE). Conventional TDE methods, such as the generalized cross-correlation (GCC) method, make the assumption of a direct-path signal model and thus suffer localization performance loss in shallow water, multipath environments. Correlated multipath returns can result in spurious peaks in GCC outputs resulting in large bearing estimate errors. A new algorithm that exploits array maneuverability is presented here. The multiple orientation geometric averaging (MOGA) technique geometrically averages cross-correlation outputs to obtain a multipath-robust TDE. A broadband multipath simulation is presented and results indicate that the MOGA effectively suppresses correlated multipath returns in the TDE.</p><p>The second application addresses the problem of field directionality mapping (FDM) or spatial spectrum estimation in dynamic environments with a maneuverable towed acoustic array. Array processing algorithms for towed arrays are typically designed assuming the array is straight, and are thus degraded during tow ship maneuvers. In this thesis, maneuvering the array is treated as a feature allowing for left and right disambiguation as well as improved resolution towards endfire. The Cramer Rao lower bound is used to motivate the improvement in source localization which can be theoretically achieved by exploiting array maneuverability. Two methods for estimating time-varying field directionality with a maneuvering array are presented: 1) maximum likelihood estimation solved using the expectation maximization (EM) algorithm and 2) a non-negative least squares (NNLS) approach. The NNLS method is designed to compute the field directionality from beamformed power outputs, while the ML algorithm uses raw sensor data. A multi-source simulation is used to illustrate both the proposed algorithms' ability to suppress ambiguous towed-array backlobes and resolve closely spaced interferers near endfire which pose challenges for conventional beamforming approaches especially during array maneuvers. Receiver operating characteristics (ROCs) are presented to evaluate the algorithms' detection performance versus SNR. Results indicate that both FDM algorithms offer the potential to provide superior detection performance in the presence of noise and interfering backlobes when compared to conventional beamforming with a maneuverable array.</p> / Dissertation

The ability to correct balance disturbances is essential for maintaining upright stability. Recent literature highlights a potentially important role for the cerebral cortex in controlling compensatory balance reactions. The objective of this research was to provide a more detailed understanding of the specific neurophysiologic events occurring at the cortex following balance disturbances. More specifically, the focus was to determine whether the N1, a cortical potential evoked during balance control, and the error-related negativity (ERN), a cortical potential measured in response to errors during cognitive tasks, have similar cortical representation, revealing a similar link to an error detection mechanism. It was hypothesized that the N1 and ERN would have the same generator located in the anterior cingulate cortex (ACC). Fourteen healthy young adults participated in a balance task (evoked N1) and a flanker task (evoked ERN). Temporally unpredictable perturbations to standing balance were achieved using a lean and release cable system. Electromyography and centre of pressure were measured during the balance task. Reaction times and error rates were measured during the flanker task. Electroencephalography was recorded during both tasks. Source localization was performed in CURRY 6 using a single fixed coherent dipole model to determine the neural generator of the N1 and ERN. The results revealed that the locations of the N1 and ERN dipoles were different. The mean (n=9) distance between N1 and ERN dipoles was 25.46 ± 8.88 mm. The mean Talairach coordinates for the ERN dipole were (6.47 ± 3.08, -4.41 ± 13.15, 41.17 ± 11.63) mm, corresponding to the cingulate gyrus (Brodmann area 24). This represents the ACC, supporting results from previous literature. The mean Talairach coordinates for the N1 dipole were (5.74 ± 3.77, -11.81 ± 10.84, 53.73 ± 7.30) mm, corresponding to the medial frontal gyrus (Brodmann area 6). This is the first work to localize the source of the N1. It is speculated that the generator of the N1 is the supplementary motor area and that it represents the generation of a contingency motor plan to shape the later phases of the compensatory balance response based on sensory feedback from the perturbation.