Synchronization of distributed time difference of arrival (TDOA) sensor networks can be performed using reference signals from GPS satellites. This method provides high accuracy, but is vulnerable to jamming, and is not reliable enough to be used in military applications. A solution that does not depend on any signals transmitted from external actors is preferred. One way to achieve this is to use reference signals transmitted from a UAV. A UAV is suitable since only local synchronization for a geographically restricted area is necessary. The local synchronization is achieved by estimating the time-delay between the transmission and reception of a reference signal. The estimated time-delay can be used to detect drifts in the clocks of the TDOA sensors. This thesis analyzes com- mon reference signals, to evaluate which provide high accuracy for time-delay estimation, and what properties of the signals influence the estimation accuracy the most. The simulations show that the time-delay estimation performance can reach the same accuracy as synchronization against GPS for different types of signals. An increased bandwidth is more important than an increased signal length or signal-to-noise ratio to improve the estimation accuracy.

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Dentre os fatores limitantes dos sistemas de controle, o tempo morto está entre os mais críticos e de difícil detecção sem testes intrusivos. O conhecimento do seu valor é essencial para a identificação de modelos e na auditoria de desempenho de sistemas de controle. Em virtude disto, o presente trabalho propõe uma metodologia eficaz para estimá-lo utilizando apenas dados históricos de processo em malha fechada. A principal vantagem frente a técnicas disponíveis na literatura é a não necessidade de testes intrusivos. A metodologia é baseada em um tratamento de sinal para remoção dos efeitos do distúrbio não medido e dos erros de modelo. O tratamento de sinal consiste na minimização das oscilações do sinal erro em malha aberta suavizado como função do tempo morto. Diversas formulações de função objetivo e procedimentos de suavização foram estudados visando facilitar a estimação do parâmetro. A qualidade da metodologia é ilustrada através de simulações em uma série de cenários, os quais simulam processos lineares de diferentes características sob o efeito de distúrbios distintos. A metodologia também é testada frente a estudo de casos com dados reais de processo industrial em malhas de nível e temperatura. Os resultados são comparados com métodos da literatura e demonstram que o método proposto foi eficaz na estimação do tempo morto para a maioria dos casos. / Among the limiting factors of control systems, the pure time delay is one of the most critical and difficult to estimate without an intrusive perturbation. The knowledge of its value is essential for model identification and control loop performance assessment. This work proposes a methodology to determine dead time using ordinary closed loop operating data. The main advantage over available techniques is the non-necessity of intrusive plant tests. The proposed approach is based on a signal processing for removing the effects of the unmeasured disturbances and the model-plant mismatches. The signal processing consists of the minimization of the oscillations of the smoothing open loop error as a function of the pure time delay. Several objective function formulations and smoothing procedures were studied in order to facilitate parameter estimation. The quality of the methodology is illustrated by simulations in a series of scenarios, which simulate linear processes of different characteristics under the effect of different disturbances. The methodology is also tested in case studies with real industrial process data. Results are compared to literature approaches and show the method was effective to estimate the pure time delay for most cases.

<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

This thesis studies the design and implementation of an ultra-sonic water depth sounder. The depth sounder is implemented in a hand-held smart console used by divers. Since the idea of echo sounding is to measure the flight time between transmitting the signal and receiving the echo, the main challenge of this task is to find a time-of-flight (ToF) estimation for a signal in noise. It should be suitable for this specific application and robust when implemented in the device. The thesis contains an investigation of suitable ToF methods. More detailed evaluations of the matched filter, also known as the correlation method, and the linear phase approach are done. Aspects like pulse frequency and duration, speed of sound in water and underwater noise are taken into account. The ToF-methods are evaluated through simulation and experiments. The matched filter approach is found suitable based on these simulations and tests with signals recorded by the console. This verification leads to the implementation of the algorithm on the device. The algorithm is tested in real time, the results are evaluated and improvements suggested. / Denna rapport behandlar skattning av vattendjup med hjälp av ultraljudspulser och implementation av detta. Djupmätaren implementeras i en handhållen dykarkonsoll. Eftersom grundidén i ekolodning är att mäta tiden mellan att pulsen skickas iväg och att ekot tas emot är en stor del av utmaningen att hitta en lämplig metod för att skatta flykttiden för en signal i brus. Metoden ska passa för detta användingsområde och vara robust. Rapporten tar upp tidigare forskning gjord inom flykttidsestimering. De metoder som utvärderas för implementation är det matchade filtret, också kallad korrelationsmetoden, och linjär fas-metoden. Andra aspekter som avvägs och utreds är pulsfrekvens och pulsvaraktighet, ljudets hastighet och brus under vattnet. Metoderna för att skatta flykttid utvärderas genom simuleringar. Det matchade filtret bedöms vara lämpligt baserat på dessa simuleringar och experiment med data inspelad med konsollen. Denna verifikation leder till att algoritmen implementeras på konsollen. Den implementerade algoritmen testas i realtid, resultaten utvärderas och förbättringar föreslås.

Dentre os fatores limitantes dos sistemas de controle, o tempo morto está entre os mais críticos e de difícil detecção sem testes intrusivos. O conhecimento do seu valor é essencial para a identificação de modelos e na auditoria de desempenho de sistemas de controle. Em virtude disto, o presente trabalho propõe uma metodologia eficaz para estimá-lo utilizando apenas dados históricos de processo em malha fechada. A principal vantagem frente a técnicas disponíveis na literatura é a não necessidade de testes intrusivos. A metodologia é baseada em um tratamento de sinal para remoção dos efeitos do distúrbio não medido e dos erros de modelo. O tratamento de sinal consiste na minimização das oscilações do sinal erro em malha aberta suavizado como função do tempo morto. Diversas formulações de função objetivo e procedimentos de suavização foram estudados visando facilitar a estimação do parâmetro. A qualidade da metodologia é ilustrada através de simulações em uma série de cenários, os quais simulam processos lineares de diferentes características sob o efeito de distúrbios distintos. A metodologia também é testada frente a estudo de casos com dados reais de processo industrial em malhas de nível e temperatura. Os resultados são comparados com métodos da literatura e demonstram que o método proposto foi eficaz na estimação do tempo morto para a maioria dos casos. / Among the limiting factors of control systems, the pure time delay is one of the most critical and difficult to estimate without an intrusive perturbation. The knowledge of its value is essential for model identification and control loop performance assessment. This work proposes a methodology to determine dead time using ordinary closed loop operating data. The main advantage over available techniques is the non-necessity of intrusive plant tests. The proposed approach is based on a signal processing for removing the effects of the unmeasured disturbances and the model-plant mismatches. The signal processing consists of the minimization of the oscillations of the smoothing open loop error as a function of the pure time delay. Several objective function formulations and smoothing procedures were studied in order to facilitate parameter estimation. The quality of the methodology is illustrated by simulations in a series of scenarios, which simulate linear processes of different characteristics under the effect of different disturbances. The methodology is also tested in case studies with real industrial process data. Results are compared to literature approaches and show the method was effective to estimate the pure time delay for most cases.

Dentre os fatores limitantes dos sistemas de controle, o tempo morto está entre os mais críticos e de difícil detecção sem testes intrusivos. O conhecimento do seu valor é essencial para a identificação de modelos e na auditoria de desempenho de sistemas de controle. Em virtude disto, o presente trabalho propõe uma metodologia eficaz para estimá-lo utilizando apenas dados históricos de processo em malha fechada. A principal vantagem frente a técnicas disponíveis na literatura é a não necessidade de testes intrusivos. A metodologia é baseada em um tratamento de sinal para remoção dos efeitos do distúrbio não medido e dos erros de modelo. O tratamento de sinal consiste na minimização das oscilações do sinal erro em malha aberta suavizado como função do tempo morto. Diversas formulações de função objetivo e procedimentos de suavização foram estudados visando facilitar a estimação do parâmetro. A qualidade da metodologia é ilustrada através de simulações em uma série de cenários, os quais simulam processos lineares de diferentes características sob o efeito de distúrbios distintos. A metodologia também é testada frente a estudo de casos com dados reais de processo industrial em malhas de nível e temperatura. Os resultados são comparados com métodos da literatura e demonstram que o método proposto foi eficaz na estimação do tempo morto para a maioria dos casos. / Among the limiting factors of control systems, the pure time delay is one of the most critical and difficult to estimate without an intrusive perturbation. The knowledge of its value is essential for model identification and control loop performance assessment. This work proposes a methodology to determine dead time using ordinary closed loop operating data. The main advantage over available techniques is the non-necessity of intrusive plant tests. The proposed approach is based on a signal processing for removing the effects of the unmeasured disturbances and the model-plant mismatches. The signal processing consists of the minimization of the oscillations of the smoothing open loop error as a function of the pure time delay. Several objective function formulations and smoothing procedures were studied in order to facilitate parameter estimation. The quality of the methodology is illustrated by simulations in a series of scenarios, which simulate linear processes of different characteristics under the effect of different disturbances. The methodology is also tested in case studies with real industrial process data. Results are compared to literature approaches and show the method was effective to estimate the pure time delay for most cases.

Passive wireless sensor networks (WSNs) are quickly becoming popular for many applications such as article tracking, position location, temperature sensing, and passive data storage. Passive tags and sensors are unique in that they collect their electrical energy by harvesting it from the ambient environment. Tags with charge pumps collect their energy from the signal they receive from the transmitting source. The efficiency of converting the received signal to DC power is greatly enhanced using a power-optimized waveform (POW). Measurements in the first part of this dissertation show that a POW can provide efficiency gains of up to 12 dB compared to a sine-wave input. Tracking the real-time location of these passive tags is a specialized feature used in some applications such as animal tracking. A passive WSN that uses POWs for the improvement of energy-harvesting may also estimate the range to a tag by measuring the time delay of propagation from the transmitter to the tag and back to the transmitter. The maximum-likelihood (ML) estimator is used for estimating this time delay, which simplifies to taking the cross-correlation of the received signal with the transmitted signal. This research characterizes key aspects of performing range estimations in passive WSNs using POWs. The shape of the POW has a directly-measurable effect on ranging performance. Measurements and simulations show that the RMS bandwidth of the waveform has an inversely proportional relationship to the uncertainty of a range measurement. The clutter of an environment greatly affects the uncertainty and bias exhibited by a range estimator. Random frequency-selective environments with heavy clutter are shown to produce estimation uncertainties more than 20 dB higher than the theoretical lower bound. Estimation in random frequency-flat environments is well-behaved and fits the theory quite nicely. Nonlinear circuits such as the charge pump distort the POW during reflection, which biases the range estimations. This research derives an empirical model for predicting the estimation bias for Dickson charge pumps and verifies it with simulations and measurements.

The angular location of an acoustic source can be estimated by measuring an acoustic direction of incidence based solely on the noise produced by the source. Methods for determining the direction of incidence based on sound intensity, the phase of cross-spectral functions, and cross-correlation functions are available. In this current work, we implement Dominant Frequency SElection (DFSE) algorithm. Direction of arrival (DOA) estimation usingmicrophone arrays is to use the phase information present in signals from microphones that are spatially separated. DFSE uses the phase difference between the Fourier transformedsignals to estimate the direction ofarrival (DOA)and is implemented using a three-element ’L’ shaped microphone array, linear microphone array, and planar 16-microphone array. This method is based on simply locating the maximum amplitude from each of the Fourier transformed signals and thereby deriving the source location by solving the set of non-linear least squares equations. For any pair of microphones, the surface on whichthe time difference ofarrival (TDOA) is constant is a hyperboloidoftwo sheets. Acoustic source localization algorithms typically exploit this fact by grouping all microphones into pairs, estimating the TDOA of each pair, then finding the point where all associated hyperboloids most nearly intersect. We make use of both closed-form solutions and iterative techniques to solve for the source location.Acoustic source positioned in 2-dimensional plane and 3-dimensional space have been successfully located.

Digital Particle Image Velocimetry (DPIV) is a planar measurement technique to measure the velocity within a fluid by correlating the motion of flow tracers over a sequence of images recorded with a camera-laser system. Sophisticated digital processing algorithms are required to provide a high enough accuracy for quantitative DPIV results. This study explores the potential of a variety of cross-correlation filters to improve the accuracy and robustness of the DPIV estimation. These techniques incorporate the use of the Phase Transform (PHAT) Generalized Cross Correlation (GCC) filter applied to the image cross-correlation. The use of spatial windowing is subsequently examined and shown to be ideally suited for the use of phase correlation estimators, due to their invariance to the loss of correlation effects. The Robust Phase Correlation (RPC) estimator is introduced, with the coupled use of the phase correlation and spatial windowing. The RPC estimator additionally incorporates the use of a spectral filter designed from an analytical decomposition of the DPIV Signal-to-Noise Ratio (SNR). This estimator is validated in a variety of artificial image simulations, the JPIV standard image project, and experimental images, which indicate reductions in error on the order of 50% when correlating low SNR images. Two variations of the RPC estimator are also introduced, the Gaussian Transformed Phase Correlation (GTPC): designed to optimize the subpixel interpolation, and the Spectral Phase Correlation (SPC): estimates the image shift directly from the phase content of the correlation. While these estimators are designed for DPIV, the methodology described here provides a universal framework for digital signal correlation analysis, which could be extended to a variety of other systems. / Master of Science