A novel CMOS tunable bandpass filter and a novel voltage controlled oscillator are proposed. Both circuits are designed using the UMC 0.5£gm CMOS process parameters. The CMOS tunablebandpass filter is realised by using the intrinic parasitic capacitance of the MOS transistor. This filter has neither on-chip planar inductor nor poly-capacitance; therefore, the chip area is reduced. Simulation results show that the bandpass filter is tunable in the range between 190MHz and 347MHz. Therefore, the filter is suitable for the IF filter application that is between 200MHz and 300MHz. The Q-factor is also tunable and has a maximum value of 983. Applying the circuit of the bandpass filter, a second order voltage controlled oscillator is designed. Simulation results show that the voltage controllable oscillator is tunable in the range between 444MHz and 746MHz.

Linear tuning the frequency of an erbium-doped fiber ring laser using both a Fabry-Perot filter and an electro-optic tunable filter has been experimentally demonstrated. The rate of frequency change is determined by monitoring the fringes produced by laser light transmitted through a fiber Fabry-Perot interferometer. The fiber ring laser has been tuned over a 50 nm spectral range when using the Fabry-Perot filter and a tuning rate of 16480 nm/s has been achieved. The spectral width of the laser is 0.049 nm and the nearest sidelobe to the main peak is more than 30 dB below the central lobe. When the electro-optic tunable filter is used, a spectral range of 11 nm is reached. The spectral width is 2.33 nm and is in close agreement with the filter theoretical results. The sidelobe to main peak difference is around 13 dB.

In principle, general approaches to optimal nonlinear filtering can be described in a unified way from the recursive Bayesian approach. The central idea to this recur- sive Bayesian estimation is to determine the probability density function of the state vector of the nonlinear systems conditioned on the available measurements. However, the optimal exact solution to this Bayesian filtering problem is intractable since it requires an infinite dimensional process. For practical nonlinear filtering applications approximate solutions are required. Recently efficient and accurate approximate non- linear filters as alternatives to the extended Kalman filter are proposed for recursive nonlinear estimation of the states and parameters of dynamical systems. First, as sampling-based nonlinear filters, the sigma point filters, the unscented Kalman fil- ter and the divided difference filter are investigated. Secondly, a direct numerical nonlinear filter is introduced where the state conditional probability density is calcu- lated by applying fast numerical solvers to the Fokker-Planck equation in continuous- discrete system models. As simulation-based nonlinear filters, a universally effective algorithm, called the sequential Monte Carlo filter, that recursively utilizes a set of weighted samples to approximate the distributions of the state variables or param- eters, is investigated for dealing with nonlinear and non-Gaussian systems. Recentparticle filtering algorithms, which are developed independently in various engineer- ing fields, are investigated in a unified way. Furthermore, a new type of particle filter is proposed by integrating the divided difference filter with a particle filtering framework, leading to the divided difference particle filter. Sub-optimality of the ap- proximate nonlinear filters due to unknown system uncertainties can be compensated by using an adaptive filtering method that estimates both the state and system error statistics. For accurate identification of the time-varying parameters of dynamic sys- tems, new adaptive nonlinear filters that integrate the presented nonlinear filtering algorithms with noise estimation algorithms are derived. For qualitative and quantitative performance analysis among the proposed non- linear filters, systematic methods for measuring the nonlinearities, biasness, and op- timality of the proposed nonlinear filters are introduced. The proposed nonlinear optimal and sub-optimal filtering algorithms with applications to spacecraft orbit es- timation and autonomous navigation are investigated. Simulation results indicate that the advantages of the proposed nonlinear filters make these attractive alterna- tives to the extended Kalman filter.

Structural Health Monitoring (SHM) is the process of monitoring the state of a structure to determine the existence, location, and degree of damage that may exist within the entire structure. A structure’s health or level of damage can be monitored by identifying changes in structural or modal parameters. In this research, the structure’s health is monitored by identifying changes in structural stiffness. The Adaptive Least Mean Square (LMS) filtering approach is used to directly identify changes in structural stiffness for the IASC-ASCE Structural Health Monitoring Task Group Benchmark problem for both Phase I and II. The research focuses primarily on Phase II of the benchmark problem. In Phase II, modeling error and noise is introduced to the problem making the problem more realistic. The research found that the LMS filter approach can be used to detect damage and distinguish relative severity of the damage in Phase II of the benchmark problem in real time. Even though the LMS filter approach identified damage, a threshold below which damage is hard to identify exists. If the overall stiffness changes less than 10%, then identifying the presence and location of damage is difficult. But if the time of damage is known, then the presence and location can be determined. The research is of great interest to those in the structural health monitoring community, structural engineers, and inspection practitioners who deal with structural damage identification problems.

Video Imaging Vehicle Detection Systems (VIVDS) are steadily becoming the dominant method for the detection of vehicles at a signalized traffic approach. This research is intended to investigate the improvement of a queue and delay estimation algorithm (QDA), specifically the queue detection of vehicles during the red phase of a signal cycle. A previous version of the QDA used a weighted average technique that weighted previous estimates of queue length along with current measurements of queue length to produce a current estimate of queue length. The implementation of this method required some effort to calibrate, and produced a bias that inherently estimated queue lengths lower than baseline (actual) queue lengths. It was the researcher’s goal to produce a method of queue estimation during the red phase that minimized this bias, that required less calibration, yet produced an accurate estimate of queue length. This estimate of queue length was essential as many other calculations used by the QDA were dependent upon queue growth and length trends during red. The results of this research show that a linear regression method using previous queue measurements to establish a queue growth rate, plus the application of a Kalman Filter for minimizing error and controlling queue growth produced the most accurate queue estimates from the new methods attempted. This method was shown to outperform the weighted average technique used by the previous QDA during the calibration tests. During the validation tests, the linear regression technique was again shown to outperform the weighted average technique. This conclusion was supported by a statistical analysis of data and utilization of predicted vs. actual queue plots that produced desirable results supporting the accuracy of the linear regression method. A predicted vs. actual queue plot indicated that the linear regression method and Kalman Filter was capable of describing 85 percent of the variance in observed queue length data. The researcher would recommend the implementation of the linear regression method with a Kalman Filter, because this method requires little calibration, while also producing an adaptive queue estimation method that has proven to be accurate.

Programmable two-port polarization independent electro-optically wavelength tunable filters based on asymmetric Mach-Zehnder structure in LiNbO3 substrate have been developed for 1.55 µm application. The operation principle is based on Mach-Zehnder interference and TE↔TM polarization conversion. Fabrication parameters for channel waveguides, polarization converters and bandpass filters have been optimized. Straight channel waveguides 7 µm in width were produced by diffusing 1116 Å thick Ti into LiNbO3 substrate at 1035°C for 10 hours. Single mode guiding has been realized for both TE and TM polarization. Insertion loss of 2.9 dB for TE polarization input and 3.3 dB for TM polarization input were achieved on a 46 mm long sample. Single sideband programmable polarization mode converters were produced with 16 electrode sets, each containing 64 grating periods. Programmability was achieved by applying spatially periodic weighted independent voltages to interdigital electrode sets, and controlled electronically via a personal computer through a digital-to-analog converter array chip. Maximum conversion efficiency of more than 99% was realized for both TM→TE and TE→TM, and was observed at 1530.48 nm. Two-port polarization independent electro-optically tunable wavelength filters were produced based on the results obtained above. The 3 dB bandwidth is 1.1 nm and the nearest side lobes to the main transmission are down by about 9 dB for uniform coupling. Side lobes are reduced to about 20 dB below peak transmission after apodization, and the 3 dB bandwidths increased to ~ 1.3 nm as a result. Seven channels (channel -4, -2, -1, 0, +1, +2 and +4) were selectable by programming the voltages on each electrode set. Channel spacing is 1.1~1.2 nm. The tuning ranges from 1524.04 to 1533.56 nm. Fiber-to-fiber insertion loss of the filter at center frequency is 4.3 dB for TE input and 4.2 dB for TM input. The polarization dependent loss is < 0.5 dB for all selectable channels. The temporal response to a 21 V step change in applied voltages was measured to be 586 ns for the 10%-90% rise time and 2.308 µs for the 90%-10% fall time. This research work provides a convenient scheme for making programmable two-port tunable bandpass filters and ROADMs.

Modern satellite, wireless communications, and radar systems often demand wideband performance for multi-channel and multi-function operations. Among these applications, phased array antennas play an important role. This dissertation covers two wideband phased array antennas, one produces linear polarization and one produces circular polarization. The main difference between these two phased array antennas is the antenna array. For the linearly polarized array, a wideband microstrip line to slotline transition is used to feed a Vivaldi antenna. For the circularly polarized array, a wideband microstrip line to parallel stripline transition is used to feed a spiral antenna. From 3 to 12 GHz, the linearly polarized beam is steered over ± 15º. Since the electromagnetic spectrum is limited and has to be shared, interference is getting serious as more and more wireless applications emerge. Filters are key components to prevent harmonic interference. The harmonic signals can be suppressed by cascading additional lowpass filters or bandstop filters. A bandstop filter combining shunt open stubs and a spurline is proposed for a compact size and a deeper rejection. Two lowpass filters with interdigital capacitors and slotted ground structures are also studied. Harmonic suppression can also be achieved with the modification of bandpass filters. Three conventional bandpass filters with spurious passbands are investigated. The first one is a dual-mode patch bandpass filter. The second passband of the proposed filter is at 2.88fo, where fo is the fundametal frequency. The second filter is an open-loop bandpass filter. Two open stubs are added to achieve high suppression in the second harmonic signal. The suppression of 35 dB at the second harmonic is obtained. For the third filter using half-wavelength open stubs, a T-shaped line is used to replace the quarter-wavelength connecting line. The T-shaped line has the same response with the connecting line in the passband. Furthermore, the T-shaped line works as a bandstop filter at the second harmonic. Finally, a new compact slow-wave resonator and bandpass filters are presented. A simple transmission-line model is used to predict the resonant frequency. Compared with the conventional uniform half-wavelength resonator, the slow-wave resonator shows a 25% size reduction.

In this thesis, a simple on-chip automatic frequency tuning circuit is presented. The tuning circuit is improved from voltage-controlled filter (VCF) frequency tuning circuit. We use a single time constant (STC) circuit to substitute the voltage-controlled filter. The STC circuit can produce a controllable delay time clock. The tuning circuit uses the constant delay time to tune the frequency of the filter. The design of a STC circuit is easy. Because the circuit is simple, the tuning circuit has less chip area and less power consumption. The circuit has been fabricated with 0.35£gm CMOS technology. It operates with supply voltages ¡Ó1.5 V. The filter operates at a 3-dB frequency of 10MHz. In simulation, the frequency tuning circuit has a 3-dB frequency tuning error of less than 12% and the power consumption less than 9.05mW over a range of supply voltages (¡Ó10%), operating temperatures (-20¢J to 70¢J) and five models of SPICE model.

This thesis consists of two parts. The first part includes design and implementation of an RF tuner module for DVB-T receiver applications. The RF tuner module adopts single-conversion architecture and has a variable gain range of more than 60 dB. After improving the tracking filter characteristics, the module can achieve an image rejection of 60 dB. The second part is focused on DVB-T RF specification test for the implemented RF tuner module. This test uses the instruments accepted by DVB association to perform the standard measurement procedure. The measured sensitivity of the module is about -86 dBm, which has good ability to receive DVB-T signal in practical environment.

In decentralized data fusion system, if the probability model of the noise is Gaussian and the innovation informations from the sensors are uncorrlated,the information filtering technique can be the best method to fuse the information from different sensors. However, in the realistic environments, information filter cannot provide the best solution of state estimation and data integration when the noises are non-Gaussian and correlated. Since particle filter are capable of dealing with non-linear and non-Gaussian problems, it is an intuitive approach to replace the information filter by particle filter with some suitable data fusion techniques.In this thesis, we investigate a decentralized data fusion system with improved particle filters for target tracking. In order to achieve better tracking performance, the Iterated Extended Kalman Filter framework is used to incorporate the newest observations into the proposal distribution of the particle filter. In our proposed architecture, each sensor consists of one particle filter, which is used in generating the local statistics of the system state. Gaussian mixture model (GMM) is adopted to approximate the posterior distribution of the weighted particles in the filters, thereby more compact representations of the distribution for transmmision can be obtained. To achieve information sharing and integration, the GMM-Covariance Intersection algorithm is used in formulating the decentralized fusion solutions. Simulation resluts of target tracking cases in a sensor system with two sensor nodes are given to show the effectiveness and superiorty of the proposed architecture.