Optimal Design of Generalized Multiple Model Adaptive Controllers

Advanced analysis and optimal design techniques that achieve performance improvement for multiple model adaptive control (MMAC) and multiple model adaptive estimation (MMAE) based control are developed and tested for this dissertation research. An adjunct area of research yielded modified linear quadratic Gaussian (LQG) control design techniques that also can be applied to nonadaptive control. For the Modified LQG (MLQG) controller, the proposed designs remove the assumption that the Kalman filter as the observer and the controller gain matrix design are necessarily based on the same model as the best system model. The filter and controller gain matrices are both determined by models possibly other than the system model. In order to achieve optimal performance, the interrelationship of the system model to the filter and controller design models is established by minimizing a position correlation (mean square error on output) measure. Enhanced robustness is realized by considering the performance over the range of values of specified parameter(s) of the system model

Low order channel estimation for CDMA systems

New approaches and algorithms are developed for the identification and estimation of low order models that represent multipath channel effects in Code Division Multiple Access (CDMA) communication systems. Based on these parsimonious channel models, low complexity receivers such as RAKE receivers are considered to exploit these propagation effects and enhance the system performance. We consider the scenario where multipath is frequency selective slowly fading and where the channel components including delays and attenuation coefficients are assumed to be constant over one or few signalling intervals. We model the channel as a long FIR-like filter (or a tapped delay line filter) with the number of taps related to the ratio between the channel delay-spread and the chip duration. Due to the high data rate of new CDMA systems, the channel length in terms of the chip duration will be very large. With classical channel estimation techniques this will result in poor estimates of many of the channel parameters where most of them are zero leading to a reduction in the system performance. Unlike classical techniques which estimate directly the channel response given the number of taps or given an estimate of the channel length, the proposed techniques in this work will firstly identify the significant multipath parameters using model selection techniques, then estimate these identified parameters. Statistical tests are proposed to determine whether or not each individual parameter is significant. A low complexity RAKE receiver is then considered based on estimates of these identified parameters only. The level of significance with which we will make this assertion will be controlled based on statistical tests such as multiple hypothesis tests. Frequency and time domain based approaches and model selection techniques are proposed to achieve the above proposed objectives.The frequency domain approach for parsimonious channel estimation results in an efficient implementation of RAKE receivers in DS-CDMA systems. In this approach, we consider a training based strategy and estimate the channel delays and attenuation using the averaged periodogram and modified time delay estimation techniques. We then use model selection techniques such as the sphericity test and multiple hypotheses tests based on F-Statistics to identify the model order and select the significant channel paths. Simulations show that for a pre-defined level of significance, the proposed technique correctly identifies the significant channel parameters and the parsimonious RAKE receiver shows improved statistical as well as computational performance over classical methods. The time domain approach is based on the Bootstrap which is appropriate for the case when the distribution of the test statistics required by the multiple hypothesis tests is unknown. In this approach we also use short training data and model the channel response as an FIR filter with unknown length. Model parameters are then estimated using low complexity algorithms in the time domain. Based on these estimates, bootstrap based multiple hypotheses tests are applied to identify the non-zero coefficients of the FIR filter. Simulation results demonstrate the power of this technique for RAKE receivers in unknown noise environments. Finally we propose adaptive blind channel estimation algorithms for CDMA systems. Using only the spreading code of the user of interest and the received data sequence, four different adaptive blind estimation algorithms are proposed to estimate the impulse response of frequency selective and frequency non-selective fading channels. Also the idea is based on minimum variance receiver techniques. Tracking of a frequency selective varying fading channel is also considered.A blind based hierarchical MDL model selection method is also proposed to select non-zero parameters of the channel response. Simulation results show that the proposed algorithms perform better than previously proposed algorithms. They have lower complexity and have a faster convergence rate. The proposed algorithms can also be applied to the design of adaptive blind channel estimation based RAKE receivers

Uncertainty and disturbance estimator design to shape and reduce the output impedance of inverter

Power inverters are becoming more and more common in the modern grid. Due to their switching nature, a passive filter is installed at the inverter output. This generates high output impedance which limits the inverter ability to maintain high power quality at the inverter output. This thesis deals with an impedance shaping approach to the design of power inverter control. The Uncertainty and Disturbance Estimator (UDE) is proposed as a candidate for direct formation of the inverter output impedance. The selection of UDE is motivated by the desire for the disturbance rejection control and the tracking controller to be decoupled. It is demonstrated in the thesis that due to this fact the UDE filter design directly influences the inverter output impedance and the reference model determines the inverter internal electromotive force. It was recently shown in the literature and further emphasized in this thesis that the classic low pass frequency design of the UDE cannot estimate periodical disturbances under the constraint of finite control bandwidth. Since for a power inverter both the reference signal and the disturbance signal are of periodical nature, the classic UDE lowpass filter design does not give optimal results. A new design approach is therefore needed. The thesis develops four novel designs of the UDE filter to significantly reduce the inverter output impedance and maintain low Total Harmonic Distortion (THD) of the inverter output voltage. The first design is the based on a frequency selective filter. This filter design shows superiority in both observing and rejecting periodical disturbances over the classic low pass filter design. The second design uses a multi-band stop design to reject periodical disturbances with some uncertainty in the frequency. The third solution uses a classic low pass filter design combined with a time delay to match zero phase estimation of the disturbance at the relevant spectrum. Furthermore, this solution is combined with a resonant tracking controller to reduce the tracking steady-state error in the output voltage. The fourth solution utilizes a low-pass filter combined with multiple delays to increase the frequency robustness. This method shows superior performance over the multi-band-stop and the time delayed filter in steady-state. All the proposed methods are validated through extensive simulation and experimental results

Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking

Traditional tracking-by-detection systems typically employ Kalman filters (KF) for state estimation. However, the KF requires domain-specific design choices and it is ill-suited to handling non-linear motion patterns. To address these limitations, we propose two innovative data-driven filtering methods. Our first method employs a Bayesian filter with a trainable motion model to predict an object's future location and combines its predictions with observations gained from an object detector to enhance bounding box prediction accuracy. Moreover, it dispenses with most domain-specific design choices characteristic of the KF. The second method, an end-to-end trainable filter, goes a step further by learning to correct detector errors, further minimizing the need for domain expertise. Additionally, we introduce a range of motion model architectures based on Recurrent Neural Networks, Neural Ordinary Differential Equations, and Conditional Neural Processes, that are combined with the proposed filtering methods. Our extensive evaluation across multiple datasets demonstrates that our proposed filters outperform the traditional KF in object tracking, especially in the case of non-linear motion patterns -- the use case our filters are best suited to. We also conduct noise robustness analysis of our filters with convincing positive results. We further propose a new cost function for associating observations with tracks. Our tracker, which incorporates this new association cost with our proposed filters, outperforms the conventional SORT method and other motion-based trackers in multi-object tracking according to multiple metrics on motion-rich DanceTrack and SportsMOT datasets.Comment: 29 page

Multi-Stage Complex Notch Filtering for Interference Detection and Mitigation to Improve the Acquisition Performance of GPS

Continuous Wave Interferences (CWIs) can degrade the accuracy of a Global Positioning System (GPS) receiver and moreover it can completely deteriorate receiver’s normal operation. In this paper a low-cost anti-jamming system design is presented for the mitigation and detection of CWIs for GPS receivers. The anti-jamming system comprises of parameterizable Complex Adaptive Notch Filter (CANF) module which is able to detect and excise single or multiple CWIs. The CANF module is composed of a first, second and third order infinite-impulse response filter with an Auto-Regressive Moving Averager structure. The proposed CANF detects the existence of the CWI and estimates JNR level of incoming signal by using the statistical value of the adaptive parameter b0. The impact of the CANF module on the acquisition is analyzed. Moreover, a simple and innovative system level model is proposed which can utilize each CANF efficiently with threshold setting of JNR estimation within the adaptation block. Threshold setting parameters provide trade-off between effective excision of CWI, order of the filter and power consumption. This results in a parameterizable CANF module and provide effective solution for the mitigation of interferences with a high-power profile for GPS based applications

Attitude determination using an adaptive multiple model filtering Scheme

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations

A Vector Channel Based Approach to MIMO Radar Waveform Design for Extended Targets

Radar systems have been used for many years for estimating, detecting, classifying, and imaging objects of interest (targets). Stealthier targets and more cluttered environments have created a need for more sophisticated radar systems to gain more precise information about the radar environment. Because modern radar systems are largely defined in software, adaptive radar systems have emerged that tailor system parameters such as the transmitted waveform and receiver filter to the target and environment in order to address this need. The basic structure of a radar system exhibits many similarities to the structure of a communication system. Recognizing the parallel composition of radar systems and information transmission systems, initial works have begun to explore the application of information theory to radar system design, but a great deal of work still remains to make a full and clear connection between the problems addressed by radar systems and communication systems. Forming a comprehensive definition of this connection between radar systems and information transmission systems and associated problem descriptions could facilitate the cross-discipline transfer of ideas and accelerate the development and improvement of new system design solutions in both fields. In particular, adaptive radar system design is a relatively new field which stands to benefit from the maturity of information theory developed for information transmission if a parallel can be drawn to clearly relate similar radar and communication problems. No known previous work has yet drawn a clear parallel between the general multiple-input multiple-output (MIMO) radar system model considering both the detection and estimation of multiple extended targets and a similar multiuser vector channel information transmission system model. The goal of this dissertation is to develop a novel vector channel framework to describe a MIMO radar system and to study information theoretic adaptive radar waveform design for detection and estimation of multiple radar targets within this framework. Specifically, this dissertation first provides a new compact vector channel model for representing a MIMO radar system which illustrates the parallel composition of radar systems and information transmission systems. Second, using the proposed framework this dissertation contributes a compressed sensing based information theoretic approach to waveform design for the detection of multiple extended targets in noiseless and noisy scenarios. Third, this dissertation defines the multiple extended target estimation problem within the framework and proposes a greedy signal to interference-plus-noise ratio (SINR) maximizing procedure based on a similar approach developed for a collaborative multibase wireless communication system to optimally design wave forms in this scenario

Adaptive Log Domain Filters Using Floating Gate Transistors

In this thesis, an adaptive first order lowpass log domain filter and an adaptive second order log domain filter are presented with integrated learning rules for model reference estimation. Both systems are implemented using multiple input floating gate transistors to realize on-line learning of system parameters. Adaptive dynamical system theory is used to derive robust control laws in a system identification task for the parameters of both a first order lowpass filter and a second order tunable filter. The log domain filters adapt to estimate the parameters of the reference filters accurately and efficiently as the parameters are changed. Simulation results for both the first order and the second order adaptive filters are presented which demonstrate that adaptation occurs within milliseconds. Experimental results and mismatch analysis are described for the first order lowpass filter which demonstrates the success of our adaptive system design using this model-based learning method