Malaria remains a leading cause of mortality, particularly in regions with limited access to healthcare. Despite the availability of diagnostic methods such as microscopy, Rapid Diagnostic Tests (RDT), and Polymerase Chain Reaction (PCR), these methods face challenges in accuracy and practicality, especially in field conditions. This thesis investigates the use of automated, Computer-aided diagnostics (CAD) to improve in-the-field malaria diagnosis. While CAD shows promise, significant challenges remain in dealing with image imperfections and data variability. The study focuses on the use of blob detectors and Zernike decomposition to improve the precision of the CAD system. Additionally, data quality is investigated to evaluate the reliability of malaria diagnostic pipeline. The results suggest that Zernike decomposition is effective in identifying shapes most likely to correspond to parasites, enhancing the blob detector’s precision. Furthermore, the use of data stratification is shown to reduce the variance between models during training, which indicates that it can generalise better to unseen data. This work contributes to the ongoing effort to develop a robust, field-deploy-able malaria diagnostic tool by incorporating prior knowledge of the malaria parasite its shape to improve precision and ensuring reliability by evaluating data quality.

The thesis presents a novel approach to optimizing input computation for minimizing classification error in image classification tasks. It leverages the capabilities of the Soft Actor-Critic (SAC) algorithm, a reinforcement learning method tailored for continuous action spaces. The focus is on developing a real-time adaptable feedback loop that continuously learns and adjusts inputs based on classifier output probabilities. Key to this approach is the incorporation of a Gated Recurrent Unit (GRU) architecture within the SAC framework to capture temporal dependencies, addressing the challenge of ever-increasing state dimensions.

The Shack-Hartmann wavefront sensor is a widely-used device to measure the light wavefront. Cur- rently, the sensor is used as a gradient sensor, which is achieved by placing the microlens array in the plane conjugate to the deformable mirror and the aberration. The resulting spot pattern is then transformed into a gradient through the use of centroiding: the measurement of the relative movement of all spots compared to the reference image. As an alternative to the traditional alignment, alternative alignment moves the Shack-Hartmann wave- front sensor: the CCD is now optically conjugated to the deformable mirror and the aberrations, and not the gradients but the curvature of the wavefront is measured. This changes the behaviour of the Shack-Hartmann sensor: instead of the amount of spots remaining the same and the CCD region oc- cupied by them changing, the amount of spots starts to change but the CCD region occupied by them remains the same in the face of aberrations. Curvature measurements can be used directly to actuate bimorph and membrane deformable mirrors, bypassing the need for complex control schemes. As an alternative to centroiding Fourier demodulation can also be used to convert the information measured by the wavefront sensor into gradients. For large aberrations where the spots stray out of their subapertures and the centroiding algorithm breaks down this method is especially suited. In this thesis it is shown that a synergy exists between alternative alignment and Fourier demodulation. Large aberrations no longer occupy a huge region on the CCD, instead changing the amount of spots, and the movement of the spots outside of their subapertures is no longer a problem as there is no need for rigidly defined subapertures. The combined use of alternative alignment and Fourier demodulation was tested on simulations, both for square and circular apertures, and for cases with extra added Poisson noise. Finally real-life experi- ments were also conducted using circular apertures.

This thesis develops an approach for rigid body pose estimation using dual quaternion mathematics. Dual quaternions combine quaternion mathematics with concepts from dual numbers, offering a compact framework for describing the six-degrees-of-freedom of rigid body motion. Using quadrotor dynamics as a test case, this work formulates a dual quaternion model of dynamics and implements an Unscented Kalman Filter (UKF) for state estimation. Using concepts from Lie theory, the traditional UKF is extended to the unit dual quaternion manifold, while appropriately handling state covariance.

In addition to simulations of the dual quaternion Lie Algebraic UKF (LAUKF), results are presented of the LAUKF running on a purpose built flight computer. The flight computer software is developed using embedded Rust within the RTIC2 framework, and discussion is included on the benefits and challenges encounter when developing the flight computer.

Super-resolution microscopy has been demonstrated to surpass the diffraction limit, delivering high-resolution images. State-of-the-art super-resolution research continually strives to improve imaging resolution using existing super-resolution technology. This thesis aims to enhance super-resolution microscopy resolution through engineered illumination and establish a solid theoretical foundation to support resolution improvements....@en

The process industry is increasingly oriented towards automation. Use is made of both automated systems and human operators to control processes. For this control, operators can use computer systems to execute actions and conduct procedures. Automation of procedures can improve performance and safety, while reducing workload. Identifying procedures suitable for automation currently relies heavily on manual inspection and knowledge of operational staff.

This thesis proposes a method for the analysis of stored process data to identify opportunities for procedural automation in processing plants. The goal of this analysis is the identification of action patterns in operator responses to alarms that are predictable. Action patterns which are predictable can present good opportunities for automation.

The method utilizes the physical layout of the process, combined with a statistical analysis of events occurring in the event log to identify the relevant events in response to each alarm. Sequential pattern mining is then applied to those events. This thesis introduces a novel pattern type called ’independent frequent patterns’ to quantify patterns in response to an alarm. The pattern mining results are condensed into a value for the predictability of the response of an alarm.

In order to ensure validity of the patterns resulting from the method, a training and test set were constructed, consisting of alarms and the response patterns known to be the correct responses to those alarms. The method was designed using the training set and validated using the test set, by applying the method to the alarms in those sets and observing if the correct response patterns were identified. Then, the method was applied to alarms in the event log of which the correct response was not yet known, to rank those alarms on their potential for automation.

The thesis is conducted in collaboration with Shell Energy and Chemicals Park Rotterdam, and the method is applied to data supplied by them. The most common alarm messages are evaluated for the response pattern required to resolve them, the predictability of that pattern and the benefit of automating the response. From this, a ranked list of automation opportunities was constructed, which was then further discussed with operational staff.

We expand on the framework by Korda and Mezic (2020) to construct eigenfunctions directly from data by exploiting the eigenfunction PDE, guaranteeing closure and eliminating the need for a prior data dictionary.
The constructed models are extended to forced systems through the multi-step prediction error of a linear state-space model.

By identifying a relationship between ESPRIT and DMD applied to Hankel matrices, we simplify the original optimisation problem and significantly reducing the required model order.
A detailed numerical investigation of both autonomous dynamics and forced dynamics follows. For autonomous dynamics we report a VAF up to 90 % for a toy model and the Van der Pol oscillator, whilst the original work is unable to reconstruct the underlying dynamics on longer time scales. We were unable to reproduce accurate multi-step predictions under the influence of forcing.

We extend the constructed models by inclusion of monomial terms into the dynamics. This can be interpreted as a linear model with nonlinear output, approximated by a polynomial. Results on the Koopman generator and the inclusion of monomial terms suggest the construction of a bilinear model. A multi-step prediction is formulated, simplified and solved, expanding the predictive capabilities of the model. Whilst the inclusion of monomial terms improved the prediction of autonomous dynamics, the bilinear models failed to converge for the Duffing oscillator and Van der Pol oscillator.

We perform a further study on the constructed eigenfunctions by designing a new neural network architecture, aimed at learning Koopman eigenfunctions. The network architecture accurately recovers the autonomous dynamics of the system. The learned eigenfunctions suggest that the constructed eigenfunctions can be severely limited by the choice of the initial condition set Γ, opening the door for future research.

Linear Parameter-Varying (LPV) systems can be used as a bridge to extend the well studied model based control methods of Linear Time-Invariant systems to certain nonlinear systems. Despite significant attention in literature over the last two decades, finding an efficient global state space identification algorithm remains an open problem. Furthermore, a common assumption has been that the scheduling signals governing the time-varying dynamics are known or measured exactly. These drawbacks are found to inhibit the number of use cases for model based LPV control. This thesis explores new ways of identifying LPV systems for more general nonlinear systems with limited information on the optimal scheduling variable and scheduling dimension.

To this end, two novel rank constrained least squares problems are presented to identify system matrices and scheduling signals from input and full state measurements, and a dictionary of possible scheduling signals. The use of the methods is demonstrated in a simulation experiment, and the results are compared to existing full state measurement methods.

At last, the full state measurement methods are coupled with a state sequence estimation method from literature in order to obtain a method for identification of quasi-LPV systems from Input-Output measurements. Here "quasi" indicates that the scheduling signal is dependent on the system state and input. The scheme is flexible, and a proof of concept is given on a small nonlinear system.

Fast and accurate diagnosis of illnesses or other health complications is not accessible in many locations around the world. Due to this, illnesses are unnecessarily left untreated. Therefore, AiDx Medical has developed a portable automated diagnostic microscope with re liable and rapid AI-assisted detection specifically for low-resource settings.

Scan speed is a key issue for the popularisation of whole slide imaging systems [1]. To address this issue a recent paper by [29] proposed a Kalman filter-based scanning algorithm. This approach eliminates the necessity for focus map generation prior to scanning and in doing so, reduces the scan time. Importantly, the proposed approach requires no additional hardware, and is more robust to noise.

In this thesis, two modifications to this work are proposed. Firstly, higher order process models are used to generate more precise estimates of the best-in-focus positions. Secondly, a two-dimensional Non-Symmetric Half Plane Kalman filter is developed to incorporate neighbouring state estimates in the prediction – an approach previously thought inapplicable for this purpose [24]. In a simulation, the new scanning algorithms are applied to scan thin smear malaria specimens and compared to state-of-the-art focus map surveying procedures.

Deformable Mirror (DM) is the key part in the AO system. Characterization of DM is important as it maps between input voltage signal and output mirror deformation. The precision of characterization largely affects the control performance. While characterizing the DM with wavefront sensors is easy and straightforward, there is very few research on the characterizing the DM in a Wavefront sensorless (WFSless) setup. Existing research focuses on using Phase Diversity Phase Retrieval (PDPR) methods to retrieve phase, while introducing diversity phase by using various additional hardware.

This thesis tries to use PDPR methods to characterize the DM in a WFSless setup without using any additional hardware. Diversity phase are introduced by the DM itself and calibrated afterwards. Simulation results show that the proposed method is capable of characterizing the DM. Experiments on the setup are also conducted and further validates the proposed method, while unstable setup is accountable for the undesired results of the characterization.

Counter-acting image quality degradation caused by phase aberrations through physical correction requires the phase field to be known. As imaging hardware captures real-valued intensity, defined as the wave amplitude squared, obtaining this lost phase information is known as the phase retrieval problem and is a non-linear and non-convex optimisation problem.

Literature treats this problem from two points of view: reconstruction using indirect phase sampling, and reconstruction using Fourier amplitude sampling. The former employs wavefront sensors such as the Shack-Hartmann sensor, which encodes phase gradient information in the form of the displacement of imaged spots. These methods, while fast, discard information such as interference and are limited to low-order reconstruction. The latter, also called wavefront sensorless phase retrieval, uses the full point-spread function (PSF) to obtain high-accuracy reconstruction at the cost of computational speed, number of intensity images required, or limited aberration magnitude. These two points of view have remained largely separated, but can be made compatible through a modelling technique called Shack-Hartmann diversity. This thesis explores the merging of wavefront sensorless methods with Shack-Hartmann intensity patterns to leverage more information from a single captured image frame.

Firstly, a low-order modal reconstruction technique is presented applied to phase gradient fields obtained from Fourier demodulation of a Shack-Hartmann intensity pattern. A method of minimising the amount of redundant data-points used for reconstruction is illustrated through the removal of Fourier-interpolated data to speed up performance.

Secondly, a novel extension of the Fourier demodulation technique to hexagonal Shack-Hartmann arrays is presented, allowing phase gradient extraction and modal reconstruction of hexagonal array intensity images using Fourier demodulation.

Thirdly, Shack-Hartmann diversity is used to extend intensity-based modal phase retrieval using Taylor approximation of the intensity function to Shack-Hartmann intensity patterns. This bridges the gap between wavefront sensorless methods and Shack-Hartmann intensity patterns.

Lastly, a novel hybrid method is presented for high-accuracy phase reconstruction based on applying the above intensity-based method to a single Shack-Hartmann intensity pattern with low-order pre-conditioning obtained from Fourier demodulation. The method is demonstrated to obtain highly accurate reconstruction on low-order aberrations, and better reconstruction accuracy on small-magnitude high-order aberrations with dominating large-magnitude low-order terms than traditional methods. Potential use cases are discussed, such as open-loop turbulence reconstruction for use in turbulence modelling.

Model-based fault diagnosis methodologies rely on an accurate mathematical representation of a system's dynamics to effectively detect and localize faults. However, creating such models can be challenging, particularly for complex systems operating under diverse conditions. Furthermore, faults affecting the system can also modify its dynamics.
Given the limitations of model-based fault diagnosis, this study introduces a data-driven approach within the Blind System Identification framework. This approach can identify both the fault and the linear-time invariant model simultaneously. The mathematical formulation of this problem is expressed as a constrained least squares problem involving rank and sparsity constraints. To illustrate the application of this methodology, we demonstrate its effectiveness in diagnosing faults in Air Data Sensors using actual flight data obtained from the Cessna Citation II aircraft.

Single Molecule Localization Microscopy (SMLM) has enabled researchers to breakthrough the diffraction limit and obtain nanometer resolution images of macromolecular structures. But due to the time involved in obtaining ample data for proper image, the technique is venerable to many problem including fluctuations due to thermal gradients from surrounding which cause the frames to drift. SMLM relies on the stochastic blinking of fluorophore probes. Thus drift in SMLM could be explicitly modelled as a stochastic state space process. These models could be used to perform drift correction. Two state space models are proposed relying on different properties of SMLM.

The first model utilizes shifting of underlying image structure. The state space model for this property is constructed using shift matrices. A system identification method along with image reconstruction method is also derived to form the drift compensation algorithm for this model. This algorithm is further developed to provide adequate performance within low computational time. The second model relies on the position of emitter molecules and utilizes linking or pairing of fluorophore probes in succeeding frame to obtain the output data. Drift compensation algorithm for this model is constructed using Prediction Error Methods (PEM) and Kalman (RTS) smoother. The drift correction algorithm for these two models are also bench-marked with existing algorithms to obtain insight into performance. Furthermore, other properties of these algorithms are explored using simulation dataset and recommendation are provided for improvement and further research.

Specular light reflections are the mirror-like reflections from a material interface. They appear in the observation of any illuminated surface. Specular reflections can be set apart from the diffuse reflection type, which has a random distribution of reflection directions. The radiance of the specular reflected light is governed by the Fresnel ratio and depends on both geometrical and spectral properties. A clear distinction can be made between the reflection properties of dielectric and the much more reflective non-dielectric material types. In certain cases the presence of specular reflections is useful, for example in object identification or computer graphics applications. However, often the presence of specular reflection causes bright spots on the image of an object, which results in loss of detail in these areas. Particularly in computer vision applications it is important to only observe the intrinsic diffuse reflection. Hence, a variety of specular reflection removal methods have been developed. Color-space analysis specular reflection removal methods are able to accurately recover the intrinsic diffuse color of the image of an object. Optimization based methods based on a non-negative matrix factorization of the data are considered. The reduced characteristic representation of the data allows the specular and diffuse reflection component to be separated more easily. A multi-image approach is considered because it provides more available information on the intrinsic diffuse reflection component, which is typically a relatively weak signal for non-dielectric surface reflections. A specular reflection removal method is developed, that combines a new reflection model for colored illumination with a sparse Non-negative Matrix Factorization (NMF) optimization in a multi-image framework. The proposed method is evaluated on synthetic data and real data images of non-dielectric materials acquired conform the proposed reflection model.

When objects are imaged, often aberrations slip into the captured images. Many techniques exist to remove part of those aberrations using hardware, software or a combination of those. This research will focus on Phase Diversity for Blind Multi-Frame Deconvolution. In Blind Multi-Frame Deconvolution both the object and the aberrations are estimated from multiple images of the same object. Previous research has shown, Phase Diversity can be used to improve the quality of the reconstructed object in combination with Blind Multi-Frame Deconvolution. Using Defocus Phase Diversity is currently mainly used in literature.

In this research new Phase Diversity methods are proposed. When there is no knowledge on the object or aberrations, Phase Diversities are proposed to improve the object reconstruction compared to Defocus Phase Diversity.

We also propose a new method to optimize the Phase Diversity for the N + 1th image, when already N images have been taken. This method first determines the low values in the frequency spectra of the images. Next it estimates a Phase Diversity that results in a Modulation Transfer Function to enhances those frequencies. This method leads to an improved restoration quality in terms of Structural Similarity and Peak Signal to Noise Ratio when there is Gaussian noise or Poisson noise with a higher Signal to Noise Ratio. Recommendations are
done to improve the method for lower Signal to Noise Ratios.

Turbulent layers high in the atmosphere cause anisoplanatic phase aberrations, that are responsible for image degradation [6, 33, 46]. Adaptive Optics (AO) aims to correct for these aberrations by sensing them with a wavefront sensor and performing the correction with phase conjugate devices [18]. Various wavefront sensors have been designed and successfully implemented over the years, including the curvature sensor, interferometers, the Shack-Hartmann (SH) sensor and the pyramid sensor [7, 32, 40, 51]. In recent work the plenoptic sensor, an intermediate between the SH and pyramid sensor designs [10, 32, 37], has been proposed to improve on modern wavefront sensing. It has been reported to be beneficial in presence of strong or complex
wavefronts, such as deep turbulence conditions [18, 47]. The goal of this thesis is to investigate the SH and plenoptic wavefront sensors, with the
specific application on the correction of anisoplanatic aberrations that vary throughout the field of view. The contribution of this thesis to the field of AO is the development of a simulation toolbox in MATLAB. This toolbox is designed to simulate the SH and plenoptic sensors, providing a comparative study between the two. The comparative simulations reinforced results obtained from a corresponding Literature Survey. The SH sensor can outperform the plenoptic sensor on many occasions. If both sensors share the same microlens array (MLA) the plenoptic sensor scores worse in terms of performance metrics. By adjusting the MLA of the plenoptic sensor its dynamic range and sensitivity can be improved, such that it outperforms the SH sensor. Additionally, it was shown that the plenoptic sensor performs best for strong aberrations, simulated using
randomly-generated Kolmogorov screens. On the other hand, it fails in the presence of weak aberrations where the SH performs best. The developed toolbox allows for iso- and anisoplanatic aberrations to be reconstructed in a single frame, by differentiating between aperture plane reconstruction and phase screen retrieval. Multiple approaches are implemented in the toolbox, such that different reconstruction methods can be selected depending on experimental conditions.

Microscopic imaging has a resolution that is often far from the diffraction limit due to aberrations induced by the optics or by the sample itself. It is therefore of interest sensing these aberrations either directly or indirectly to improve image quality in post-processing or with adaptive optics. To avoid the use of extra hardware, several techniques are available to algorithmically retrieve aberrations in microscopy, but they often require images of an isolated point source, which is seldom a reasonable assumption as even single fluorescent beads often have a non negligible size. A different technique, known in literature joint estimation or phase diversity, was first developed by Gonsalves to estimate phase aberrations when imaging extended objects.
In this master thesis we will derive a simple physical model that accounts for the vectorial nature of light and use it to expand the algorithms derived from Gonsalves seminal papers. We will show with numerical simulation that the effect of polarization is indeed non negligible when imaging through high-NA lenses, and therefore a vectorial model can substantially decrease model deviation and improve the quality of the estimates. The novel algorithm is thoroughly tested both in simulation and with experimental data.

The ever-increasing industry desire for improved performance makes linear controller design run into its fundamental limitations. A nonlinear controller, such as Reset Control (RC), is needed to overcome these. RC is promising since, unlike other nonlinear methods, it easily integrates into the PID design framework preferred by industry. Thus far, closed-loop behaviour of RC has been analysed in the frequency domain either through Describing Function analysis or by direct closed-loop numerical computation. The former method computes a simplified closed-loop RC response by ignoring all harmonics, an approach which literature has found to inflict significant modelling errors. The latter method gives an accurate solution but does not provide understanding of how open-loop RC design affects closed-loop performance. No methods link these aspects, which impairs RC design and tuning. The main contribution of this work is aimed at providing this link, while achieving an accurate closed-loop RC model. A novel approach for modelling RC is considered, which uses state-dependent impulse inputs. This approach is shown to permit an accurate computation of closed-loop RC behaviour starting from an open-loop model, thus linking both aspects, enhancing system understanding. A frequency-domain description for closed-loop RC is obtained, as needed for the PID design framework, which is solved for analytically by inserting several well-defined assumptions. This solution is verified using a simulated high-precision stage, critically examining sources of modelling errors. The accuracy of the proposed method is further substantiated using controllers designed for various specifications.

The visualization of objects within or beyond a turbulent medium is hampered by the aberrations the medium induces in the wavefront. The sharpness of an image is maximal when the incoming wavefront is flat, with aberrated wavefronts yielding distorted images of limited utility. In the particular case of astronomy, the flat wavefront of the light from a distant target is aberrated as it moves through the atmosphere, before reaching our telescopes. The field of Adaptive Optics (AO) is dedicated to the correction of these aberrations, with the goal of enabling the imaging of objects through turbulent media with as much detail as possible. Due to a delay inherent to the AO control loop, each control input is used to correct an aberration slightly ahead in time, which means that, for the correction to be effective, the controller must be equipped with predictive capabilities. Prediction demands a model, and fortunately, the dynamics of atmospheric turbulence can be effectively modelled using a linear system. This makes the Kalman filter, the statistically optimal state estimator for linear systems, a natural choice for prediction. However, the steady-state Kalman filter requires a solution to the computationally intensive Discrete-time Algebraic Riccati Equation (DARE), which precludes its application to large-scale systems. Furthermore, the Kalman filter assumes a precise model with particular properties is available, which often is not the case in practice. The advent of extremely large telescopes demands prediction algorithms that can handle likewise extremely large system dimensions, which means that application of the steady-state Kalman filter, in its conventional form, is unfeasible. This thesis proposes a data-driven approach to Kalman filtering that exploits the intuitive sparsity pattern of the matrices involved in the modelling of an AO system. The developed algorithm replaces the DARE and, with knowledge of the system matrices, estimates the Kalman gain from measurement data, with the exploitation of sparsity providing a substantial drop in complexity when compared to the DARE, and with its data-driven nature inherently compensating, to a certain extent, for modelling errors. This thesis further proposes a two-stage approach to reduce the burden of the online prediction operation. While the measurement and state-transition matrices of the AO system are sparse, the Kalman gain is generally dense, consequentially slowing down prediction, which should accompany the sampling period of the loop. Our proposal splits prediction into a sparse stage for prediction of local structures in the wavefront phase, and a low-dimensional dense stage for prediction of the remaining low-frequency aberrations. The two-stage predictor is inherently suboptimal, but allows for quicker prediction and identification for extremely large systems. Via tuning, the user strikes a balance between performance and execution time.

Though NanoSats are becoming increasingly capable of featuring active payloads, and acquiring inertial data at arcsec level precision; the payload operational duty-cycle largely remains limited by its data downlink capabilities. NanoSat laser communication terminal (LCT) promises 1 Gbps data downlink capabilities. However, LEO to ground free space optical data transmission requires precision attitude knowledge under agile ground target tracking conditions. Under such stringent operational conditions, the calibration of gyro-stellar misalignment and scale factors is a well-established practice for traditional satellites. Nevertheless, the impact of such non-idealities in the context of MEMS gyro-stellar based NanoSat precision pose estimation is unclear. Furthermore, the gyro-stellar calibration performance that can be achieved with NanoSat ADCS system requires further investigation. Under agile operational conditions, slew rate induced star tracker drop-out is common for NanoSats, which limits the operational envelope of agile precision tracking missions. To increase our understanding of attitude knowledge estimation capability of today's NanoSats, this thesis work is extended at Hyperion Technologies. In collaboration with Hyperion Technologies, gyro-stellar sensors are characterised; and CubeCAT LCT is used to laydown the reference payload and mission requirements of an agile precision terrestrial target tracking NanoSat mission. Based on state steady noise PSD properties, and attitude knowledge requirements set by the LCT, gyro-stellar configurations are selected, and characterised. A rigid body attitude simulator is developed, considering the characterised sensor suite, to generate representative nominal and non-ideal sensor outputs under four different mission phases: 1.) Inertial pointing with no angular rate, 2.) Non-harmonic sinusoidal manoeuvres 3.) Agile precision ground target tracking and 4.) Inertial target tracking with non-zero angular rate. Reaction-wheel time delay and torque limits are evaluated to analyse the feasibility of attitude pointing requirements under agile target tracking conditions. Due to favourable properties of robustness to large initialisation errors, fast convergence, and preservation of attitude quaternion unity norm constraint; UnScented QUaternion Estimator (USQUE) variant of UKF based filter is synthesised for attitude estimation. The USQUE filter is further extended to facilitate the calibration of gyro-stellar misalignment and scale factors. It is demonstrated that the presence of gyro-stellar misalignment and scale factor, and star tracker occultation under agile slew rates significantly deteriorates the attitude estimation performance of USQUE based attitude estimator. Star tracker misalignments and dropouts are observed to have a significantly larger impact on the attitude knowledge estimation performance, when compared against MEMS rate-gyro scale factor and misalignment. Degraded MEMS rate gyro sensors are observed to have little impact on the attitude knowledge estimation performance of 6/7 state USQUE filter. However, it has a considerable impact on the convergence performance of star tracker misalignment calibration. More persistent calibration manoeuvres are observed to improve the star tracker misalignment parameter estimation performance. Gyro misalignment and scale factor calibration objectives were not met. A root-cause analysis showed that the signal distortions introduced by such non-idealities are below the gyro-stellar noise floor. Calibration filter synthesised has the potential to improve autonomy, reduce commissioning and ground calibration times, and enhance NanoSat pose estimation performance.