Dispersive Fourier Transformation for Versatile Microwave Photonics Applications

Abstract: Dispersive Fourier transformation (DFT) maps the broadband spectrum of an ultrashort optical pulse into a time stretched waveform with its intensity profile mirroring the spectrum using chromatic dispersion. Owing to its capability of continuous pulse-by-pulse spectroscopic measurement and manipulation, DFT has become an emerging technique for ultrafast signal generation and processing, and high-throughput real-time measurements, where the speed of traditional optical instruments falls short. In this paper, the principle and implementation methods of DFT are first introduced and the recent development in employing DFT technique for widespread microwave photonics applications are presented, with emphasis on real-time spectroscopy, microwave arbitrary waveform generation, and microwave spectrum sensing. Finally, possible future research directions for DFT-based microwave photonics techniques are discussed as well

Optical Signal Processing For Data Compression In Ultrafast Measurement

Today the world is filled with continuous deluge of digital information which are ever increasing by every fraction of second. Real-time analog information such as images, RF signals needs to be sampled and quantized to represent in digital domain with help of measurement systems for information analysis, further post processing and storage. Photonics offers various advantages in terms of high bandwidth, security, immunity to electromagnetic interference, reduction in frequency dependant loss as compared to conventional electronic measurement systems. However the large bandwidth data needs to be acquired as per Nyquist principle requiring high bandwidth electronic sampler and digitizer. To address this problem, Photonic Time Stretch has been introduced to reduce the need for high speed electronic measurement equipment by significantly slowing down the speed of sampling signal. However, this generates massive data volume. Photonics-assisted methods such as Anamorphic Stretch Transform, Compressed Sensing and Fourier spectrum acquisition sensing have been addressed to achieve data compression while sampling the information. In this thesis, novel photonic implementations of each of these methods have been investigated through numerical and experimental demonstrations. The main contribution of this thesis include (1) Application of photonic implementation of compressed sensing for Optical Coherence Tomography, Fiber Bragg Grating enabled signal sensing and blind spectrum sensing applications (2) Photonic compressed sensing enabled ultra-fast imaging system (3) Fourier spectrum acquisition for RF spectrum sensing with all-optical approach (4) Adaptive non-uniform photonic time stretch methods using anamorphic stretch transform to reduce the the number of samples to be measured

Beating Nyquist with Ultrafast Optical Pulses

Photonic sources readily provide several THz of analog bandwidth for information processing. Taking advantage of this fact, problems such as ultrawideband radio (RF) spectrum sensing, high performance radar, and analog-to-digital conversion can achieve significant performance gains with photonic techniques. Likewise, photonic imaging systems such as time-stretch microscopy have produced a breakthrough in continuous high speed imaging, enabling faster shutter speeds, higher frame rates, and greater gain-bandwidth product than is possible with continuous read-out CCDs and CMOS sensor arrays. However, imaging at this rate with traditional Nyquist sampling inevitably yields sustained data output on the order of 100 Gb/s or more, creating a significant challenge for storage and transmission. Real images and video are highly compressible, so this deluge of data is also highly inefficient. This thesis will address several techniques based on chirp-processing of ultrafast laser pulses that demonstrate real-time efficient compression of both electronic and optical signals, overcoming electronic bottlenecks via optical processing in the analog domain. Several systems will also be presented that permit greater information extraction from high throughput microscopy experiments by measuring quantitative phase images on a time-stretch microscope

Optical Signal Processing For Data Compression In Ultrafast Measurement

Today the world is filled with continuous deluge of digital information which are ever increasing by every fraction of second. Real-time analog information such as images, RF signals needs to be sampled and quantized to represent in digital domain with help of measurement systems for information analysis, further post processing and storage. Photonics offers various advantages in terms of high bandwidth, security, immunity to electromagnetic interference, reduction in frequency dependant loss as compared to conventional electronic measurement systems. However the large bandwidth data needs to be acquired as per Nyquist principle requiring high bandwidth electronic sampler and digitizer. To address this problem, Photonic Time Stretch has been introduced to reduce the need for high speed electronic measurement equipment by significantly slowing down the speed of sampling signal. However, this generates massive data volume. Photonics-assisted methods such as Anamorphic Stretch Transform, Compressed Sensing and Fourier spectrum acquisition sensing have been addressed to achieve data compression while sampling the information. In this thesis, novel photonic implementations of each of these methods have been investigated through numerical and experimental demonstrations. The main contribution of this thesis include (1) Application of photonic implementation of compressed sensing for Optical Coherence Tomography, Fiber Bragg Grating enabled signal sensing and blind spectrum sensing applications (2) Photonic compressed sensing enabled ultra-fast imaging system (3) Fourier spectrum acquisition for RF spectrum sensing with all-optical approach (4) Adaptive non-uniform photonic time stretch methods using anamorphic stretch transform to reduce the the number of samples to be measured

Iterative synthetic aperture radar imaging algorithms

Synthetic aperture radar is an important tool in a wide range of civilian and military imaging applications. This is primarily due to its ability to image in all weather conditions, during both the day and the night, unlike optical imaging systems. A synthetic aperture radar system contains a step which is not present in an optical imaging system, this is image formation. This is required because the acquired data from the radar sensor does not directly correspond to the image. Instead, to form an image, the system must solve an inverse problem. In conventional scenarios, this inverse problem is relatively straight forward and a matched lter based algorithm produces an image of suitable image quality. However, there are a number of interesting scenarios where this is not the case. Scenarios where standard image formation algorithms are unsuitable include systems with data undersampling, errors in the system observation model and data that is corrupted by radio frequency interference. Image formation in these scenarios will form the topics of this thesis and a number of iterative algorithms are proposed to achieve image formation. The motivation for these proposed algorithms is primarily from the eld of compressed sensing, which considers the recovery of signals with a low-dimensional structure. The rst contribution of this thesis is the development of fast algorithms for the system observation model and its adjoint. These algorithms are required by large-scale gradient based iterative algorithms for image formation. The proposed algorithms are based on existing fast back-projection algorithms, however, a new decimation strategy is proposed which is more suitable for some applications. The second contribution is the development of a framework for iterative near- eld image formation, which uses the proposed fast algorithms. It is shown that the framework can be used, in some scenarios, to improve the visual quality of images formed from fully sampled data and undersampled data, when compared to images formed using matched lter based algorithms. The third contribution concerns errors in the system observation model. Algorithms that correct these errors are commonly referred to as autofocus algorithms. It is shown that conventional autofocus algorithms, which work as a post-processor on the formed image, are unsuitable for undersampled data. Instead an autofocus algorithm is proposed which corrects errors within the iterative image formation procedure. The proposed algorithm is provably stable and convergent with a faster convergence rate than previous approaches. The nal contribution is an algorithm for ultra-wideband synthetic aperture radar image formation. Due to the large spectrum over which the ultra-wideband signal is transmitted, there is likely to be many other users operating within the same spectrum. These users can produce signi cant radio frequency interference which will corrupt the received data. The proposed algorithm uses knowledge of the RFI spectrum to minimise the e ect of the RFI on the formed image