Laser radars or lidar’s have been used extensively to remotely study winds within the atmospheric boundary layer and atmospheric transport. Lidar sensors have become an important tool within the meteorology and the wind energy community. For example, Doppler lidars are used frequently in wind resource assessment, wind turbine control as well as in atmospheric science research. A Time of Flight based (ToF) direct detection lidar sensor is used in vehicles to navigate through complex and dynamic environments autonomously. These optical sensors are used to map the environment around the car accurately for perception and localization tasks that help achieve complete autonomy.

This thesis begins with a detailed discussion on the fundamentals of a Doppler lidar system. The laser signal flow path to and from the target, the optics of the system and the core signal processing algorithms used to extract velocity information, were studied to get closer to the hardware of a Doppler lidar sensor. A Doppler lidar simulator was built to study the existing signal processing algorithms to detect and estimate doppler frequency, and radial velocity information. Understanding the sensor and its processing at the hardware level is necessary to develop new algorithms to detect and track specific flow structures in the atmosphere. For example, the aircraft vortices have been a topic of extensive research and doppler lidars have proved to be a valuable sensor to detect and track these coherent flow structures. Using the lidar simulator a physics based doppler lidar vortex algorithm is tested on simulated data to track a pair of counter rotating aircraft vortices.



At a system level the major components of a time of flight lidar is very similar to a Doppler lidar. The fundamental physics of operation is however different. While doppler lidars are used for radial velocity measurement, ToF sensors as the name suggests provides precise depth measurements by measuring time of flight between the transmitted and the received pulses. The second part of this dissertation begins to explore the details of ToF lidar system. A system level design, to build a ToF direct detection lidar system is presented. Different lidar sensor modalities that are currently used with sensors in the market today for automotive applications were evaluated and a 2D MEMS based scanning lidar system was designed using off-the shelf components.

Finally, a range of experiments and tests were completed to evaluate the performance of each sub-component of the lidar sensor prototype. A major portion of the testing was done to align the optics of the system and to ensure maximum field of view overlap for the bi-static laser sensor. As a laser range finder, the system demonstrated capabilities to detect hard targets as far as 32 meters. Time to digital converter (TDC) and an analog to digital converter (ADC) was used for providing accurate timing solutions for the lidar prototype. A Matlab lidar model was built and used to perform trade-off studies that helped choosing components to suit the sensor design specifications.

The size, weight and cost of these lidar sensors are still very high and thus making it harder for automotive manufacturers to integrate these sensors into their vehicles. Ongoing research in this field is determined to find a solution that guarantees very high performance in real time and lower its cost over the next decade as components get cheaper and can be seamlessly integrated with cars to improve on-road safety.

This thesis outlines the development of a vector retrieval technique, based on data assimilation, for a coherent Doppler LIDAR (Light Detection and Ranging). A detailed analysis of the Optimal Interpolation (OI) technique for vector retrieval is presented. Through several modifications to the OI technique, it is shown that the modified technique results in significant improvement in velocity retrieval accuracy. These modifications include changes to innovation covariance portioning, covariance binning, and analysis increment calculation. It is observed that the modified technique is able to make retrievals with better accuracy, preserves local information better, and compares well with tower measurements. In order to study the error of representativeness and vector retrieval error, a lidar simulator was constructed. Using the lidar simulator a thorough sensitivity analysis of the lidar measurement process and vector retrieval is carried out. The error of representativeness as a function of scales of motion and sensitivity of vector retrieval to look angle is quantified. Using the modified OI technique, study of nocturnal flow in Owens' Valley, CA was carried out to identify and understand uncharacteristic events on the night of March 27th 2006. Observations from 1030 UTC to 1230 UTC (0230 hr local time to 0430 hr local time) on March 27 2006 are presented. Lidar observations show complex and uncharacteristic flows such as sudden bursts of westerly cross-valley wind mixing with the dominant up-valley wind. Model results from Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) and other in-situ instrumentations are used to corroborate and complement these observations. The modified OI technique is used to identify uncharacteristic and extreme flow events at a wind development site. Estimates of turbulence and shear from this technique are compared to tower measurements. A formulation for equivalent wind speed in the presence of variations in wind speed and direction, combined with shear is developed and used to determine wind energy content in presence of turbulence.