Ultra-wideband (UWB) technology is the next viable solution for applications in wireless personal area network (WPAN), body area network (BAN) and wireless sensor network (WSN). However, as application evolves toward a more realistic situation, wideband channel characteristics such as pulse distortion must be accounted for in channel modeling. Furthermore, application-oriented services such as ranging and localization demand fast prototyping, real-time processing of measured data, and good low signal-to-noise ratio (SNR) performance. Despite the tremendous effort being vested in devising new receivers by the global research community, channel-estimating Rake receiver is still one of the most promising receivers that can offer superior performance to the suboptimal counterparts. However, acquiring Nyquist-rate samples costs substantial power and resource consumption and is a major obstacle to the feasible implementation of the asymptotic maximum likelihood (ML) channel estimator. In this thesis, we address all three aspects of the UWB impulse radio (UWB-IR), in three separate contributions. First, we study the {\it a priori} dependency of the CLEAN deconvolution with real-world measurements, and propose a high-resolution, multi-template deconvolution algorithm to enhance the channel estimation accuracy. This algorithm is shown to supersede its predecessors in terms of accuracy, energy capture and computational speed. Secondly, we propose a {\it regularized} least squares time-of-arrival (ToA) estimator with wavelet denoising to the problem of ranging and localization with UWB-IR. We devise a threshold selection framework based on the Neyman-Pearson (NP) criterion, and show the robustness of our algorithm by comparing with other ToA algorithms in both computer simulation and ranging measurements when advanced digital signal processing (DSP) is available. Finally, we propose a low-complexity ML (LC-ML) channel estimator to fully exploit the multipath diversity with Rake receiver with sub-Nyquist rate sampling. We derive the Cram\'er-Rao Lower Bound (CRLB) for the LC-ML, and perform simulation to compare our estimator with both the $\ell_1$-norm minimization technique and the conventional ML estimator.

Aerosols are small, micrometer-sized particles, whose optical effects coupled with their impact on cloud properties is a source of large uncertainty in climate models. While their radiative forcing impact is largely of a cooling nature, there can be significant variations in the degree of their impact, depending on the size and the nature of the aerosols. The radiative and optical impact of aerosols are, first and foremost, dependent on their concentration or number density (an extensive parameter) and secondly on the size and nature of the aerosols (intensive, per particle, parameters). We employed passive (sunphotmetry) and active (backscatter lidar) measurements to retrieve extensive optical signals (aerosol optical depth or AOD and backscatter coefficient respectively) and semi-intensive optical signals (fine and coarse mode OD and fine and coarse mode backscatter coefficient respectively) and compared the optical coherency of these retrievals over a variety of aerosol and thin cloud events (pollution, dust, volcanic, smoke, thin cloud dominated). The retrievals were performed using an existing spectral deconvolution method applied to the sunphotometry data (SDA) and a new retrieval technique for the lidar based on a colour ratio thresholding technique. The validation of the lidar retrieval was accomplished by comparing the vertical integrations of the fine mode, coarse mode and total backscatter coefficients of the lidar with their sunphotometry analogues where lidar ratios (the intensive parameter required to transform backscatter coefficients into extinction coefficients) were (a) computed independently using the SDA retrievals for fine mode aerosols or prescribed for coarse mode aerosols and clouds or (b) computed by forcing the computed (fine, coarse and total) lidar ODs to be equal to their analog sunphotometry ODs. Comparisons between cases (a) and (b) as well as the semi-qualitative verification of the derived fine and coarse mode vertical profiles with the expected backscatter coefficient behavior of fine and coarse mode aerosols yielded satisfactory agreement (notably that the fine, coarse and total OD errors were <~ sunphotometry instrument errors). Comparisons between cases (a) and (b) also showed a degree of optical coherency between the fine mode lidar ratios.