Russell E. Warren

Two-tone optical heterodyne spectroscopy with diode lasers: theory of line shapes and experimental results

This paper presents a theoretical analysis of the line shapes and signal-to-noise ratios obtained in two-tone optical heterodyne spectroscopy with tunable lead-salt diode lasers. The theory is described in terms of the frequency-modulation (FM) index β, the amplitude-modulation (AM) index M, their relative phase shift ψ, and the ratio of modulation frequency to the absorption-line half-width ν¯m. Synthetic spectra are presented for both Gaussian and Lorentzian line shapes and show considerable structural variation with the theoretical parameters. Experimental two-tone optical heterodyne spectra were obtained by modulating a specially modified lead-salt diode laser in the radio-frequency region. The experimental spectra obtained from NH3 absorption lines confirm the theoretical results.

Frequency modulation spectroscopy with lead–salt diode lasers: a comparison of single-tone and two-tone techniques

We present a theoretical and experimental comparison of single-tone and two-tone frequency modulation (FM) spectroscopy using lead-salt diode lasers. Our analysis reveals those diode laser operating characteristics that are necessary for high sensitivity performance using either technique for IR absorption measurements. High sensitivity performance using these techniques requires laser diodes having low incidental amplitude modulation and a small variation in FM/AM phase shift over a suitable diode tuning range. Neither requirement is met with the present mesa-stripe lead-salt diode laser technology.

Signal-to-noise ratio enhancement in frequency-modulation spectrometers by digital signal processing.

A signal-to-noise ratio enhancement of an order of magnitude was observed when digital signal-processing algorithms were applied to two diode-laser frequency-modulation spectrometers in the near-infrared and midinfrared spectral ranges. These algorithms include digital bandpass filters, a Wiener filter, a matched filter, and a least-squares fit. Digital signal processing has a practical advantage over other noise suppression techniques because it is easy to implement and to adapt to all experiment configurations without any physical modifications or additions to the spectrometer.

Rayleigh lidar system for middle atmosphere research in the arctic

A Rayleigh/Mie lidar system deployed at the Sondrestrom At- mospheric Research Facility located on the west coast of Greenland near the town of Kangerlussuaq (67.0 deg N, 50.9 deg W) has been in operation since 1993 making unique observations of the arctic middle atmosphere. The vertically directed lidar samples the elastically back- scattered laser energy from molecules (Rayleigh) and aerosols (Mie) over the altitude range from 15 to 90 km at high spatial resolution. The limited amount of arctic observations of the middle atmosphere currently available emphasizes the importance and utility of a permanent Rayleigh lidar system in Greenland. The lidar system consists of a frequency- doubled, 17-W Nd:YAG laser at 532 nm, a 92 cm Newtonian telescope, and a two-channel photon counting receiver. The principal objective of the lidar project is to contribute to studies concerned with the climatology and phenomenology of the arctic middle atmosphere. To this end, we describe the lidar system in detail, evaluate system performance, de- scribe data analysis, and discuss the system capabilities in determining the density, temperature, and the presence of aerosols in the arctic middle atmosphere. Particular emphasis is placed on the derivation of temperature from the lidar measurement and on the impact of signal- induced noise on this analysis. Also, we develop a statistical filter based on a Bayesian approach to optimally smooth the lidar profile in range. This filter preserves the short-term fluctuations in the low-altitude data consisting of relatively high SNR, whereas more smoothing is applied to the high-altitude data as the SNR decreases.

Kalman filtering of tunable diode laser spectrometer absorbance measurements

A recursive Kalman time-series filter was applied to absorbance measurements obtained with a tunable diode laser spectrometer. The spectrometer uses frequency modulation spectroscopy and a nearinfrared diode laser operating at 1.604 µm to monitor the CO(2)-vapor concentration in a 30-cm absorption cell. The Kalman filter enhanced the signal-to-noise ratio of the spectrometer by an order of magnitude when an absorbance of 6 × 10(-5) was monitored.

Detection and discrimination using multiple-wavelength differential absorption lidar

A methodology is presented for generalizing two-wavelength single-material differential absorption lidar to multiple wavelengths for use in simultaneous multimaterial detection and discrimination. A key role in the analysis is played by the concentration path length (CL) product covariance matrix ΛĈL, which generalizes the CL variance. Detection statistics for a multiwavelength alarm system are computed using ΛĈL with a multivariate normal distribution for the estimated CL product values. Off-diagonal elements in ΛĈL are found to affect significantly the predicted performance of two-material detection systems.

Adaptive Kalman-Bucy filter for differential absorption lidar time series data

An extension of the Kalman-Bucy algorithm for on-line estimation of multimaterial path-integrated concentration from multiwavelength differential absorption lidar time series data is presented in which the system model covariance is adaptively estimated from the input data. Performance of the filter is compared with that of a nonadaptive Kalman-Bucy filter using synthetic and actual lidar data.

Simultaneous estimation of aerosol cloud concentration and spectral backscatter from multiple-wavelength lidar data

We present a sequential algorithm for estimating both concentration dependence on range and time and backscatter coefficient spectral dependence of optically thin localized atmospheric aerosols using data from rapidly tuned lidar. The range dependence of the aerosol is modeled as an expansion of the concentration in an orthonormal basis set whose coefficients carry the time dependence. Two estimators are run in parallel: a Kalman filter for the concentration range and time dependence and a maximum-likelihood estimator for the aerosol backscatter wavelength and time dependence. These two estimators exchange information continuously over the data-processing stream. The state model parameters of the Kalman filter are also estimated sequentially together with the concentration and backscatter. Lidar data collected prior to the aerosol release are used to estimate the ambient lidar return. The approach is illustrated on atmospheric backscatter long-wave infrared (CO2) lidar data.

Optimum detection of small targets in a cluttered background

We discuss an optimum method for target detection where the input data consist of a time series of images in one or more data channels. Backgrounds are assumed to be complicated by nonstationary Gaussian clutter that is correlated over time and across channels and may be further corrupted by highly localized non-Gaussian interference terms that appear target-like. Because of the nonstationary clutter, methods based on the Fourier transform are impractical. Instead, we use a pixel-based autoregressive (AR) model. To deal with the inhomogeneous clutter, we segment the data into locally stationary regions; each region is then whitened using its estimated AR parameters, and an optimum matched filter is applied to the whitened data. The main contributions are the following. First, we develop a generalized AR model to describe multiple frames of multiple-channel data. Second, we introduce a novel automated method for the detection of small targets in nonstationary background. Third, we discuss some special applications such as the detection of small targets in non-Gaussian background clutter. We describe in detail the implementation of these techniques, and demonstrate their performance using both synthetic data and real data obtained from the Compact Airborne Spectrographic Imager (CASI).

Concentration estimation from differential absorption lidar using nonstationary Wiener filtering

An approach is presented for smoothing and differentiating path-integrated concentration estimates provided by range-resolved differential absorption lidar that is based on a nonstationary implementation of the Wiener-Kolmogorov filtering theory. The primary advantage of the method lies in its ability to provide filtered estimates that are smoothed relative to the local uncertainty in the input data. The approach is derived and illustrated on both synthetic and actual lidar data.