Communications: directive radio wave systems and devices (e.g. – Radar for meteorological use
Reexamination Certificate
2002-01-09
2003-06-10
Sotomayor, John B. (Department: 3662)
Communications: directive radio wave systems and devices (e.g.,
Radar for meteorological use
C342S192000, C342S195000, C342S196000, C342S193000
Reexamination Certificate
active
06577265
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The invention relates to the processing of data from signals that indicate information related to scatterers, such as signals from a Doppler scanning system, and in particular, to multi-stage processing that estimates the spectral moments of the scatterers enabling an effective trade-off between processing resources and accuracy.
2. Statement of the Problem
Doppler systems detect weather phenomena by reflecting signals off of rain, snow, dust, and debris carried by the wind. In a Doppler radar system, a transmitter emits a series of pulses that are scattered by these materials (scatterers). Some of the energy in the scattered pulses is reflected to a receiver. For a given pointing direction (azimuth and elevation), these pulses are sampled by the receiver as a function of time. Due to the propagation of the transmitted pulses, the time-sampling gives information as a function of range from the receiver. Often times, the transmitter is rotated, so that the continuous set of pulses also gives information as a function of the rotation angle. Typically, radars rotate around a vertical axes (conical, or azimuthal scanning) or along a plane perpendicular to the ground (scanning in elevation). Thus, Doppler systems obtain data that indicates the presence and motion of scatterers at various ranges and angles from the receiver. Doppler systems process this data to estimate the moments (i.e. the first three moments of the Doppler spectrum). The zeroth Doppler moment is related to the average intensity of the reflected energy of the scatterers. The first Doppler moment is related to the reflectivity-weighted velocity of the scatterers toward or away from the receiver (radial velocity). The second Doppler moment is related to the reflectivity-weighted variance of the relative motion of the scatterers. In this manner, Doppler systems are able to estimate wind patterns to aide in the detection of phenomena such as severe storms, tornadoes and turbulence.
Ideally, Doppler systems would only receive reflected energy that is associated with weather phenomenon, but unfortunately, Doppler systems also receive reflections from birds, insects, ground clutter, and other contaminants. To complicate the situation, Doppler systems are also affected by many types of spurious signals. Additional processing is required if atmospheric signals are to be effectively analyzed without corruption from contaminants.
Another problem faced by Doppler systems is the production of quality weather data in a timely manner. For phenomena such as severe storms and tornadoes, real time detection is essential. Doppler scanning at multiple azimuths and ranges generates a huge volume of data that can be contaminated by spurious reflections and signals. Typical real-time Doppler processing systems are simplistic and prone to errors in the presence of contamination. Existing Doppler processing systems that are sophisticated enough to eliminate these errors are too slow to handle real-time operation with vast quantities of data.
Another challenge faced by Doppler systems is the production of confidence values for the output data. The confidence values indicate a probability that the output data (e.g., the Doppler moments) is accurate. Systems that process the output data may use the confidence values to discount or ignore potentially inaccurate data. Currently, fast Doppler processing systems may weed-out some bad data based on signal-to-noise (SNR) thresholds, but these systems do not have the processing time or capacity to provide effective confidence values. Furthermore, this use of strict thresholding only provides for a binary (good/bad) indicator of the data quality. Currently, sophisticated Doppler systems that provide confidence values for their outputs are too slow to handle real-time operation with vast quantities of data.
Detailed Discussion of the Problem—
FIGS. 1-5
A problem for the Doppler system is the generation of accurate moments in real-time. The large volume of data produced when scanning Doppler measurement devices are used in applications, such as turbulence sensing and warning, and weather analysis, precludes the use of the accurate real-time processing of moments by highly sophisticated moment estimation methods. The typical real-time methods for processing of scanning radar and lidar data for meteorological applications are fast, but inaccurate at low signal-to-noise ratio (SNR) or in the presence of clutter, radio frequency interference (RFI) and other contaminants.
Radars and lidars use a transmitter and receiver to measure the radial motion of scatterers within the radar/lidar pulse volumes. The transmitter sends out a series of pulses in a given direction. A Fourier transform is applied to the complex time series of received amplitudes and phases for a given range to produce a Doppler spectrum for that range (see FIG.
1
). The spectra give information regarding the return power as a function of Doppler frequency. These frequencies are directly related to the Doppler radial velocities. As the device scans, a sequence of Doppler spectra is generated. The term “range gate” will be used to describe the range and pointing direction. In the following and without any loss of generality, it will be assumed that the radar is scanning in azimuth.
FIG. 2
shows a contour plot of Doppler spectra before processing for each range gate in a single pointing direction (azimuth).
Let the portion of the spectrum that is primarily influenced by the scatterers of interest (e.g., return from atmospheric scatterers), be referred to as the “signal.” If all of the scatterers were identical and moved at the same radial velocity, then the spectra would have significant amplitude only at the frequency that corresponds to that radial velocity. The amplitude of the signal would be proportional to the size and number of scatterers in the pulse interaction volume, the shape of this volume, and the transmitted power from the Doppler measurement device. If the scatterers have more than one velocity, the signal broadens. Theoretically, this broadened signal takes the form of a Gaussian shaped spectra, as is shown in FIG.
3
. The width of the signal is proportional to the variance in radial velocities in the pulse volume, which in turn, is indicative of turbulence. The width of the signal can be measured using either the second moment of the Doppler spectrum or the width of a Gaussian fit to the signal. The first moment or the center of a Gaussian fit can be used to measure the mean velocity of the scatterers. The signal power is proportional to the size and distribution of the scatterers in the pulse volume, and is given by the area of the Gaussian, or the zeroth moment.
For actual signals, the velocity mean and width are not always easy to calculate. The Doppler measurement systems have noise associated with the measurements, which contaminates the spectrum and may cause the signal to deviate from the theoretical Gaussian shape. Systematic variations in particle size and/or constitution (e.g. rain or ice) over the pulse volume can also cause the signal to lose its ideal shape. For weaker signals, the noise level can make the signal very difficult to identify. Techniques (e.g., phase randomization) to minimize the effect of scattering returns from beyond the device's maximum unambiguous range (second trip returns) can elevate the noise level and introduce random fluctuations onto the signal. Clutter, RFI, non-atmospheric objects, such as birds and insects, and radar anomalies can produce contamination in the spectra for radar. Other Doppler measurement devices, (e.g. acoustic radars, or sodars), have similar sources of contamination. The task is then to identify the atmospheric signal in the spectra and minimize the effect of contamination on the moment calculations.
Current Moment Estimation Methods
Current moment estimation methods fall into two categories: fast or highly accurate. The so-called pulse-pair and peak-picking methods are fast, but they do not address the
Beagley Nathaniel
Cornman Lawrence B.
Dalton Shelly D.
Goodrich Robert K.
Sotomayor John B.
University Corporation for Atmospheric Research
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