The European Union COST (Cooperation in Science and Technology) action on advanced weather radar systems is described. The associated five-year research project, which began in early 1993, has the objective to develop guideline specifications for a future generation of European operational radar systems. The authors describe the status of the project, the results reached so far in assessing and reviewing the potential improvements to conventional radars, the products and application of Doppler radar data, the contribution of polarimetric radars to the improvement of quantitative precipitation measurements and for nowcasting, and the possible development of electronically scanned systems. Problems to be tackled in the remaining years of the project are assessments of future technological feasibility, market forecasts, and cost/benefit investigations for the varied requirement profiles across Europe. It is intended to generate a high-level specification for the next generation of weather radars in Europe.

Advanced weather radar systems in Europe : The COST 75 action

A radar antenna intercepts thermal radiation from various sources including the ground, the sun, the sky, precipitation, and man-made radiators In the radar receiver, this external radiation produces noise that constructively adds to the receiver internal noise and results in the overall system noise Consequently, the system noise power is dependent on the antenna position and needs to be estimated accurately Inaccurate noise power measurements may lead to reduction of coverage if the noise power is overestimated or to radar data images cluttered by noise speckles if the noise power is underestimated Moreover, when an erroneous noise power is used at low-to-moderate signal-to-noise ratios, estimators can produce biased meteorological variables Therefore, to obtain the best quality of radar products, it is desirable to compute meteorological variables using the noise power measured at each antenna position In this paper, an effective method is proposed to estimate the noise power in real time

Radial-Based Noise Power Estimation for Weather Radars

Nonlinear FM (NLFM) waveforms offer a radar matched filter output with inherently low range sidelobes. This yields a 1-2 dB advantage in Signal-to-Noise Ratio over the output of a Linear FM (LFM) waveform with equivalent sidelobe filtering. This report presents details of processing NLFM waveforms in both range and Doppler dimensions, with special emphasis on compensating intra-pulse Doppler, often cited as a weakness of NLFM waveforms.

/pdf/sar-processing-with-non-linear-fm-chirp-waveforms-1ulnwocjts.pdf

SAR processing with non-linear FM chirp waveforms.

A multi-stage processing system receives data from signals that indicate information related to scatterers, such as signals from a Doppler scanning system. The multi-stage processing system tracks allowed processing time for the data. The multi-stage processing system performs a first stage of processing for the data to generate first estimates of spectral moments for the signals. The multi-stage processing system performs additional stages of processing for the data as the allowed processing time permits and stops the additional stages of processing for the data when the allowed processing time expires. The additional stages of processing may comprise generating second estimates for at least some of the spectral moments. The additional stages of processing may use methods having increasing complexity and accuracy.

Multi-stage processing for efficient and accurate spectral moment estimation

Systems and methods that adapt to the weather and clutter in a weather radar signal and apply a frequency domain approach that uses a Gaussian clutter model to remove ground clutter over a variable number of spectral components that is dependent on the assumed clutter width, signal power, Nyquist interval and number of samples. A Gaussian weather model is used to iteratively interpolate over the components that have been removed, if any, thus restoring any overlapped weather spectrum with minimal bias caused by the clutter filter. The system uses a DFT approach. In one embodiment, the process is first performed with a Hamming window and then, based on the outcome, the Hamming results are kept or a portion of the process is repeated with a different window. Thus, proper windows are utilized to minimize the negative impact of more aggressive windows.

System and method for processing data in weather radar

Dispersive pulse transmission and pulse compression upon reception is frequently used in radar to achieve high energy per pulse with low peak power while maintaining large bandwidth for fine range resolution. Pulse compression results in range sidelobes that must be suppressed for many applications. While suppression methods exist, they are very sensitive to unknown doppler shift, a situation frequently encountered in weather radar applications. We present a method for alleviating the doppler sensitivity. The method consists of processing a sequence of pulse echoes in three steps: (1) Pulse to pulse doppler filter in a doppler filter bank (usually an FFT); (2) heterodyning each doppler output from the bank with a signal that removes the doppler phase variation across the uncompressed pulse, i.e., along the range dimension; (3) pulse compression and range sidelobe suppression based upon zero doppier design. The particular criterion of sideloble suppression in this paper is minimization of integrated sidelobe level. INTRODUCTION Radars that are peak power limited to low or moderate levels may use dispersive pulse transmission and pulse compression upon reception to achieve high energy per pulse with low peak power. The dispersive transmission enables high bandwidth to be achieved for fine range resolution. The fine range resolution is obtained in the receiver by pulse compression or matched filtering. Matched filtering results in range sidelobes that can be troublesome in an extended clutter environment because of clutter “flooding” into the range increment being examined for the presence of scatterers and for the measurement of the charateristics of these scatterers. While there exist techniques for suppressing range sidelobes, these techniques are very sensitive to doppler frequency shifts in the echo. This is particularly significant in radar meteorology where the reflectors of interest are extended collections of small scatterers, such as precipitation. For mapping reflectivity, clutter “flooding” from range sidelobes can obscure the proper measurement of scatterer properties in the desired range increment. Sidelobe suppression in pulse compression radars is, in many respects, an established art, but there are some limitations to known techniques having to do with doppler shifted echoes. In previous approaches to range sidelobe suppression the varying phase shift, due to a doppler frequency shift over the duration of the uncompressed pulse, has been neglected. It has been thought to be too small to have a significant effect. We have found, however, that it is quite significant in that even moderate doppler shifts destroy the sidelobe suppression. Analysis and computation show that the quantity controlling the doppler sensitivity is the product of the uncompressed pulse duration and the doppler frequency shift. This product is measured by the doppler phase shift over the uncompressed pulse duration. Calling this the “doppler phase variation” we have doppler phase variation = 2nfdT, radians where fd is the doppler frequency shift in hertz and To is the uncompressed pulse duration in seconds. If the doppler frequency shift could be confined to a narrow band, then time sidelobe suppression could be very effective. Therefore, the technique or techniques to be described here separate sequences of echoes into several doppler “bins”, and time (i.e., range) sidelobe suppression is applied to each of the doppler bins. OUTLINE OF THE TECHNIQUE The technique is outlined in Figure 1. The received signal is downmixed to yield the complex envelope (I+jQ) which is then sampled by an N D converter. The next step is doppler filtering by K doppler channels using a pulse to pulse DFT to separate the groups of returns about the center doppler frequency of each filter. The doppler filter outputs still contain the doppler phase shifts along the range. Each doppler filter output is multiplied by a complex exponential giving the phase shift corresponding to the center frequency of the doppler filter to remove the doppler phase shift across the pulse. This doppler phase shift removal in the range dimension is crucial because it turns out that the sidelobe suppression doppler sensitivity results from the doppler phase shift over the duration of the uncompressed pulse. The resulting waveform is passed to the pulse compression and range sidelobe suppression filter. The compression and suppression filter design corresponds to the type of code transmitted with zero doppler on the return. The K filters operate in parallel to cover frequencies evenly distributed in the desired doppler velocity band [-Vmax,Vmax]. Because of the removal of the doppler phase shift after each doppler filter, the compression and suppression filters have a common design, being designed for zero doppler. We consider this to be an important aspect because it simplifies manufacture. The design of the combined pulse compression and range sidelobe suppression can take several forms; one form that we chose is a cascade of pulse compression followed by sidelobe suppression. The criteria upon which the suppressors may be designed include: 1. Minimizing the peak sidelobe. 2. Minimizing the integrated square of the sidelobes. DOPPLER FILTER RECEIVED COMPLEX ENVELOPE I +jQ ANALOG TO DIGITAL CONVERSION ZERO DOPPLER PULSE BANK COMPRESSION AND SIDELOBE 1 SUPPRESSION FILTERS BXP I-iZnflr?gl .--ITO ENVELOPE DETECTORS AND FURTHER PROCESSING F exp k i 2 n f ~ 1 r q ) Figure 1. Block diagram of doppler tolerant range sidelobe suppression. f,, fi, . . ., fKp1 are the doppler filter center frequencies. r is the integer time index.z, = range sampling period. 206 91-72810/92$03.00 0 IEEE 1992 3. Suppressing completely the sidelobes in a specified interval In this paper, we report only on the results using the second criterion. The integrated square of the sidelobes is called the “integrated sidelobe level” (ISL). The criterion on minimizing ISL is particularly appropriate when the interfering echoes or clutter is range extensive. In such a case, the interfering clutter power, for uniform clutter extent, is proportional to ISL. The suppression filter is designed in the form of a transversal filter (i.e., a finite impulse response filter). The design equations yield the coefficients or weights that are applied to the taps of the transversal filter. An outline of the derivation of the suppression filter coefficients is given in the Appendix. around the principal. RESULTS Figure 2 shows what conventional range sidelobe suppression can achieve when the echo has no doppler shift. Figure 3 shows the sensitively of sidelobe suppression to doppler sliift calulated for an 11 cm radar and a 33 microsecond transmitted pulse. This figure shows that while very great sidelobe suppression is achievable when the doppler frequency is known, the suppression is extremely sensitive to (unknown) doppler shift. Figure 4 compares the suppresssion capabilities of conventional and our doppler tolerant method of range sidelobe suppression. The superiority of the latter is evident. Figure 5 shows the loss in signal to noise ratio to be expected as a function of the number of suppression filter coefficients. For very deep sidelobe suppression, a loss of approximately 3 dB is to be expected.

Doppler Tolerant Range Sidelobe Suppression For Meteorological Radar With Pulse Compression

We present a distribution-free technique for estimating the first moment of a power density spectrum. The method determines a noise level by the Kolmogorov-Smirnov (K-S) test applied to the periodogram obtained from a disarete Fourier Transform (DFT) obtained from the complex envelope of radar echo. The K-S test gives results that are independent of the noise distribution and depend only upon the pulse to pulse noise samples being uncorrelated, a conditional almost always satisfied in a radar. After the noise level has been determined, it is removed from the periodogram. In simulation experiments we started with 64 data points in each replication, computed eight 8-point periodograms, averaged these to get a smoothed periodogram. The K-S test was applied to the smoothed periodogram and the first moment obtained from the noise removed smoothed periodogram. Results are tabulated for, specifically, an example of an asymmetrical power density spectrum. The results show quite acceptable accuracy for most radar meteorology applications. GLOSSARY OF PRINCIPAL SYMBOLS S(f,) = periodogram evaluated at frequency f, f, = n-th doppler frequency in received complex envelope Sck) = k-th ordered value of S(f,) M = total number of frequency components NI = number of frequency components used a step i. F,(k) = i-th empirical spectral distribution function TI, = K-S two sided statistic at step i S,,,,, = estimate of noise power density spectrum wIpa = critical value of TI, = quantile of the 2-sided K-S statistic K-S = Kolmogorov-Smirnov a = significance level of K-S test INTRODUCTION Methods for estimating spectral moments include time domain methods [ 1, 21 and frequency domain methods [ 1, 3-51. Although time domain methods have been popular, there are circumstances in which a frequency domain method must be used. One such circumstance arises in a technique for suppressing range sidelobes in pulse compression radar [6]. In the frequency domain methods, it becomes important to determine the noise level. Hildebrand and Sekhon [3] have described a method of determining the spectral density level of the noise. Their method involves eliminating, by successively decreasing thresholds, those spectral components with values greater than the noise spectral level. What remains is subjected to a sequence of tests assuming that the residue is white Gaussian noise. If the tests are passed, the spectral level obtained is taken as the noise power density spectrum. In this paper we present an alternative algorithm in which a distribution-free test is applied to each step of decreasing threshold. The test (or sequence of tests) is a Kolmogorov-Smirnov (K-S) test. The algorithm involves discarding high spectral values (or retaining low spectral values) until the application of the K-S test shows that a flat power density spectrum remains with a pre-determined probability of lying within a specified interval around the correct value: The advantages over the Hildebrand-Sekhon algorithm are: 1. A Gaussian process is not assumed. 2. There are definite, quantitative, criteria for deciding when the noise level has been reached and what that noise level is. An explanation of the algorithm follows. THE ALGORITHM FOR ESTIMATING MEAN DOPPLER The steps in the algorithm are best explained in connection with Figure I. We start with a sequence of pulse to pulse complex envelope values from each of a set of range bins. For one specific range bin at a time we obtain the discrete Fourier transform (DFT), usually by means of a Fast Fourier Transform (FFT), of a sequence of M such complex envelope values. Of course, the algorithm is to be applied to a large number of range bins, but the procedure is the same for all of the range bins. Now we enumerate the steps in the algorithm.

Obtaining Spectral Moments By Discrete Fourier Transform With Noise removal In Radar Meteorology

The use of complementary binary phase codes in a pulse compression radar system provides a way to reduce received clutter power from range sidelobes. Complementary codes are such that perfect cancellation of range sidelobes occurs under zero or known doppler frequency shift. However, like other forms of sidelobe suppression, complementary codes are extremely sensitive to doppler phase shifts across the return pulse. The usefulness of these codes is therefore limited by this sensitivity to doppler. We offer a processing technique for alleviating this sensitivity. The method consists of transmitting a set of pulses; half of them modulated with one of the pair of complemenatary phase codes and the other half of the sequence modulated with the other of the pair. The processing consists of pulse to pulse doppler filtering and each doppler filter output having the doppler phase shift along the pulse removed by heterodying with a wave having the opposite doppler frequency. INTRODUCTION Dispersed pulse transmission and pulse compression upon reception is frequently used in radar to achieve high energy per pulse with low peak power while maintaining large bandwidth for fine range resolution. The fine range resolution is obtained in the receiver by pulse compression or matched filtering. Matched filtering results in range sidelobes that can be troublesome in an environment of extended scatterers (such as preceipitation and other meteorological phenomena). The reason is echo “flooding” into the measurement of their properties. While we have investigated other means for sidelobe suppression, we offer here an alternative that can suppress these sidelobes to a very low level. This alternative uses paris of complementary sequences. Dopper Tolerant Sidelobe Elimination Basic complementary sequences are pairs of biphase sequences with the property that the sum of the time autocorrelation functions (obtained with pulse compression) has no sidelobes outside of the main lobe. This is illustrated in Figure 1. The absence of range sidelobes is a very desirable trait, but complete suppression of these sidelobes depends on the absence of a doppler frequency shift or the knowledge of the doppler shift so that it can be compensated. The technique for range sidelobe elimination involves the separation of the echo sequences into their respective doppler bins before matched filtering so that doppler tolerance is achieved. A typical transmitted set of pulses is illustrated in Figure 2. Of a total of 2L pulses the first L are modulated with phase code #1 of a complementary pair and the last L are modulated with the second phase code of the complementary pair. This sequence is processed as illustrated in Figure 3. In a digital or discrete time embodiment, the doppler filter bank is achieved by means of a discrete Fourier transform (DFT) (usually in the form of a Fast Fourier Transform (FIT) algorithm). The DFT operates on a sequence of L input echoes. L of the echoes come from one of the two sequences of the complementary sequence pair and the other L echoes come from the other sequence of the complementary sequence pair. The fact that all of mixer outputs have had all doppler removed results in all filters being designed for zero doppler frequency. All filters pairs are therefore identical. In this paper, the deleterious effects of range sidelobes are measured by the “integrated sidelobe Ibl COMPRESSED SEQUENCE A SEQUENCE A SEQUENCE B IC) COMPRESSED SEOUENCE B (dl SUM OF COMPRESSED SEOUENCES Figure 1. Example of a p,air of basic biphase complementary sequences, each of length 4. cp(t) is the pattern of phase changes of the transmitted waveform. ’The sum of the compressed sequences has no sidelobes. kLSEQUENCE # 1 PULSES + LSEOUENCE # Z PULSES -1 Figure 2. Transmit pulse train for doppler tolerant complementary code pulsa compression radar. DOPPLER Am FILTER DELAY LT12 -% MATCHED FILTER # 2 I DETECT, , TRACK. WEATHElR PROCESS. ETC.

http://ieeexplore.ieee.org/iel2/1014/12511/00578863.pdf

Using Complementary Sequences with Doppler Tolerance for Radar Sidelobe Suppression in Meteorological Radar

The mean cross section of a fluctuating radar echo is estimated by the sample mean of a sequence of pulse to pulse echo power measurements. The variance of the sample mean decreases with the number of measurements or observations, but not proportionally because, in general, the measurements are correlated. This phenomenon is well known, but the effect of receiver noise has been neglected in previous applications to radar meterology. This memorandum includes the effect of receiver noise upon the equivalent number of uncorrelated terms making up the sample mean. The results are given by equations (3.1) and (3.2). In addition, it is shown that doppler shift does not affect the equivalent number of uncorrelated pulses. GLOSSARY OF PRINCIPAL SYMBOLS’ = squared envelope of the radar echo plus receiver noise = echo power density spectrum = displaced positive portion of C(f). See equation (C.1). = carrier frequency = doppler frequency shift. = autocovariance function of b(t) = number of samples in the sample mean = equivalent number of uncorrelated samples = sample mean of M samples of b(t) N = receiver noise power nc(t) n,(t) Rb($ R,(T) = noise component of xc(t) = noise component of x,(t) = autocorrelation function of b(t) = autocorrelation function of x, (t), the inphase baseband component of x(t), and of x,(t), the quadrature baseband component of x(t) = crosscorrelation function of x,(t) and x,(t), the quadrature and inphase baseband components of x(t) = crosscorrelation function of x,(t) and xc(t), the quadrature and inphase baseband components of x(t)

http://ieeexplore.ieee.org/iel2/1014/12511/00578459.pdf

Estimating Mean Fluctuating Cross Section in the Presence of White Gaussian Noise

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Doppler Tolerant Range Sidelobe Suppression For Meteorological Radar With Pulse Compression

Obtaining Spectral Moments By Discrete Fourier Transform With Noise removal In Radar Meteorology

Using Complementary Sequences with Doppler Tolerance for Radar Sidelobe Suppression in Meteorological Radar

Estimating Mean Fluctuating Cross Section in the Presence of White Gaussian Noise