Third Winter School on Indian MST Radar March 5-9, 2001

IITM-NMRF-VKA-Training /Workshop programme on Wind profiler and Radio Acoustic Sounding System

Training /Workshop programme on Wind profiler and Radio Acoustic Sounding System

Signal detection and Processing techniques for Atmospheric radars

Dr. V.K.Anandan

National MST Radar Facility

Dept. of Space

1. Introduction

RADAR is the acronym for Radio Detection and Ranging. The radar invention has its roots in the pioneering research during nineteen twenties by Sir Edward Victor Appleton in UK and Breit and Tuve (1925) in USA on the detection of ionization layers in the upper atmosphere. The radar works on the principle that when a pulse of electromagnetic waves is transmitted towards a remotely located object, a fraction of the pulse energy is returned through either reflection or scattering, providing information on the object. The time delay with reference to the transmitted pulse and the received signal power provide respectively the range and the radar scattering cross-section of the target detected. These class of radars are known as pulse radar. In case the target is in motion when detected, the returned signal is Doppler shifted from the transmitted frequency and the measurement of the Doppler shift provides the line-of-sight velocity of the target. The radars having this capability are referred to as pulse Doppler radars. In addition to the above, if the location of the target is to be uniquely determined, it is necessary to know its angular position as well. The radars having this capability employ large antennas of either phased array or dish type to generate narrow beams for transmission and reception. Two major radars of this kind used for scientific research are the phased array radar of Jicamarca and dish antenna radar of Arecibo. Two important parameters that characterize the capability of a radar are its sensitivity and resolution for target detection. The sensitivity is determined by the peak power-aperture product and the resolution by the pulse volume which depends on the pulse length and the radar beam width. There are several variants to the above type of pulse radars that have been developed with varying degrees of complexity to meet the demands of application in various fields.

2. Atmospheric Radars

Radar can be employed, in addition to the detection and characterization of hard targets, to probe the soft or distributed targets such as the earth’s atmosphere. The atmospheric radars of interest to the current study are known as clear air radars and they operate typically in the VHF (30 –300 MHz) and UHF (300 MHz – 3GHz) bands (Rotteger and Larsen, 1990). The turbulent fluctuations in the refractive index of the atmosphere serve as a target for these radars

There is another class of radars known as weather radars which serve to observe the weather systems and they operate in the SHF band (3- 30 GHz) (Doviak and Zrnic, 1984). A major advance has been made in the radar probing of the atmosphere with the realization in early seventies, through the pioneering work of Woodman and Guillen (1974), that it is possible to explore the entire Mesosphere-Stratosphere-Troposphere (MST) domain by means of a high power VHF backscatter operating ideally around 50 MHz. It led to the concept of an MST radar and this class of radars have come to dominate the atmospheric radar scene over the past few decades.

An MST radar is a high power phase coherent radar operating typically around 50 MHz with an average power-aperture product exceeding about 5x107 Wm2. Radars operating at higher frequencies or having smaller power-aperture products are termed ST (Stratosphere-Troposphere) radars. In arriving at an optimum radar frequency for MST application, the main considerations are the frequency dependence of radar reflectivity for turbulent scatter and possible interference from other sources of sporadic nature. The weak radar reflectivity of the turbulent scatter coupled with a requirement of a few tens of meters of range resolution has called for the application of pulse compression and advanced signal and data processing techniques. In the next few sections are presented some of the basic concepts on pulse compression and signal and data processing as applied to the MST radars, which form the background to the study.

3. Signal Detectability and Pulse Compression

The efficiency of the radar system depends on how best it can identify the echoes in the presence of noise and unwanted clutter. The important parameters from the system point of view influence the radar returns are the average power of transmission and the antenna aperture size. Signal detectability is a measure of the radar performance in terms of transmission parameters.

3.1 Signal Detectability

One of the important parameters which decides the received power can be indirectly defined in terms of detectability factor (Farley, 1985). This important quantity is the received signal power Psig to the uncertainty dPn in the estimate of the noise power after averaging. For optimum
processing

Where Ae is the effective antenna area, Pt is the peak power transmitted, t is the pulse length, PRF is the pulse repetition frequency, Pave = PttPRF is the average transmitter power, Brec is the receiver band-width, Ts is the effective system noise temperature, Nc is the number of samples coherently added, Ninc is the number of resulting sums which are incoherently averaged, tc ~ 1/Bsig is the correlation time of the scattering medium for the radar wavelength used, Dt is the total integration time, h is the range or height and Dh is the height resolution. Before the operation of the coherent integration and incoherent integration, which comes in the digital domain, the signal is maximized at the receiver with a “matched” filter whose impulse response is the time inverse of the transmitted pulse. It is also assumed that the target fills the scattering volume defined by the beam pulse and shape length.

From equation (1.1) it is obvious that the average power is the important parameter for the strong returns and this is function of pulse length. Short pulses are required for good range resolution, and the shorter length of Inter pulse period (IPP) generates the problem of range ambiguity. Therefore maximum limit on the PRF is restricted due to the above problems. Pulse compression and frequency stepping are techniques which allow more of the transmitter average power capacity to be used without sacrificing range resolution.

3.2 Pulse Compression

As the name implies, a pulse of power P and duration t is in a certain sense converted into one of power nP and duration t/n. In the frequency domain compression involves manipulating the phases of the different frequency components of the pulse. In the time domain a pulse can be compressed via phase coding, especially binary phase coding, a technique which is particularly amenable to digital processing techniques. Since frequency is just the time derivative of phase, either can be manipulated to produce compression. Phase coding has been used extensively in atmospheric radars and in commercial & military applications.

The codes in general use fall in to a number of general classes

Barker codes: These were first discussed by Barker (1953) and have been used in Ionospheric incoherent scatter measurements. The distinguishing feature of these codes is that, the range side-lobes have a uniform amplitude of unity. The compression process only works, if the correlation time of the scattering medium is substantially longer than the full-uncompressed length of the transmitted pulse. The decoding involves adding and subtracting voltages, not powers. If the scattering centers move a significant fraction of a radar wavelength between time of arrival of the first and last baud of the pulse, the compression process will fail. This is never a problem in practice in Mesosphere, Stratosphere, Troposphere (MST) observations, but it can be a problem in ionospheric studies. Although 13 bauds is the longest possible binary Barker sequence (Unity side lobe), there are many longer sequences with side lobes that are only slightly larger which are used in radar observations (Woodman et al., 1980).

Complementary code pairs: Barker codes have range side lobes which are small, but which may still cause problems in MST applications. Ideally a codes which supports high compression ratios (long codes) to get the possible altitude resolution, but if we do so the signal from an altitude in the upper stratosphere, may be contaminated by range side lobe return from lower altitudes, since the scattered signal strength is a strong function of altitude, typically decreasing by 2-3 dB/km (Farley, 1985). This side lobe problem can be eliminated by the use of complementary codes.

The existence of complementary codes was first pointed out by Golay (1961) and has been discussed further in the literature (Rabiner and Gold; 1975,), but the severe restriction on their application to radar - phase changes introduced by the target must vary only a time scale much longer than the IPP - have prevented them being utilized in practice. The Doppler shifts encountered in military, civilian application, and in incoherent scatter from the ionosphere are too large. The Doppler shifts associated with MST radar observation on the other hand, are very small and are entirely compatible with the use of such codes. The medium correlation time is typically tens or hundreds of times longer than IPP.

Complementary phase codes are binary in their simplest form and they usually come in pairs. They are coded exactly as Barker codes, by a matched filter whose impulse response is the time reverse of the pulse. The range side lobes of the resulting ACF output for each pulse will generally be larger for a barker code of comparable length, but the two pulses are complementary pair have the property that their side lobes are equal in magnitude but opposite in sign, so that when outputs are added the side lobes exactly cancel, leaving only the central peak. This code is used in SOUSY radar (Schmidt et.al., 1979), Arecibo (Woodman, 1980) and Gadanki, India (Rao et.al., 1995).

4. Signal Processing

The decoding of the pulse compressed data and coherent integration need to be realized in real time. The decoding operation essentially involves cross correlating the incoming digital data with the replica of the transmit code. It is implemented by means of a correlator/transversal filter. Since decoding would normally require several tens of operations per msec, the implementation would be difficult in software. One approach that can be adopted is to apply coherent integration first and then decode the signal, which is implemented in Sousy radar (Woodman, 1983; Woodman et.al., 1984).

Until recently, most of the signal processor designs were based LSI ICs resulting in limited flexibility. The field of digital signal processing (DSP) has been a very active area of research and application for more than two decades. This broad development has paralleled in time the development of high-speed electronic digital computers, microelectronics and integrated fabrication technologies. An ever increasing assortment of integrated circuit parts specifically tailored to perform common DSP functions is available to the design engineers as system building blocks on parts-in-trade. Effective utilization of advanced DSP IC and fast digital to analog converter has made possible the implementation of decoding without integrating and the software coding in a later stage. In the new generation radars most of the signal processing is realized in firmware with the help of DSP ICs.

4.1 Data processing and parameter extraction

Figure 1shows the functional block diagram of various processing stages involved in the extraction and estimation of atmospheric parameters.

The complex time series of the decoded and integrated signal samples are subjected to the process of FFT for the on-line computation of the Doppler power spectra for each range bin of the selected range window. The Doppler spectra are recorded on a Hard disk for off-line processing. There is a provision, however, to record raw data (complex time samples) directly for any application, if so desired. The off-line data processing for parameterization of the Doppler spectrum follows closely the procedure adopted at the poker flat radar (Riddle, 1983). The computation involved in the various stages of operation and its advantages is given below.

Coherent Integration

The detected quadrature signals are coherently integrated for many pulse returns which lead to an appreciable reduction in the volume of the data to be processed and an improvement in the SNR. The coherent integration is made possible because of the over sampling of the Doppler signal resulting from the high PRF relative to the Doppler frequency. In other words, the coherence time of the scattering process tc is much greater than the sampling interval given by the inter pulse period tp. In the case of phase coding, a complementary pair of phase-coded pulse constitutes one radar cycle with a time interval of Tp (= 2tp). The odd and even pulses are coherently integrated and decoded separately before combining them to provide the complex time series for spectral analysis for each range gate. Since the integration is linear operation it can be performed before any decoding is carried out of the phase coded pulse returns (Woodman et.al., 1980).

The operation of coherent integration amounts to applying a low pass filter, whose time-domain representation is a rectangular window of Ti duration. The effects of coherent integration on the signal power spectrum have been discussed by Farley (1985). The signal spectrum is weighted by that of the integration filter sin2x/x2, where x = pfTi and f is the Doppler shift in Hz. The sampling operation at the integration time interval of Ti leads to frequency aliasing with signal power at frequencies f ± (m/Ti), where m is any integer, added to that at f. In the case of a flat spectrum, the filtering and aliasing balance each other and white noise still looks white, with no tapering at window edges. On the other hand, a signal peak with Doppler shift of 0.44/Ti Hz, near the edge of the aliasing window, will be attenuated by 3 dB by the filter function, whereas a peak near the center of the spectrum will be almost unaffected. One should, therefore, be conservative in choosing Ni for coherent integration so as to ensure that all signals of interest are in the central portion of the post-integration spectrum. The coherently integrated complementary pairs of coded signals are decoded for each range gate and added together to generate the final time series of the signal return for spectral analysis.