Re: Radar Engineering By G S N Raju Download From
Leana Eckes
Research in RSL runs the gamut from theoretical signal processing and concept development to experimental system development and open-air demonstration of advanced radar capabilities. Current topics of investigation include MIMO, waveform agility, adaptive transmission, dual-function radar/communication and spectrum sharing operation, direction-finding and signal classification, adaptive receive processing, and more. Radar research encompasses multiple disciplines including RF systems engineering, estimation & detection theory, high-performance computing (including real-time operation), optimization theory, and signal processing. Faculty and students are engaged in a wide variety of projects ranging from the design, construction, and experimentation of actual radar systems in hardware to the theoretical analysis and development of advanced mathematical concepts.
Jon Owen / Charles Mohr / Brandon Ravenscroft -- 1st Place, Student Paper Contest, 2022 IEEE Radar Conference, New York City, NY for "Real-time experimental demonstration and evaluation of open-air sense-and-notch radar
Christian Jones / Zeus Gannon / Jon Owen -- 2nd Place, Student Paper Contest, 2022 IEEE Radar Conference, New York City, NY for "Development & experimental assessment of robust direction finding and self-calibration"
Christian Jones / Brandon Ravenscroft -- 1st Place, Student Paper Contest, 2021 IEEE Radar Conference, Atlanta, GA for "Computationally efficient joint-domain clutter cancellation for waveform-agile radar"
Christian Jones / Lumumba Harnett / Charles Mohr -- 2nd Place, Student Paper Contest, 2020 IEEE International Radar Conference, Washington, DC for "Structure-based adaptive radar processing for joint clutter cancellation and moving target estimation"
Brandon Ravenscroft / Jonathan Owen -- Finalist, Student Paper Contest, 2019 IEEE Radar Conference, Boston, MA for "Optimal mismatched filtering to address clutter spread from intra-CPI variation of spectral notches"
Dr. Cenk Sahin / Dr. Justin Metcalf -- Finalist, Best Industry Paper Contest, 2017 IET International Conference on Radar Systems, Belfast, UK for "Characterization of range sidelobe modulation arising from radar-embedded communications"
RSL was originally founded as the Remote Sensing Laboratory in 1964. Professor R. K. Moore (Electrical Engineering) served as its director and Professors D. S. Simonett (Geography), L. F. Dellwig (Geology), and R. D. Ellermeier (EE) as associate directors. At that time, Prof. Moore led the NASA Radar Team that devised a space-based radar that was a predecessor to the radar later flown as SIR-C (Shuttle-Imaging Radar-C). RSL has been involved with numerous remote sensing radar systems flown in space, radar remote sensing for myriad scientific missions, and with the development of radar technology for defense applications.
RSL has been involved with the following space-borne radar programs: Skylab, Seasat, SIR-A, SIR-B, SIR-C, ERS-1, JERS-1, TRMM, and SeaWinds. RSL has participated in many large national and international programs for study of vegetation, oceans, sea ice, and glacial ice.
In 1998, RSL merged with the Telecommunication & Information Sciences Lab (TISL) to form the Information and Telecommunication Technology Center (ITTC) at KU. In 2005, RSL research formed the basis of the Center for the Remote Sensing of Ice Sheets (CReSIS), an NSF Science and Technology Center established to study the ice sheets in Greenland and Antarctica.
Power spectrum is the considerable aspect in the atmospheric radar data processing to estimate wind parameters. Due to the poor resolution and high sidelobe level problems of the existing algorithms, there is a requisite for the novel data-dependent approaches. A non-parametric and hyperparameter-free iterative adaptive approach (IAA) is presented for the power spectral density estimation. This approach is able to work with single snapshot and is obtained by minimizing the weighted least square fitting criterion. The IAA method provides the accurate amplitude and frequency estimation for the simulated data. The data for the above study is collected from Indian MST (mesosphere, stratosphere, and troposphere) radar. The power spectrum and Doppler frequency are estimated using IAA. In this paper, zonal (U), meridional (V), windspeed (W) are also calculated and validated using Global Positioning System Sonde data. The effectiveness of the spectral estimation performance showed by IAA is demonstrated and assessed.
Indian MST radar provides information on wind data above 3.5 km with a resolution of 150 m. The three wind components U, V and W are determined by the Doppler beam swinging (DBS) method of the MST radar. The radar collects the data using multiple beam positions with 16 μs coded pulse and with an inter pulse period (IPP) of 100 μs. The online calculation of Doppler power spectra for each range bin can be obtained by subjecting the complex time series data to the process of fast Fourier transform (FFT). The DC removal, average noise level estimation, interference removal and incoherent integration are the steps that are involved in offline data processing. The 0th, 1st and 2nd moments denotes the signal strength, mean Doppler shift and half width parameters of the spectrum are computed, respectively.
The accurate estimation of the Doppler frequency is the crucial one in the detection and estimation of the wind speed by the MST radar. A package for processing the radar data has been developed by the National Atmospheric Research Laboratory (NARL), Gadanki, Andhra Pradesh, India. It is known as the atmospheric data processor (ADP) (Anandan 2002). The Doppler frequencies can be accurately estimated by the ADP up to certain heights. Since the signals are highly corrupted with noise at higher altitudes, the ADP is unable to estimate the Doppler frequencies and thus the wind speed. It can be seen in the literature that several algorithms have been put to use to accurately estimate the Doppler frequencies from MST radar data.
Let \(\left\ y_n \right\_n = 1^N\) be the complex data obtained by weighted combination of C complex exponentials with frequencies \(\left\ \varOmega_r \right\_r = 1^N \in \left[ 0,\varOmega_max \right]\)
Iterative adaptive approach is a weighted least square-based data-dependent, non parametric algorithm. It can be used for the single data sequence or the multiple data snapshots spectral estimation. Here, we assume the single snapshot case.
where \(\left\ m_i \right\_i = 1^R + N\) is a replacement for \(\left\ ^ 2 \right\_i = 1^R + N\). The first R diagonal elements of the matrix \(\varvecP\) denote the power values that are to be estimated and the remaining N diagonal elements denote the noise variance values.
where \(\left\ \varvecX \right\_\varvecO_r^ - 1 ^2 \triangleq \varvecX^H \varvecO_r^ - 1 \varvecX.\) \(\varvecO_\varvecr\) is the interference covariance matrix written as
The root mean square error (RMSE) of the frequency estimation and amplitude estimation versus SNR plots are shown in Fig. 3a, b, respectively. All the performance curves are obtained via 100 Monte Carlo simulations. We change the value of σ2 to obtain different signal-to-noise ratio conditions. From Fig. 3a, b, it can be observed that both the RMSE of the frequency and the amplitude estimates decreased with the SNR as expected. It can also be observed that the IAA method has better variance characteristics than the Periodogram method. It is evident from the above simulations, that even the signal is completely buried in noise the IAA method is able to retrieve the parameters well.
The radar data collected from the Indian MST radar being operated at the NARL, Gadanki, Andhra Pradesh is taken for the present study. The MST radar data is one of the e formats of 15 scans with each scan having signal information from six beam directions (East, West, Zenith-X, Zenith-Y, North and South). Each beam consists of 147 height range bins with a resolution of 150 m, starting from 3.6 km and reaching to a height of 25.6 km. Each range bin contains complex time-series data with 512 samples. The spectrum of the radar signal is calculated using IAA. Since, the echoes are usually corrupted by interference, clutter, etc., it has to be cleaned before analysis. Maximum peak detection method (Anandan et al. 1996) is used for the estimation of Doppler profile after performing spectrum cleaning of the radar signal. The Doppler frequencies are calculated from the Doppler profiles.
The power spectrum of the collected data is determined using IAA method. The basic method of periodogram is employed for estimating the power spectrum when complex time-series data is subjected to the ADP.
Figure 4a, b shows the improvement of output SNR estimated from power spectrum using periodogram and IAA for the east and south beams, respectively, for the MST radar data collected on February 9, 2015. The output SNR is obtained using the method (Hildebrand and Sekhon 1974). The comparison of average SNR values in dB for six beams on February 9 and 10, 2015 for the periodogram and IAA algorithms is given in Table 2. From Table 2, it is seen that the IAA gives the better improvement in SNR values for all the six beams.
The Doppler height profiles for four scans of the east beam attained using ADP and IAA are shown in Fig. 5a, b, respectively, for the radar data collected on February 9, 2015. The compared mean Doppler profiles and standard deviations are shown in Fig. 5c, d respectively. The observed significant difference between ADP and IAA is that the standard deviation for IAA lied very close to zero.
The Zonal, Meridional and Wind Speed components calculated using the ADP, IAA and GPS radiosonde are depicted in Figs. 6 and 7 for the data collected on June 9, 2006 and February 9, 2015, respectively. It is revealed that the IAA is following the GPS. The minor deviation from 15 to 17.5 km can be attributed to the fact that the data used in IAA are collected from the reflected echoes from the layers of the atmosphere in the vertical direction without any drift in the horizontal direction, whereas in the data collected through GPS, there can be horizontal drift of the balloon due to high wind speeds. The wind speeds using ADP, IAA and GPS radiosonde for real-time radar data collected on two different dates namely July 2, 2014 and February 9, 2015 is represented in Fig. 8.
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