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Simultaneous polarimetric radars can measure the polarization scattering matrix (PSM) of moving targets within one pulse. The key point is the orthogonal waveform design to reduce the interference caused by the simultaneous transmission and reception. Meanwhile, to ensure the measurement accuracy of moving targets, the Doppler tolerance of the transmitted waveforms is also important. In this article, first, a matched filter (MF) at the receiver is considered and a unified framework with a newly proposed objective function to design Doppler resilient sequences under several constraints is described. The proposed objective function contains the widely used peak sidelobe level (PSL) and the integrated sidelobe level (ISL) as special cases. Meanwhile, a series of constraint conditions, including the unimodular constraint, the uncertainty modulus constraint, and the PAR constraint, is considered. Thereafter, a mismatched filter is considered for further improving the correlation properties and a similar framework for designing the optimal receiving filter at the expense of a controllable signal-to-noise ratio (SNR) loss is proposed. Numerical results are presented to verify the performance of the proposed methods. The designed sequence pair is also implemented on a fully polarimetric system to measure the PSM of a canonical target in the anechoic chamber, and the effectiveness of the proposed framework is verified by using the simultaneous polarimetric radar. It is predicted that the framework has the potential to be applied for other multichannel systems. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) has become a growing concern for weather radar, distorting radar variable estimation. By simultaneously or alternately transmitting the horizontal and vertical polarized waves, polarimetric weather radar can be referred to as SHV radar or AHV radar. The SHV radar can mimic the AHV radar by discarding either H- or V-channel measurements, which leads to an alternating scheme. In this research, the real RFI measurements from an operational C-band SHV radar are used to characterize the RFI temporal, spectral, and polarimetric features. Then, the RFI is simulated to quantify the performance of the object-orientated spectral polarimetric (OBSPol) filter in RFI mitigation. The OBSPol filter has been previously proposed by the authors to mitigate the narrowband clutter (both stationary and moving) and noise. This work extends the application of the filter to remove the RFI for SHV radar. Specifically, by taking advantage of the low copolar correlation of the RFI signal measured in AHV radar, the RFI mitigation method is designed, and its effectiveness is proven by qualitative and quantitative analyses. In particular, in the case of RFI overlapped to weather echoes in the time domain, the RFI can be mitigated, also when the duty cycle of the RFI is high. However, this work does not provide a full evaluation of the RFI mitigation performance on all radar data outputs but a proof of concept to show the effectiveness of the proposed filter for RFI mitigation. [ABSTRACT FROM AUTHOR]

High cross-polarization isolation (CPI) is crucial to the accurate polarization measurement using polarimetric phased array radar (PPAR). In this article, we propose an adaptive direction-dependent polarization state configuration (AD2PSC) method to improve the polarization isolation. Compared with the conventional fixed polarization state of radiated wave whether it is linear, circular, or elliptical polarization state, our AD2PSC approach configures the polarization state on basis of beam steering. To achieve the adaptive configuration of magnitude and phase of the dual-polarization antenna, an improved steepest descent algorithm is put forward. To facilitate the uniform representation for the polarization measurement application of PPAR, the universal expressions of intrinsic and measured backscatter matrices are derived for arbitrary polarization state. The dual-polarization dipole array is used to assess the priority of our proposed method. Compared with the conventional approaches, our approach could obtain higher CPI while being available for a larger scanning range. The configured CPI meets the specific polarization requirement for PPAR. [ABSTRACT FROM AUTHOR]

The use of spectral polarimetric filters in the range-Doppler domain shows great promise for clutter mitigation in weather radar applications. One limitation of these filters is that they cannot deal with situations in which ground clutter and precipitation overlap. In this letter, we propose a new signal recovery technique based on kriging in the spectral domain to recover the precipitation in clutter-contaminated areas. Using synthetic radar data, we test our new method and compare its performance to that of Gaussian model adaptive processing and bilinear interpolation. Our results indicate that kriging is the most accurate and robust technique out of the three. [ABSTRACT FROM AUTHOR]

Radar micro-Doppler (m-D) signature, which reflects the micromotion dynamic and structural characteristics of radar target with micromotion, has received increasing attention. Most of the existing m-D signature-extraction methods operate in the time domain or the time-frequency domain. Different from these methods, in this paper, an m-D period estimation approach that operates in the ambiguity domain is proposed. Although the ambiguity function (AF) has been widely used in the field of radar signal processing, its application for m-D signal is introduced for the first time. It is proved that the AF of m-D signal exhibits periodicity along the lag axis and has the best concentration when the lag equals to multiples of the m-D period. Based on this, three AF concentration statistics are employed to capture the periodicity and to provide the m-D estimate. The most important property of the AF concentration statistics is that they are (or approximately) invariant to polynomial translations with terms not larger than second order even if the signal is Doppler ambiguous. Numeric simulation and real radar experiments are used to validate the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

A novel algorithm is put forward to separate radar target and moving clutter based on a combination of the low-rank matrix optimization (LRMO) and the decision tree. Similar to the moving object detection in the field of automated video analysis, the proposed separation method, which is carried out in the range-Doppler domain, makes use of different motion variations of radar target and clutter in the spectrogram sequence. The technique is very general, but the focus of this paper is on narrowband moving clutter suppression in a weather radar. The first step in implementing this method is the generation of a range-Doppler spectrogram sequence. Then, the LRMO is applied to the obtained sequence to divide target and moving clutter into foreground and background. From the foreground sequence which is obtained by solving the LRMO, foreground frequency and spectral width are combined in a decision tree to obtain a filtering mask to mitigate the narrowband moving clutter and noise. Data collected by a polarimetric Doppler weather radar known as the IRCTR Drizzle Radar are used to validate the performance of the proposed algorithm. Moreover, its effectiveness in removing narrowband moving clutter is quantitatively assessed through comparisons with another clutter mitigation method. [ABSTRACT FROM AUTHOR]

In this paper, a new clutter suppression method, named the moving double spectral linear depolarization ratio (MDsLDR) filter, is put forward to mitigate narrow-band clutter in weather radars. The narrow-band clutter observed in the Doppler domain includes: 1) stationary clutter such as ground clutter and 2) nonstationary clutter such as artifacts caused by the radar system itself or external sources. These artifacts are difficult to remove, because they are not confined to specific azimuth and range bins. Based on the difference of the spectral-polarization feature and the spectral continuity of precipitation and clutter, the MDsLDR filter can remove ground clutter, artifacts, and noise. The performance of the newly proposed filter is assessed by data collected by the Doppler-polarimetric IRCTR Drizzle Radar. Three precipitation cases are considered in this paper: moderate/light precipitation, convective precipitation with hook-echo signature, and light precipitation with severe artifact contamination. Furthermore, the implementation of the MDsDLR filter requires relatively low computation complexity, so that the MDsLDR filter can be operated in real time. [ABSTRACT FROM PUBLISHER]