Four-channel monopulse (FCM) technique is integral to contemporary radar systems for pinpointing and tracking multiple targets. However, the practical utility of FCM is significantly hindered by the issue of estimation failure, which arises from the specific relative phases and positions of the targets. In this paper, we introduce a polarimetric four-channel monopulse (PFCM) approach designed to deliver efficient, robust, and unambiguous resolution of two targets. Generally, PFCM adaptively modulates the null-polarization for each target. We begin by dissecting the limitations of the traditional method, utilizing the closed-form solution of FCM. Subsequently, we devise a novel closed-form solution that harnesses polarimetric adaptive nulling (PAN) technology to estimate the angles of the two targets under two distinct scenarios: the ideal condition and angular ambiguity. Under ideal conditions, estimation is conducted directly using the received signals. Incorporating polarization information mitigates the estimation results’ sensitivity to relative phase. To counteract the challenges of PAN failure in angular ambiguity situations, the proposed PFCM adeptly resolves the two targets by reconstructing the four-channel signal, indicating its applicability across various relative target positions. Furthermore, we present a comprehensive performance analysis focusing on three critical aspects. As a result, two closely spaced targets can be resolved robustly and unambiguously with minimal complexity. Numerical simulations and experimental results are substantiated to validate the effectiveness of the proposed method.

The task of detecting small targets poses a significant challenge for ocean surveillance radars. Since the sea clutter is time-varying and the targets with small radar cross-sections (RCS) have weak returns, traditional adaptive detectors that rely on the statistical model of clutter fail to work. An effective approach is feature-based detector, which primarily takes advantage of the feature difference between target and clutter. The discriminability of single-dimensional features such as amplitude and Doppler spectra is limited while the potential of polarization information remains undeveloped. Stemming from the polarization dependence of the clutter Doppler spectrum, we propose an innovative polarization-Doppler joint feature-based detection method. Based on the polarization modulation that digitally synthesizes multiple transmitting and receiving polarization states, we construct a two-dimensional polarization-Doppler spectrum and extract the joint polarization-Doppler features, which can reveal the significant differences between clutter and targets. Subsequently, a detector based on the false alarm rate controllable SVM classifier is proposed. The experimental results, derived from IPIX data sets, demonstrate that the proposed method exhibits enhanced robustness compared to traditional feature-based approaches. Furthermore, it has superior performance and less computational cost compared to the time-frequency feature-based method.

Man-made targets with various orientations usually exhibit scattering diversity which makes polarimetric radar target recognition difficult. The recently developed polarimetric rotation domain techniques provide solutions to understand and utilize target scattering diversity. Meanwhile, the Null-Pol theory is the important component of target optimal polarization in radar polarimetry. This work first extends the Null-Pol theory into the polarimetric rotation domain and discloses the different scattering responses from canonical man-made structures. In this vein, the Null-Pol response pattern interpretation tool is established and a set of Null-Pol features are extracted and characterized. Then, a man-made structure identification approach is proposed based on selected features derived from the Null-Pol response pattern. Comparison studies are carried out with simulated datasets of canonical structures considering polarimetric measurement errors, simulated, and measured data of unmanned aerial vehicles (UAVs). Compared with the Cameron decomposition, the proposed method achieves more accurate and stable performance.

Ship contour extraction is vital for extracting the geometric features of ships, providing comprehensive information essential for ship recognition. The main factors affecting the contour extraction performance are speckle noise and amplitude inhomogeneity, which can lead to over-segmentation and missed detection of ship edges. Polarimetric synthetic aperture radar (PolSAR) images contain rich target scattering information. Under different transmitting and receiving polarization, the amplitude and phase of pixels can be different, which provides the potential to meet the uniform requirement. This paper proposes a novel ship contour extraction framework from PolSAR images based on polarization modulation. Firstly, the image is partitioned into the foreground and background using a super-pixel unsupervised clustering approach. Subsequently, an optimization criterion for target amplitude modulation to achieve uniformity is designed. Finally, the ship’s contour is extracted from the optimized image using an edge-detection operator and an adaptive edge extraction algorithm. Based on the contour, the geometric features of ships are extracted. Moreover, a PolSAR ship contour extraction dataset is established using Gaofen-3 PolSAR images, combined with expert knowledge and automatic identification system (AIS) data. With this dataset, we compare the accuracy of contour extraction and geometric features with state-of-the-art methods. The average errors of extracted length and width are reduced to 20.09 m and 8.96 m. The results demonstrate that the proposed method performs well in both accuracy and precision.

Conventional polarimetric measurement systems typically require multi-channel polarization information, leading to high hardware cost and complexity. To address this limitation, this letter proposes a low-cost method for polarization scattering matrix (PSM) measurement using single-channel polarization information. By employing a polarization modulator in a single-channel front-end to cycle the polarization along a Poincaré sphere orbit, a unique triple-line spectrum is generated. The elements in PSM are then estimated by decoupling these spectral components. The underlying principles, polarization-agile orbit constraints, and performance limits of the method are thoroughly analyzed. Simulations demonstrate high-precision PSM estimation for various types of targets, exhibiting strong robustness against signal-to-noise ratio (SNR) and polarization modulation errors. In particular, it maintains a polarization similarity coefficient (PSC) above 0.985 even at an SNR as low as $-$ 20dB.

Oriented manmade targets can produce significant cross-polarization power. The scattering mechanism interpretation of them is still challenging. Within the framework of traditional scattering models, the scattering mechanism of oriented manmade targets will be interpreted as volume scattering. Recently, many advanced approaches have been proposed to mitigate the cross-polarization terms of the coherency matrix or distribute the power of cross-polarization to new scattering models, such as orientation angle compensation and multiple scattering components decomposition. Among these methods, the general model-based decomposition with physically meaningful double-bounce and odd-bounce scattering models has been proposed by modeling their independent orientation angles and becomes a widely accepted method. However, the two vital parameters of generalized scattering models: double- and odd-bounce orientation angles are derived through nonlinear optimization procedure. These generalized models lead to a heavy computation burden for parameters inversion. In this paper, we disclose the latent relationship between the double-bounce orientation angle and polarization orientation angle by data fitting experiments. With this simplified relationship, a refined double-bounce scattering model is established. Then, the odd-bounce orientation angle can be derived through equations. In this way, the nonlinear optimization procedure can be converted to a linear solution. A fast generalized model-based decomposition is developed thereafter. The main contribution of this work is to inherit the generalized models while speeding up the parameter calculation procedure. The comparison studies are carried out with X-band airborne PiSAR, L-band spaceborne ALOS-2, and C-band spaceborne Radarsat-2 PolSAR datasets. Compared with the state-of-the-art approaches, the proposed decomposition achieves improved interpretation performance from both visual and quantitative investigations especially for oriented built-up areas.