Range-azimuth Cell Research Articles

Monopulse-Radar Angle Estimation of Multiple Targets Using Multiple Observations

Amplitude comparison monopulse systems encounter the target resolution problem when multiple targets occupy the same range-azimuth cell. To solve this problem, a new angle estimation approach is proposed. The traditional range-cell-based signal processing is improved by modeling the signal in the frequency domain. Thus, the range straddling loss and the range migration problem can be avoided. The closely spaced targets in the same range-azimuth cell can be well resolved by the jointly maximum likelihood estimation of the range and velocity of the targets. Then, the angle estimates are extracted by using the monopulse ratio technique with the estimated parameters of the resolved targets in the two channels. Furthermore, the RELAX algorithm is utilized to reduce the computational burden of the proposed approach. The performance of the proposed estimator is evaluated using Monte Carlo simulation.

Holographic SAR Tomography 3-D Reconstruction Based on Iterative Adaptive Approach and Generalized Likelihood Ratio Test

Holographic synthetic aperture radar (HoloSAR) tomography is an attractive imaging mode that can retrieve the 3-D scattering information of the observed scene over 360° azimuth angle variation. To improve the resolution and reduce the sidelobes in elevation, the HoloSAR imaging mode requires many passes in elevation, thus decreasing its feasibility. In this article, an imaging method based on iterative adaptive approach (IAA) and generalized likelihood ratio test (GLRT) is proposed for the HoloSAR with limited elevation passes to achieve super-resolution reconstruction in elevation. For the elevation reconstruction in each range-azimuth cell, the proposed method first adopts the nonparametric IAA to retrieve the elevation profile with improved resolution and suppressed sidelobes. Then, to obtain sparse elevation estimates, the GLRT is used as a model order selection tool to automatically recognize the most likely number of scatterers and obtain the reflectivities of the detected scatterers inside one range-azimuth cell. The proposed method is a super-resolving method. It does not require averaging in range and azimuth, thus it can maintain the range-azimuth resolution. In addition, the proposed method is a user parameter-free method, so it does not need the fine-tuning of any hyperparameters. The super-resolution power and the estimation accuracy of the proposed method are evaluated using the simulated data, and the validity and feasibility of the proposed method are verified by the HoloSAR real data processing results.

The WSR-88D Inanimate Hydrometeor Class

AbstractThe dual-polarization upgrade to the WSR-88D network of weather radars included the addition of a hydrometeor classification algorithm (HCA) to the Open Radar Product Generator (ORPG). The HCA product categorizes each Level-III radar range–azimuth cell into one of 10 classifications or marks it as “unknown.” However, not all target types fall under the 10 classification options. For this reason, multiple studies have examined adding new classes to the operational HCA. In this study, a new “inanimate” class is developed for additional hydrometeor classification in the ORPG and is described in detail. This class encompasses nonweather phenomena such as chaff (the primary motivation of this work), sea clutter, and some types of combustion debris and radio frequency interference. The design process is detailed, including human truthing, data selection, and optimization with a genetic algorithm. Multiple case examples are presented and analyzed, both qualitatively and quantitatively. Quantification is put into perspective with previous studies for a better understanding of the algorithm’s performance and impact. A discussion of applications, including subclassing, chaff detection, and implementation as an “aviation” classification algorithm in the ORPG, is presented.

δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells

The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment.

Urban Area Tomography Using a Sparse Representation Based Two-Dimensional Spectral Analysis Technique

Synthetic aperture radar (SAR) tomography (TomoSAR) estimates scene reflectivity along elevation coordinates, based on multi-baseline measurements. Common TomoSAR approaches are based on every single range-azimuth cell or the cell’s neighborhood. By using an additional synthetic aperture for elevation, these techniques have higher resolution power for elevation, to discriminate scatterers with differences in location in the same range-azimuth cell. However, they cannot provide sufficient range resolution power to discriminate these scatterers by joining different spectra to a wider range spectrum, which limits the resolution power of TomoSAR. Therefore, in this paper, we proposed using a compressive sensing (CS) technique to reconstruct range and elevation signals from multi-baseline SAR images. This TomoSAR method not only retrieves the vertical signals but also improves the resolution in the range, which helps improve the resolution power of the TomoSAR technique. We present the theory of the CS-based two-dimensional TomoSAR technique and compare it to the CS-based one-dimensional TomoSAR technique. The excellent resolution power and scatterer localization accuracy of this novel technique are demonstrated using simulations and real data obtained by RADARSAT-2 in Lanzhou, China.