Forward-looking Radar Research Articles

Airborne Multi-Channel Forward-Looking Radar Super-Resolution Imaging Using Improved Fast Iterative Interpolated Beamforming Algorithm

Radar forward-looking imaging is critical in many civil and military fields, such as aircraft landing, autonomous driving, and geological exploration. Although the super-resolution forward-looking imaging algorithm based on spectral estimation has the potential to discriminate multiple targets within the same beam, the estimation of the angle and magnitude of the targets are not accurate due to the influence of sidelobe leakage. This paper proposes a multi-channel super-resolution forward-looking imaging algorithm based on the improved Fast Iterative Interpolated Beamforming (FIIB) algorithm to solve the problem. First, the number of targets and the coarse estimates of angle and magnitude are obtained from the iterative adaptive approach (IAA). Then, the accurate estimates of angle and magnitude are achieved by the strategy of iterative interpolation and leakage subtraction in FIIB. Finally, a high-resolution forward-looking image is obtained through non-coherent accumulation. The simulation results of point targets and scenes show that the proposed algorithm can distinguish multiple targets in the same beam, effectively improve the azimuthal resolution of forward-looking imaging, and attain the accurate reconstruction of point targets and the contour reconstruction of extended targets.

A Learned Ambiguity-Depression Method for Forward-Looking Radar With Perturbed Antenna Array

A Learned Ambiguity-Depression Method for Forward-Looking Radar With Perturbed Antenna Array

A novel vertical element-pulse coding scheme for range-ambiguous clutter elimination

The coexistence of clutter range ambiguity and range dependence in the airborne forward-looking radar may render the common space–time adaptive processing (STAP) ineffective. To cope with this challenge, a novel vertical element-pulse coding (EPC) radar is proposed in our paper. Note that both the elevation angles and coding factors determine the transmitted spatial frequency of the proposed vertical EPC radar. Therefore, the clutters corresponding to different ambiguous areas can be distinguished in the spatial frequency dimension based on this property. The element-pulse decoding and pre-STAP filtering are devised to separate and extract these clutters corresponding to different ambiguous areas. In the following, the clutter elimination is independently accomplished for each ambiguous area that has been separated. Consequently, the clutter range ambiguity as well as clutter range dependence are addressed simultaneously. The efficacy of the proposed approach is verified with simulation experiments.

Data-driven airborne bayesian forward-looking superresolution imaging based on generalized Gaussian distribution

Airborne forward-looking radar (AFLR) has been more and more impoatant due to its wide application in the military and civilian fields, such as automatic driving, sea surveillance, airport surveillance and guidance. Recently, sparse deconvolution technique has been paid much attention in AFLR. However, the azimuth resolution performance gradually decreases with the complexity of the imaging scene. In this paper, a data-driven airborne Bayesian forward-looking superresolution imaging algorithm based on generalized gaussian distribution (GGD- Bayesian) for complex imaging scene is proposed. The generalized gaussian distribution is utilized to describe the sparsity information of the imaging scene, which is quite essential to adaptively fit different imaging scenes. Moreover, the mathematical model for forward-looking imaging was established under the maximum a posteriori (MAP) criterion based on the Bayesian framework. To solve the above optimization problem, quasi-Newton algorithm is derived and used. The main contribution of the paper is the automatic selection for the sparsity parameter in the process of forward-looking imaging. The performance assessment with simulated data has demonstrated the effectiveness of our proposed GGD- Bayesian algorithm under complex scenarios.

Automated target detection methodology for drill head sensor systems

A stepped-frequency continuous-wave radar system has been designed and integrated into a drill head for use on midrange horizontal directional drills. Data from the forward-looking radar are acquired, processed, and displayed in near real time. A unique, computationally efficient target detection algorithm has been developed and tested to predict the position of potential obstacles in front of the drill head. This method is called derivative imaging, and it provides distance to target and radial offset information such that the drill operator may respond accordingly. Application to traditional ground-penetrating radar systems is also demonstrated.

Development of Simulation Models Supporting Next-Generation Airborne Weather Radar for High Ice Water Content Monitoring

In this article, a method of extending airborne weather radar modeling to incorporate high-ice-water-content (HIWC) conditions has been developed. A novel aspect is incorporating flight test measurement data, including forward-looking radar measurements and in situ microphysics probes data, into the model and part of the evaluations of modeling. The simulation models assume a dual-polarized, airborne forward-looking radar, while for single-polarized operations, the index-of-dispersion is included as a helpful indicator for HIWC detection. The radar system simulation models are useful for design evaluations for the next generation of airborne aviation hazard monitoring and incorporate HIWC hazard detection algorithms. Example applications of the simulator, such as hazard detection based on the simulated HIWC flight encounter scenarios based on specific numeric weather prediction (NWP) model outputs, are discussed.

Autofocusing and imaging algorithm for moving target by vortex electromagnetic wave radar

Electromagnetic (EM) vortex imaging, which combines with spiral wavefront phase modulation and computational imaging concept introduced by orbital angular momentum (OAM), has demonstrated superior performance in target information extraction and two-dimensional (2-D) or even three-dimensional (3-D) imaging capability in forward-looking observation geometry. However, due to the unknowns and complexity of target motion, the translational component will lead to the disturbance of echo phase, which hinders the motion compensation between target echo pulses and causes imaging defocus. In this paper, a high-resolution imaging algorithm based on Fourier ptychography (FP) is proposed for moving target autofocusing imaging by vortex electromagnetic forward-looking radar (VEMFLR). Firstly, based on the spatial geometric model and echo model of VEMFLR, high-order phase terms caused by target motion are derived, which provides a theoretical basis for applying FP phase retrieval to autofocusing imaging; Then, the spectrum information in FP sub-aperture is obtained; Thirdly, the imaging method, based on FP phase retrieval, is exploited to compensate phase error and obtain a well-focused image. Simulation results show that this method can achieve fine imaging performance without estimating target motion parameters.

A Novel Approach to Unambiguous Doppler Beam Sharpening for Forward-Looking MIMO Radar

The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data.

Range-Ambiguous Clutter Suppression via FDA MIMO Planar Array Radar with Compressed Sensing

Range-ambiguous clutter is an inevitable issue for airborne forward-looking array radars, especially with the high pulse repetition frequency (PRF). In this paper, a method to suppress the range-ambiguous clutter is proposed in an FDA-MIMO radar with a forward-looking planar array. Compressed sensing FDA technology is used to suppress the range-ambiguous clutter and the forward-looking non-uniformity short-range clutter of radar. Specifically, first, the range ambiguous clutter in different regions is separated by the characteristics of the planar array radar elevation dimension and FDA radar range coupling. Meanwhile, regarding the issue of the FDA radar main lobe moving between coherent pulses, a main lobe correction (MLC) algorithm proposes a solution for the issue, where the FDA radar cannot coherently accumulate signals in the case of non-full angle illumination. Finally, compressed sensing technology and elevation dimension filtering are utilized to suppress the range ambiguous clutter at the receiver, with the approach alleviating the range dependence of clutter in the observation region. A small number of clutter snapshots can obtain an approximately ideal clutter covariance matrix through compressed sensing sparse recovery. The method not only reduces the number of training samples, but also overcomes the problem of clutter non-uniformity in the forward-looking array. Therefore, the clutter suppression problems faced by the high repetition frequency airborne radar forward-looking array structure are solved. At the analysis stage, a comparison among the conventional MIMO and FDA methods is carried on by analyzing the improvement factor (IF) curves. Numerical results verify the effectiveness of the proposed method in range-ambiguous clutter suppression.

Importance of Human Resource Management in Risk Management: A study with IT Industry

Risk is part of every human endeavor. Risk Management area has grown many folds in past decade in direct proportion to the number of projects undertaken. The number of projects in all the sectors have increased to fulfil the quest towards a better and more and more productive, convenient and evolved life - and is termed as ‘development’ and ‘growth’. With such increase in growth rate, the number of capital and resource intensive projects have also increased across all the sectors. Today for organizations, the competition, customer expectations, and developments in automation, IT technologies and concurrent engineering methods - all are pushing the project durations to fraction of durations they used to take a decade ago. Also with improvement in skills & competencies and communication and conveyance infrastructure getting hi-tech, today more and more projects are being managed from multiple locations within country or multiple locations spread across the globe. A business with an effective risk process will experience fewer surprises as it deploys the forward-looking radar of risk management. This research work is focused on the Risk Identification step of the risk management process. Risk management is high on the agenda for forward-thinking firms due to the increased pace of change, client demands, and market globalization. In today's economy, a thorough risk management strategy is required to stay afloat.

Robust Copula-Based Detection of Shallow-Buried Landmines With Forward-Looking Radar

We propose a technique for landmine detection using forward-looking ground-penetrating radar. The detector is applied to radar images obtained from multiple viewpoints of the region of interest and is based on a robust version of the likelihood ratio test (LRT). We incorporate the statistical dependence between multiview images into the test via copula-based model. The test is designed to maximize the worst-case performance over all feasible pairs of target and clutter distributions, thereby eliminating the need for strong assumptions about the image statistics. We evaluate the detection performance of the proposed technique for different copula functions representing the dependence structure. Using electromagnetic modeled data of shallow-buried targets under varying ground surface roughness profiles, we demonstrate the superiority of the robust copula-based detector over existing parametric and robust LRT approaches designed under the assumption of statistical independence of multiview images.

Bayesian Forward-Looking Superresolution Imaging Using Doppler Deconvolution in Expanded Beam Space for High-Speed Platform

Deconvolution technique can be utilized in the forward-looking radar (FLR). However, the forward-looking imaging performance degenerates greatly due to the effect of high-speed movement of the platform. In this article, an efficient Bayesian forward-looking superresolution imaging algorithm based on Doppler deconvolution in expanded beam space is proposed. First, the Doppler phase information caused by the high-speed platform is fully exploited and the Doppler matrix is integrated with the antenna pattern. The Doppler convolution model of the echo signal for forward-looking is derived in this article. Then, the Doppler phase information is adopted to perform the Doppler deconvolution. Moreover, an expanded beam space is constructed to enhance the sparsity of the imaging scene. The complex Gaussian distribution and the Laplace distribution have been used to model the distribution characteristics of noise and targets in the imaging scene, respectively. Finally, based on the Bayesian framework, the forward-looking imaging problem is converted into the convex optimization problem. The performance assessment based on simulated and experimental data, also in comparison to the conventional real beam, truncated singular value decomposition (TSVD), iterative adaptive approach (IAA) methods, has demonstrated the effectiveness of our proposed algorithm under high-speed platform scenarios.

항공용 전방 관측 레이다에서의 RBGM 영상 형성

In this study, we introduce the real beam ground map (RBGM) mode of airborne forward-looking radars and present the images obtained by the radar under development. In addition to an introduction to the overall contents of the RBGM mode, we present the main features considered in the algorithm design and verification steps (M&S analysis, ground test, and flight test) and the RBGM images obtained in each step. We suggest the features of the RBGM mode through the analysis of RBGM images and demonstrate that the RBGM mode is suitable for the purpose of providing rough terrain information for a wide area ahead.

Robust space‐time adaptive processing based on covariance matrix reconstruction and steering vector correction

Clutter presents considerable heterogeneity in forward-looking airborne radar (FLAR) applications and conventional space-time adaptive processing (STAP) methods are sensitive to model mismatch. As a result, when a strong target signal contaminates the training samples, despite the use of guard cells, the performance of conventional STAP methods degrades significantly. In this study, a robust method, which involves reconstructing a target-free covariance matrix and correcting the presumed steering vector to prevent target cancellation in FLAR, is proposed. First, the target-free covariance matrix is reconstructed through integrating the spatial–temporal spectrum over a sector separated from the desired frequency and direction of targets. Subsequently, the mismatch between presumed steering vector and actual steering vector is corrected via quadratic optimisation. In addition, the processing scheme is applied to real-measured clutter data, and the experimental results validate the effectiveness of the proposed method.

Azimuth Superresolution of Forward-Looking Radar Imaging Which Relies on Linearized Bregman

Forward-looking radar plays an important role in many military and civilian fields. However, the problem of low azimuth resolution has restricted its applications seriously. Although many methods have been used to achieve azimuth superresolution, the traditional methods suffer from noise amplification or limited resolution under low signal-to-noise (SNR) condition. In this paper, we proposed a Bayesian deconvolution method which relies on linearized Bregman to achieve azimuth superresolution of forward-looking radar imaging. We first used the complex Gaussian distribution and Laplace distribution to describe the distribution of noise and targets, respectively, and transformed the superresolution problem into a convex optimization problem by maximum a posteriori estimation in the Bayesian framework. Second, linearized Bregman algorithm was used to solve the convex optimization problem. The proposed method introduces the prior information of noise and target, and overcomes the ill-posedness of deconvolution. As a result, the azimuth resolution is remarkably enhanced. Besides, the proposed method has high computational efficiency by linearizing objection function, so it can take both time cost and resolution improvement into consideration. Finally, the superior performance was verified by simulation and experimental data.

Airborne Forward-Looking Radar Super-Resolution Imaging Using Iterative Adaptive Approach

Airborne forward-looking radar (AFLR) imaging has raised many concerns in fields of Earth observation, independent of weather and daytime. Constrained by imaging principles, conventional high-resolution radar imaging techniques such as synthetic aperture radar (SAR) and Doppler beam sharpening (DBS) are incapable of AFLR imaging. The real aperture radar (RAR) can obtain AFLR images using a scanning antenna, but suffers from coarse cross-range resolution. Recently, there has been much attention paid to the iterative adaptive approach (IAA), which draws from the benefits of RAR imaging and provides improved cross-range resolution. However, earlier work on the IAA imposed a convolution model on the received azimuth echo, neglecting the effect of the Doppler phase. This model mismatch degrades the imaging performance for moving platforms. To settle this problem, this paper first establishes a Doppler-convolution model of AFLR imaging, where both Doppler phase and antenna convolution effects are considered, allowing more accurate reconstruction when applying the IAA to formulate a super-resolution image. Then, a data-depended approach for Doppler centroid estimation is proposed to circumvent the problem of low estimation precision using platform motion parameters delivered by navigational devices mounted on the radar platform. Simulation results demonstrate that the proposed implementation of the IAA based on the Doppler-convolution model and Doppler centroid estimation can overcome the deficiencies of the SAR and DBS techniques in the forward-looking imaging direction, and present a noticeably superior performance as compared with conventional AFLR imaging methods.

First experimental results on the feasibility of noise radar systems on fast moving platforms

Forward-looking radars are essential sensors for many fast moving platforms such as fighter aircraft and missiles. Despite its multiple benefits, noise radar has not yet been used for this task because of the low Doppler tolerance of its waveform and the associated need for computational intense Doppler compensation. This Letter presents the very first experimental results of a continuous-emission noise radar whose antennas are oriented along the direction of proper motion of a car operating as a radar platform at a significant speed. To the best of the authors' knowledge, no experiments of this kind have been published to date. Very likely, the results presented here are the first ones showing moving target detection by a noise radar on a fast moving platform.

Vector extrapolation accelerated iterative shrinkage/thresholding regularization method for forward-looking scanning radar super-resolution imaging

The iterative shrinkage/thresholding regularization method is an efficient and robust approach to implement super-resolution imaging for forward-looking scanning radar (FLSR). Nevertheless, numerous iterations are normally required to obtain a high super-resolution ratio and thus it can be time consuming. A vector extrapolation accelerated iterative shrinkage/thresholding regularization method is proposed for FLSR. In order to achieve effective acceleration, a predicted point is particularly formulated by linearly extrapolating the historical iterates before conducting the iterative shrinkage/thresholding operation. To reduce the prediction error and stabilize the acceleration process, an adaptively adjusted acceleration step size is derived according to the similarity of the adjacent iterative vectors. Last, a fixed-threshold constraining operator is conducted on the step size to guarantee the convergence of the acceleration method. The results of simulation experiment and real data processing verify the advantage of the proposed method in both the convergence speed and super-resolution performance when compared with the traditional acceleration methods.

A `DIVIDE AND CONQUER' REGULARIZATION IMAGING METHOD FOR FORWARD-LOOKING SCANNING RADAR AZIMUTH SUPER-RESOLUTION

A `DIVIDE AND CONQUER' REGULARIZATION IMAGING METHOD FOR FORWARD-LOOKING SCANNING RADAR AZIMUTH SUPER-RESOLUTION

Efficient forward-looking imaging via synthetic bandwidth azimuth modulation imaging radar for high-speed platform

This paper deals with the forward-looking imaging problem under high-speed platform for the airborne single-channel forward-looking radar (ASFLR). Biphase-based synthetic bandwidth azimuth modulation imaging radar (Bi-SBAMIR) can be used to increase the azimuth resolution. However, the performance degrades significantly when the velocity of the platform is high. To solve this problem, a more generalized SBAMIR framework based on linear frequency modulation (LFM) waveform (LFM-SBAMIR) is proposed. The LFM waveform is more Doppler tolerant than the biphase waveform, which makes it a good choice for high speed platform. In LFM-SBAMIR, the synthetic bandwidth of the received signal has two parts: the modulation bandwidth and the Doppler bandwidth. Therefore, large synthetic bandwidth can be achieved by increasing the modulation bandwidth even in the case of small Doppler bandwidth, and high azimuth resolution can be achieved after matched filtering. Moreover, it is firstly proved that the azimuth resolution is proportional to the time bandwidth product (TBP) of the modulated LFM waveform. Finally, the azimuth resolution performance under different TBPs, SNRs and velocities are analyzed. The performance assessment conducted by resorting to simulated data, also in comparison to the previously proposed Bi-SBAMIR method, has confirmed the effectiveness of the newly proposed algorithm in high-speed platform scenarios.