Forward-looking Radar Research Articles

Clutter suppression for hypersonic vehicle-borne radar with frequency diverse array

The seriously range-ambiguous clutter is one of the main problems in clutter suppression for hypersonic vehicle-borne forward-looking radar. An approach based on the frequency diverse array (FDA) technique is proposed to mitigate the range-ambiguous clutter. The frequency increment is designed to distinguish the clutter at ambiguous ranges and suppress the clutter by using a subspace projection algorithm. On the platform with high altitude or limited array antennas, the proposed method performs better for its independence of the elevation degrees-of-freedom (DOF). Finally, simulation results verify the effectiveness of the proposed method.

A Study on Sidelobe Analysis and Reduction Technique in Azimuth for Forward-Looking Imaging Radar

A Study on Sidelobe Analysis and Reduction Technique in Azimuth for Forward-Looking Imaging Radar

Synthetic bandwidth azimuth modulation imaging radar for airborne single-channel forward-looking imaging

An efficient airborne single-channel forward-looking radar (ASFLR) imaging framework to improve the azimuth resolution performance with synthetic bandwidth azimuth modulation imaging radar (SBAMIR) is proposed. During the framework, we incorporate azimuth modulation to the transmitted signal with a coded sequence in azimuth direction, and derive the SBAMIR imaging model for the first time. In SBAMIR, the synthetic bandwidth of the received echo is not only determined by the Doppler bandwidth, but also by the modulation bandwidth. Even though the Doppler bandwidth has little contribution to the synthetic bandwidth in ASFLR, the modulation bandwidth can be utilized to increase the synthetic bandwidth. Accordingly, a high azimuth resolution can be achieved after azimuth matched filtering. Achievable azimuth resolution and performance to SNR are analyzed later. Simulation results are given to verify the effectiveness of the proposed algorithm.

Bayesian Forward-looking Imaging for Airborne Single-channel Radar Based on Combined Multiple Frames Data

An adaptive Bayesian super-resolution imaging algorithm based on the combined multiple frames data is proposed to enhance the azimuth resolution of airborne single-channel forward-looking radar. The echo of the forward-looking radar in the Gaussian noise is modeled, and the processing space is expanded from the low dimension of single frame data to the high dimension of multiple frames data to enhance the sparsity of domain scatterers. During the framework, the sparsity of the scatterers is modeled in spatial domain, and the imaging is converted into a problem of signal optimization based on Bayesian formalism. The final optimal result can be solved by the conjugate gradient method. In this framework, the statistic parameter is estimated with data-driven. Simulation results demonstrate that the proposed algorithm both can increase the resolution of the forward-looking imaging results and suppress the artifacts.

Bayesian Angular Superresolution Algorithm for Real-Aperture Imaging in Forward-Looking Radar

In real aperture imaging, the limited azimuth angular resolution seriously restricts the applications of this imaging system. This report presents a maximum a posteriori (MAP) approach based on the Bayesian framework for high angular resolution of real aperture radar. First, Rayleigh statistic and the lq norm (for 0 < q ≤ 1) sparse constraint are considered to express the clutter property and target scattering coefficient distribution, respectively. Then, the MAP objective function is established according to the hypotheses above. At last, a recursive iterative strategy is developed to estimate the original target scattering coefficient distribution and clutter statistic. The comparison of simulations and experimental results are given to verify the performance of our proposed algorithm.

Cross‐range resolution improvement in forward‐looking imaging radar using autoregressive model‐based data extrapolation

The authors propose an extrapolation method to improve cross-range resolution in forward-looking imaging radar with multi-input/dual-output configuration. The extrapolation, which is based on autoregressive (AR) model, has different prediction directions according to the position of transmitting antennas. They tested the proposed method in a simulation with noise and in an actual experiment. In the simulation and experiment, the proposed method, which uses Burg method as an estimator of AR model, shows better image quality than other methods.

Augmented Lagrangian method for angular super-resolution imaging in forward-looking scanning radar

Angular super-resolution imaging in the forward-looking area of a scanning radar platform plays an important role in the application of scanning radar. However, the angular resolution of scanning radar is limited by the system parameters. Thus, improving the angular resolution of scanning radar beyond the limitation of the given system parameters is desired. We present an angular super-resolution imaging method by solving the associated deconvolution problem. We first formulate an angular super-resolution problem in scanning radar as a deconvolution task and then convert it to a constrained optimization problem by incorporating the prior information of the target in the scene. We then solve the constrained optimization problem in convex optimization framework using an augmented Lagrangian method. In order to solve the constrained optimization problem, a corresponding augmented Lagrangian function is constructed and its saddle point is found using alternating direction method. The advantages of the proposed method for angular super-resolution imaging in scanning radar are that the proposed method can not only realize the angular super-resolution imaging in scanning radar but also has high precision. Simulation and experiment results are given at the end to verify the validity of the proposed method compared with a Wiener filter that is applicable for angular super-resolution in scanning radar.

Sparse super‐resolution imaging for airborne single channel forward‐looking radar in expanded beam space via l p regularisation

An adaptive super-resolution imaging algorithm to enhance the performance of airborne single channel forward-looking radar is proposed. Multiple beams data to expand the processing space to enforce the sparsity of scatterers with respect to the antenna pattern was first utilised. Then, the imaging is converted into a problem of signal reconstruction with Lp-norm regularisation, and the regularisation parameter is data driven. Simulations are given to verify the effectiveness of the algorithm.

Image Quality Comparison According to the Interpolation Method in Forward-Looking Imaging Radar

Image Quality Comparison According to the Interpolation Method in Forward-Looking Imaging Radar

Ultra-wideband imaging radar to reveal obstacles concealed in vegetation to improve navigation of unmanned ground vehicles

To increase mobility of an unmanned ground vehicle (UGV), this study describes the use of forward-looking imaging radar applying ultra-wideband radar technology. If radar is able to visualize images with high resolution in front of a UGV, the radar can be used to support the vehicle by detecting obstacles through foliage. This system employs the multi-input/double-output configuration with two transmitting horn antennas to increase cross-range resolution. Also, two rows of physical receiving antenna arrays provide the height information of targets using an interferometer principle. For visualizing objects in front of the radar, the back-projection method is applied to the data acquired from the receivers. A field test is used to verify the effectiveness of the new radar system. The performance of the radar is proved experimentally by the analysis of image resolution, grass-penetrating capability, and target height information.

Large-Scale, Full-Wave-Based Emulation of Step-Frequency Forward-Looking Radar Imaging in Rough Terrain Environments

Large-scale, full-wave modeling of multistatic target imaging in a rough ground environment is described. The emulation methodology employs a parallelized three-dimensional “near-field” finite-difference time-domain algorithm in characterizing the electromagnetic scattering from the ground surface and buried and on-surface targets in the form of landmines and unexploded ordnances; subsequent focusing of the scattered fields into an image is obtained by exploiting the time-reversal technique applied over a spectral band consistent with one used by a step-frequency system under development. Imaging performance is investigated with numerical experiments for both single- and multi-aperture sensing geometries. The emphasis of this study is on examining the responses of discrete ground targets in the presence of distributed variable ground clutter as relevant to performance prediction for ultra-wideband forward-looking radar applications.

Performance Assessment of a Microwave Tomographic Approach for the Forward Looking Radar Configuration

This paper deals with the application and the performance analysis of a microwave tomography approach for Forward-Looking Radar (FLR) bistatic illumination. The imaging problem is faced by adopting an inverse scattering algorithm based on an approximated model of the electromagnetic scattering. In particular, the Born Approximation is used to describe the wave–material interaction and the targets are assumed to be embedded in a homogenous medium. The adoption of a simplified model of the electromagnetic scattering allows us to analyse how the reconstruction capabilities depend on the measurement configuration. An investigation of the resolution limits in the FLR case is performed and some numerical results are provided in order to show the effectiveness of the proposed approach in cases resembling the ones occurring in real situations.

Study on Space-time Character of Clutter for Forward-looking Frequency Diverse Array Radar

Study on Space-time Character of Clutter for Forward-looking Frequency Diverse Array Radar

Approach for Ground Fast-moving Target Detection and Imaging Based on the Three-channel Forward-looking Radar

摘要: 针对3通道前向阵雷达SAR-地面运动目标检测(SAR-GMTI)体制，该文提出一种易实现的地面快速动目标检测与定位新方法。该方法针对快速运动目标距离散焦严重问题及多普勒谱折叠带来的处理过程繁琐等问题，在杂波抑制后对快速动目标进行重聚焦从而实现对快速目标的检测，并在重聚焦过程中先充分压缩多普勒带宽再进行距离走动校正，有效避免了多普勒谱折叠。仿真结果验证了该文方法的有效性。 关键词: 前向阵雷达 / SAR-地面运动目标检测(GMTI) / 地面快速运动目标 / 多普勒谱折叠

Quantitative Image Recovery From Measured Blind Backscattered Data Using a Globally Convergent Inverse Method

The goal of this paper is to introduce the application of a globally convergent inverse scattering algorithm to estimate dielectric constants of targets using time-resolved backscattering data collected by a U.S. Army Research Laboratory forward-looking radar. The processing of the data was conducted blind, i.e., without any prior knowledge of the targets. The problem is solved by formulating the scattering problem as a coefficient inverse problem for a hyperbolic partial differential equation. The main new feature of this algorithm is its rigorously established global convergence property. Calculated values of dielectric constants are in a good agreement with material properties, which were revealed a posteriori.

Full-Wave Characterization of Rough Terrain Surface Scattering for Forward-Looking Radar Applications

In characterizing ground surface clutter as relevant to forward-looking radar applications, a finite-difference time-domain (FDTD)-based solver is proposed to study dielectric surface scattering at low depression angles. The solver's effectiveness and accuracy are carefully evaluated for one-dimensional surfaces by comparing Monte Carlo scattering results to those from a surface integral equation (SIE) approach for various surface parameters and incidence angles. It is demonstrated that satisfactory results can be attained at near-grazing angles for most surface parameters of interest with a relatively small simulation domain size, independent of the incidence angle. Subsequently, FDTD simulations of two-dimensional terrain surfaces are featured, along with a demonstration of the effects of ground clutter on target imaging generated by the time-reversal technique. By providing a practical full-wave simulation framework for the emulation of forward-looking radar operation and imaging, this study is intended to facilitate ongoing investigations into the detectability of discrete ground targets in the presence of distributed variable ground clutter in the near-grazing regime.

Pre-Compensation Clutter Range-Dependence STAP Algorithm for Forward-Looking Airborne Radar Utilizing Knowledge-Aided Subspace Projection

The range-dependence of clutter spectrum for forward-looking airborne radar strongly affects the accuracy of the estimation of clutter covariance matrix at the range under test, which results in poor clutter suppression performance if the conventional space-time adaptive processing (STAP) algorithms were applied, especially in the short range cells. Therefore, a new STAP algorithm with clutter spectrum compensation by utilizing knowledge-aided subspace projection is proposed to suppress clutter for forward-looking airborne radar in this paper. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then by decomposing the corresponding space-time covariance matrix to calculate the clutter subspace projection matrix which is applied to transform the secondary range samples so that the compensation of clutter spectrum for forward-looking airborne radar is accomplished. After that the conventional STAP algorithm can be applied to suppress clutter in the range under test. The proposed method is compared with the sample matrix inversion (SMI) and the Doppler Warping (DW) methods. The simulation results show that the proposed STAP method can effectively compensate the clutter spectrum and mitigate the range-dependence significantly.

A Method of Speed Compensation Based on Feature Extraction for Ve-hicle-mounted Forward-looking Virtual-aperture Radar Using Stepped Frequency

A Method of Speed Compensation Based on Feature Extraction for Ve-hicle-mounted Forward-looking Virtual-aperture Radar Using Stepped Frequency

AMSAR - A European success story in AESA radar

DGA, BWB and D&ES initiated a radar programme, called AMSAR (Airborne Multi-role Solid-state Active-array Radar) to demonstrate the enormous potential of Active Electronically Scanned Array (AESA) radar. This paper describes all the stages of the AMSAR project and the results obtained. The AMSAR demonstrator is still the only European, forward-looking multichannel active array radar with the capability of recording the outputs of more than 4 quadrants.

Modelling uneven terrain for geo-location of mines detected via vehicular mounted sensors

When searching for landmines using vehicular-mounted sensors, it is important that the ground locations of the detected mines be accurately determined. This is useful for data association when one has multiple looks at a mine by a single sensor or if one uses multiple sensors. This knowledge is necessary for neutralizing the detected mines or at least marking them for avoidance. Factors that contribute to errors in geo-location include sensor error, inaccurate knowledge of the vehicle position and/or attitude, and incomplete knowledge about the terrain being searched. This paper addresses the problem of incomplete terrain knowledge and its effects on geo-location accuracy. It presents different techniques for modelling the terrain and reducing the geo-location errors. A variety of uneven surfaces have been simulated. Random noise has been added to the basic surfaces to simulate the terrain roughness. A terrain model is then derived based on measurements one could obtain from a coarse scan of the field of view using a ranging laser. Two types of sensors are considered for the landmine detection: Synthetic Aperture Forward-Looking Radar (SAFLR) and Camera Type Sensors such as Infrared (IR). Relationships between the terrain unevenness and resulting geo-location errors are presented. The geo-location errors are worse for the Camera Type Sensor, but they can be significant for SAFLR also. The results of this analysis demonstrate that there may be significant geo-location errors for situations where the terrain is not so smooth, e.g. off-road searches, and that the problem can be alleviated via better knowledge of the terrain. If the field of view is planar or almost planar, even coarse-range scanning can improve the geo-location accuracy. For more complex surfaces, finer scanning may be required (preliminary results on this topic were presented at the SPIE (International Society for Optical Engineering) Conference, Orlando, FL, April 2001 (G. Cook, S. Jakkidi, S. Kansal, C. Amazeen and K. Sherbondy, “Impact of uneven terrain on geo-locations errors for mines detected via vehicular mounted sensors”, in Proceedings of SPIE, 2001, pp. 594–605.)). The computed geo-location errors, and the conclusions drawn as to the effectiveness of the different models are presented in the paper.