Radar Imaging Based on IEEE 802.11ad Waveform in V2I Communications

Since most of vehicular radar systems are already exploiting millimeter-wave (mmWave) spectra, it would become much more feasible to implement a joint radar and communication system by extending communication frequencies into the mmWave band. In this paper, an IEEE 802.11ad waveform-based radar imaging technique is proposed for vehicular settings. A roadside unit (RSU) transmits the IEEE 802.11ad waveform to a vehicle for communications while the RSU also listens to the echoes of transmitted waveform to perform inverse synthetic aperture radar (ISAR) imaging. To obtain high-resolution images of the vehicle, the RSU needs to accurately estimate round-trip delays, Doppler shifts, and velocity of vehicle. The proposed ISAR imaging first estimates the round-trip delays using a good correlation property of Golay complementary sequences in the IEEE 802.11ad preamble. The Doppler shifts are then obtained using least square estimation from the echo signals and refined to compensate phase wrapping caused by phase rotation. The velocity of vehicle is determined using an equation of motion and the estimated Doppler shifts. Simulation results verify that the proposed technique is able to form high-resolution ISAR images from point scatterer models of realistic vehicular settings with different viewpoints. The proposed ISAR imaging technique can be used for various vehicular applications, e.g., traffic condition analyses or advanced collision warning systems

ISAR Imaging of High-Speed Maneuvering Target Using Gapped Stepped-Frequency Waveform and Compressive Sensing

115Nsciescopu

SAR and ISAR Signal Processing under various Radar Configurations

DoctorThis dissertation discusses a study on radar image reconstruction under various radar configurations. Synthetic aperture radar (SAR) is an imaging radar that operates at microwave frequencies and can see through smoke, clouds, and foliage to reveal detailed images of the surface below in all weather conditions. SAR systems usually are carried on airborne or space-based platforms, including manned aircraft, drones, and military and civilian satellites. SAR imaging with high angular resolution is based on the artificial synthesis of the radar antenna aperture by a synchronous collection of reflected signals and coherent processing of the acquired data as if they originated from physically long antenna. There are many different types of radar, such as weather, surveillance, fire control, SAR and inverse SAR (ISAR). In SAR, the radar moves with the radar platform and the target is stationary. On the contrary to this, the radar is stationary while the target moves during the imaging interval in ISAR. ISAR imaging has a significant role aboard maritime patrol aircraft to provide them with radar image of sufficient quality to allow it to be used for target recognition purposes. It is understood that the basis of the difference between SAR and ISAR lies in the noncooperation of the target. Such a subtle difference has led in the last decades to a significant separation of the two areas. The noncooperation of the target introduces the main problem of the unknown geometry and dynamic of the target during the coherent integration time. It is observed that SAR images would tend to blur or defocus if there is motion of the target within the frame of reference. ISAR processing exploits the target’s rotational motion to produce a well-focused image of what was causing the blurred effects of the SAR image. Consequentially, SAR and ISAR are equivalent in theory because only the relative motion between the target and radar matter. Therefore, the problem ISAR imaging can be considered as equivalent to a SAR imaging problem. In this thesis, we deal with major issues about ISAR and SAR signal processing under various radar configurations. In general, the range-Doppler (RD) image generated with the Doppler resolution is inappropriate for target identification. This is because a direct comparison between the RD images from unknown targets and those from training templates is impossible as a result of the variable Doppler resolution related to target’s own rotational motion, which induces ISAR images with either expanded or compressed shapes along the cross-range direction. Thus, the RD image generated with the Doppler resolution (meter-by-hertz) should be transformed into a scaled image generated with the cross-range resolution in the homogeneous range and cross-range (RC) domain (meter-by-meters) to more efficiently identify targets. In Chapter 2, a novel ISAR cross-range scaling (CRS) method is proposed to rescale an image from the RD domain to RC domain. Under monostatic radar configurations where a transmitter and a receiver are collocated, monostatic ISAR imaging suffers from certain limitations such as geometrical limitations and difficulty in the imaging of stealthy targets. In case of a target moving along the radar line of sight (LOS), a lack of change in the aspect angle of the target makes it difficult to meet the requirements of ISAR imaging with fine resolution in the cross-range direction. Furthermore, the monostatic ISAR often fails to generate ISAR images for stealthy targets, because the energies of the signals received along the LOS of the radar decrease dramatically. Recently, a bistatic configuration used for ISAR imaging has attracted much attention because of its potential to overcome these problems. Since the bistatic configuration, wherein the transmitter and the receiver are spatially separated, is capable of providing adequate look-angle diversity for the target, the rotational motion of the target with respect to the radar is often guaranteed for the acquisition of the desired crossrange resolution. In addition, the bistatic radar configuration can generate ISAR images of stealthy targets. This is because reliable acquisition of the signals reflected from directions other than that of the transmitter can be ensured because of the separated receiver in the bistatic configuration. However, compared to the monostatic ISAR, the bistatic ISAR (Bi-ISAR) has two distinctive features in terms of the following issues: 1) scaled image resolution, and 2) shearing distortion of the image. Thus, range and cross-range scaling (RCRS) and bistatic distortion correction are essential for the efficient use of a Bi-ISAR image in their applications. In Chapter 3, we introduce a novel RCRS technique and bistatic distortion correction method for the restoration of a sheared (Bi-ISAR) image. In the case of a stepped-frequency waveform (SFW) ISAR system, the translational motion (TM) of a target can be usually divided into two parts: 1) target motion within a pulse repetition interval, called the inter-pulse translational motion (IPTM) and 2) target motion between bursts, called the inter-burst translational motion (IBTM). The former induces severe blurring in the ISAR images as well as range-compressed data (i.e., range profile), and the latter also causes dramatic degradation of the ISAR image quality. In Chapter 4, a novel framework for high-resolution gapped SFW (GSFW) ISAR imaging of high-speed maneuvering target is proposed. The main novelty of the proposed method is twofold: 1) accurate TM parameter estimation in conjunction with a compressive sensing theory using a newly devised cost function and particle swarm optimization (PSO) and 2) compensation for both the IPTM and IBTM phase errors simultaneously even with the GSFW data set. When a target with extreme maneuvers undergoes complex motions, ISAR imaging suffers from TM, which is modeled as a one-dimensional (1-D) phase error, and non-uniform rotational motion (RM), which is a multidimensional (MD) phase error that causes severe blurring in ISAR images. Full aperture data collection is often unachievable because of interference with other radar activities, resulting in sparse-aperture (SA) data. In Chapter 5, we present a new framework for SA ISAR imaging and CRS for maneuvering targets based on compressive sensing. Instead of solving conventional optimization problems constrained by a sparsity of signals, the proposed method utilizes the sensing-matrix estimation technique for ISAR image reconstruction using parametric signal-model reconstruction. To do this, it looks for basis functions that best represent the behavior of a sensing-dictionary matrix comprising the observed SA data. The sensing-matrix reconstruction is based on a modified orthogonal matching pursuit (MOMP)-type basis function-searching scheme. Finally, we generate a well-focused and scaled ISAR image from the recovered complete ISAR signal using the conventional Fourier transform after the removal of signals corresponding to 1-D TM and MD RM phase errors. In Chapter 6, we introduce a method of Bi-ISAR imaging and scaling of highly maneuvering target with complex motion to more effectively use Bi-ISAR images in their applications. We note that monostatic ISAR imaging and CRS method, presented in Chapter 5, cannot be applied in Bi-ISAR configurations. For this, we introduce a method to estimate the basis functions that best represent the behavior of a sensing-dictionary matrix comprising the observed SA data of a target in a Bi-ISAR imaging system, and restore a bistatic distortion that yields a sheared shape of Bi-ISAR images. Several simulation results reveals that the proposed method is very efficient in forming Bi-ISAR images of high-speed maneuvering targets in terms of the Bi-ISAR signal reconstruction accuracy. For a SAR system, the individual beam pattern pointing and shaping due to amplitude and phase settings in transmit/receive modules (TRMs) are mainly used in the elevation direction over the total range of incidence angles. The criteria for the optimized antenna elevation beam pattern are profoundly linked to the overall SAR system performance requirements. In particular, a high sidelobe level (SLL) in the antenna pattern leads to a high range ambiguity-to-signal ratio (RASR), which degrades the quality of the SAR image. RASR can be controlled by appropriate antenna SLL suppression at defined positions in the elevation pattern. Chapter 7 focuses on the improvement in SAR system performance using an effective technique for optimizing antenna pattern synthesis. The desired antenna patterns can be synthesized referring to the optimized antenna mask templates using the newly devised cost function and improved particle swarm optimization (IPSO). Even though there are some defective TRMs in array phased antennas, one can regenerate an optimal pattern as close as possible to the desired one, owing to the proposed cost function and IPSO. For SAR imaging, the image quality is usually degraded by some undesired phase errors induced by platform motion aberration, propagation effects, and system phase instability. It is necessary to use data-driven autofocus techniques due to these unpredictable phase errors. In Chapter 8, a novel approach to SA-SAR imaging is proposed based on improved Tikhonov regularization (ITR) coupled with an adaptive strategy using iterative-reweighted-matrix to solve the CS reconstruction problem of SAR images with sparsity. The proposed method can provide different degrees of performance of SAR autofocus with changes to the value of certain parameters of ITR. The proposed scheme outperforms conventional CS-based methods with respect to image quality, noise robustness, and computational complexity of the algorithm owing to the additional sensitivity of the proposed objective function