This paper considers the problem of power minimization-based robust orthogonal frequency division multiplexing (OFDM) radar waveform design, in which the radar coexists with a communication system in the same frequency band. Recognizing that the precise characteristics of target spectra are impossible to capture in practice, it is assumed that the target spectra lie in uncertainty sets bounded by known upper and lower bounds. Based on this uncertainty model, three different power minimization-based robust radar waveform design criteria are proposed to minimize the worst-case radar transmitted power by optimizing the OFDM radar waveform, which are constrained by a specified mutual information requirement for target characterization and a minimum capacity threshold for communication system. These criteria differ in the way the communication signals scattered off the target are considered: 1) as useful energy, 2) as interference, or 3) ignored altogether at the radar receiver. Numerical simulations demonstrate that the radar transmitted power can be efficiently reduced by exploiting the communication signals scattered off the target at the radar receiver. It is also shown that the robust waveforms bound the worst-case power-saving performance of radar system for any target spectra in the uncertainty sets.

/pdf/power-minimization-based-robust-ofdm-radar-waveform-design-25bbydu5y4.pdf

Power Minimization-Based Robust OFDM Radar Waveform Design for Radar and Communication Systems in Coexistence

With the significant development of practicability in deep learning and the ultra-high-speed information transmission rate of 5G communication technology will overcome the barrier of data transmission on the Internet of Vehicles, automated driving is becoming a pivotal technology affecting the future industry. Sensors are the key to the perception of the outside world in the automated driving system and whose cooperation performance directly determines the safety of automated driving vehicles. In this survey, we mainly discuss the different strategies of multi-sensor fusion in automated driving in recent years. The performance of conventional sensors and the necessity of multi-sensor fusion are analyzed, including radar, LiDAR, camera, ultrasonic, GPS, IMU, and V2X. According to the differences in the latest studies, we divide the fusion strategies into four categories and point out some shortcomings. Sensor fusion is mainly applied for multi-target tracking and environment reconstruction. We discuss the method of establishing a motion model and data association in multi-target tracking. At the end of the paper, we analyzed the deficiencies in the current studies and put forward some suggestions for further improvement in the future. Through this investigation, we hope to analyze the current situation of multi-sensor fusion in the automated driving process and provide more efficient and reliable fusion strategies.

/pdf/multi-sensor-fusion-in-automated-driving-a-survey-2331lj978b.pdf

Multi-Sensor Fusion in Automated Driving: A Survey

In this paper, we present a new technique to exploit the data acquired simultaneously by multiple radar sensors carried by multiple air platforms to increase the cross-range resolution of inverse synthetic aperture radar (ISAR) images of rotating targets. This distributed ISAR technique is devised for two different cases: 1) multiple-input-multiple-output (MIMO) case with each platform carrying an active radar that transmits and receives RF waveforms and 2) multistatic case with a single platform carrying an active radar (transmitting and receiving) and the remaining platforms equipped with passive sensors (namely, receiving only). The processing chain proposed for the distributed ISAR is shown, together with the results obtained against simulated ISAR data for both the MIMO and the multistatic cases. The performance analysis shows that the proposed technique is able to provide an increase of the cross-range resolution up to the number of platforms in the multistatic case and even higher in the MIMO case, if the platforms are properly located. This is of great benefit in applications where the target rotation angle is insufficient to guarantee the desired resolution. A typical case is the imaging of ship targets with rotation induced by the sea swell structure under low sea state conditions. To make the results appealing for practical application, the performance degradation is also analyzed arising from errors in the knowledge of both the target rotation motion and the acquisition geometry. Experimental data collected by a ground-based radar operating together with a rotating platform are processed by following the presented distributed ISAR technique to validate the proposed approach.

Multistatic and MIMO Distributed ISAR for Enhanced Cross-Range Resolution of Rotating Targets

Accurate and timely prediction of defect dimensions from magnetic flux leakage signals requires one to solve an inverse problem efficiently. This paper proposes a new inversing approach to such a problem. It combines cuckoo search (CS) and particle filter (PF) to estimate the defect profile from measured signals and adopts a radial-basis function neural network as a forward model as well as the observation equation in PF. As one of the latest nature-inspired heuristic optimization algorithms, CS can solve high-dimensional optimization problems. As an effective estimator for a nonlinear filtering problem, PF is applied to the proposed inversing approach in order to improve the latter’s robustness to the noise. The resulting algorithm enjoys the advantages of both CS and PF where CS produces the optimized state sequence for PF while PF processes the state sequence and estimates the desired profile. The simulation and experimental results have demonstrated that the proposed approach is significantly better than the inversing approach based on CS alone in a noisy environment.

Cuckoo Search and Particle Filter-Based Inversing Approach to Estimating Defects via Magnetic Flux Leakage Signals

This paper considers the problem of passive detection with a multistatic radar system involving a noncooperative illuminator of opportunity (IO) and multiple receive platforms. An unknown source signal is transmitted by the IO, which illuminates a target of interest. These receive platforms are geographically dispersed, and collect independent target echoes due to the illumination by the same IO. We consider a generalized canonical correlation (GCC) detector for passive detection which requires the knowledge of the noise power. We derive closed-form expressions for the probabilities of false alarm and detection of this detector. For the case where the noise power is unknown, we propose a generalized likelihood ratio test (GLRT) detector to deal with the passive detection problem. Moreover, a closed-form expression for the probability of false alarm of this GLRT detector is given, which shows that the proposed GLRT detector exhibits a constant false alarm rate property with respect to the noise power. Numerical simulations demonstrate that the proposed GLRT detector generally outperforms the generalized coherence detector, a previous popular passive detector that neither requires the knowledge of the noise power. In addition, the GLRT also outperforms the GCC detector when the latter has an uncertainty in its knowledge of the noise power.

Two Target Detection Algorithms for Passive Multistatic Radar

In this paper, a 3D inverse synthetic aperture radar (ISAR) imaging method based on an antenna array configuration is proposed. The performance of conventional interferometric ISAR imaging system using three antennas is poor, as the positions of scatterers, which have the same range-Doppler value and projected onto the ISAR plane as a synthesis scatterer, cannot be correctly estimated. However, by using two antenna arrays perpendicular to each other, the system's ability to separate these scatterers can be improved. The criterion for the selection of a range unit that contains an isolated scatterer in the 2-D array domain for doing motion compensation is discussed. If there is no range unit which contains only an isolated scatterer, then radial and cross-range motion compensation has to be carried out by motion parameter estimation. Two cross-range motion parameters measurement algorithms, one based on array processing of the range profile and another based on correlation of ISAR images of different antennas, are proposed. The coordinates registration problem for the scatterers of a synthesis scatterer is also discussed. Simulation results have shown the effectiveness of the proposed methods.

Three-Dimensional ISAR Imaging Based on Antenna Array

A wideband dual-polarised patch antenna with high isolation and a low cross-polarisation level is presented. Two orthogonal linearly-polarised modes are excited by the electromagnetic-fed structure. One of the two modes is excited by a pair of hook-shaped probes with a 180o phase difference; while the other is excited by a magnetic-coupled loop, which is comprised of a metal loop and an open-ended transmission line. By introducing two shorting pins, the isolation between two feeding ports can be enhanced to more than 40 dB. Moreover, the radiation patterns in the ± 45° planes have a cross-polarisation level of less than − 30 dB within the 3 dB beam widths. The measured − 10 dB reflection coefficients bandwidth of the two modes are 40.7% (2.45–3.7 GHz) and 38.7% (2.6–3.85 GHz). The gains of the proposed antenna are about 8.7 and 9.1 dBi over the operating band. Thus, this antenna is highly suitable for the base station antenna that is required to cover the operating bandwidth of 3.5 GHz WiMAX systems.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/el.2012.3934

Wideband dual-polarised electromagnetic-fed patch antenna with high isolation and low cross-polarisation

This study investigates the problem of manoeuvring target tracking with passive coherent location (PCL) radar. The main challenge stems from the non-linearities of the dynamic state space and the non-Gaussian measurement noise. The contribution of the study is twofold. First, to deal with the problem of manoeuvring target tracking in the presence of glint noise, an interacting multiple model (IMM) particle filtering (PF) method using multiple time-of-arrival (TOA) measurements from several transmitter-receiver pairs are proposed and evaluated. Second, because tracking precision is sensitive to the configuration of transmitters and receiver stations, the authors provide insight into the influence of the configuration of transmitters and receiver stations on the tracking methods of PCL radar. Simulations illustrate the effectiveness of the proposed method. Compared with conventional IMM algorithm and PF algorithm in different glint noise environments and various configurations of transmitters and receiver stations, the proposed method obtains better estimates of position, velocity and acceleration.

Particle filter for manoeuvring target tracking via passive radar measurements with glint noise

This study examines the problem of solving the range ambiguity in analogue TV (ATV)-based passive bistatic radar (PBR). When matched filter (MF) is applied to ATV-based PBR, a range ambiguity at 19.2 km will be produced because of the 64 s line fly-back time of the ATV signal. The relatively short range and the severity of this ambiguity make application of this type of waveform much more likely to be limited. A novel mismatched filter (MMF) algorithm is developed for solving the problem of range ambiguity. In this new MMF algorithm, the MMF factor is acquired based on minimising the overall impact of the SNR loss (SL) compared to MF and the range sidelobes energy that the authors want to suppress. And a batch version with much computational saving is also introduced. Then the performance of this batch version is studied in detail. Finally, its effectiveness is demonstrated with the application to real data acquired with an experimental PBR system.

Mismatched filter for analogue TV-based passive bistatic radar

Conventional antenna array 3-D inverse synthetic aperture radar (ISAR) imaging method does imaging by first carrying out ISAR imaging for all the different antennas and then using array direction of arrival (DOA) estimation on every scatterer to get the position of that scatterer. The aperture formed by the target's motion is used to separate the scatterers, and the antenna array aperture is used for position measurement. In this letter, a 3-D imaging method based on a combination of sparse array beamforming and ISAR imaging is proposed, where 3-D images are formed by space-time match filtering. The estimation of the target's rotation parameters is also given. Simulation results have shown the effectiveness of the proposed methods.

Jun Wang

Papers

Three-Dimensional ISAR Imaging Based on Antenna Array

Wideband dual-polarised electromagnetic-fed patch antenna with high isolation and low cross-polarisation

Particle filter for manoeuvring target tracking via passive radar measurements with glint noise

Mismatched filter for analogue TV-based passive bistatic radar

Three-Dimensional ISAR Imaging Using a Two-Dimensional Sparse Antenna Array