Frequency Modulated Continuous Waveform Research Articles

Low sampling rate digital dechirp for Inverse Synthetic Aperture Ladar imaging processing

The application of digital dechirp technology to Inverse Synthetic Aperture Ladar (ISAL) can greatly reduce the sampling rate burden on analog-to-digital converters. We address the disadvantage of signal sampling and imaging processing in linear frequency-modulated continuous waveform (LFMCW) ISAL systems and suggest a delay estimation method based on a reference channel to reduce the impact of echo delay error. Our method uses the strongest point in the scene as the scene center to estimate the delay and target distance via dechirped pulse compression. The reference channel data are also used to align the dechirped pulse signal and, in turn, reduce clock errors and ensure azimuth signal quality. To our knowledge, our experiment represents the first successful application of digital dechirp to ISAL systems. Experimental results confirm the applicability of our digital dechirp method to ISAL system in order to reduce sampling requirements.

Efficient Implementation for SBL-Based Coherent Distributed mmWave Radar Imaging

In a distributed frequency-modulated continuous waveform (FMCW) radar system, the echo data collected are not continuous in the azimuth direction, so the imaging effect of the traditional range-Doppler (RD) algorithm is poor. Sparse Bayesian learning (SBL) is an optimization algorithm based on Bayesian theory that has been successfully applied to high-resolution radar imaging because of its strong robustness and high accuracy. However, SBL is highly computationally complex. Fortunately, with FMCW radar echo data, most of the time-consuming SBL operations involve a Toeplitz-block Toeplitz (TBT) matrix. In this article, based on this advantage, we propose a fast SBL algorithm that can be used to obtain high-angular-resolution images, in which the inverse of the TBT matrix can be transposed as the sum of the products of the block lower triangular Toeplitz matrix and the block circulant matrix by using a new decomposition method, and some of the matrix multiplications can be quickly computed using the fast Fourier transform (FFT), decreasing the computation time by several orders of magnitude. Finally, simulations and experiments were used to ensure the effectiveness of the proposed algorithm.

High Fidelity Physics-Based Simulation of a 512-Channel 4D-Radar Sensor for Automotive Applications

Radar has emerged as a core sensing technology for many active-safety and comfort related advanced driver assistance systems (ADAS) being deployed in today’s vehicles. Using radar technology, the ego vehicle can simultaneously detect the range and velocity of multiple targets. With multiple-input, multiple-output (MIMO) arrays, it is also possible to detect the angle of arrival of targets in azimuth and elevation. 4D automotive radar sensors can determine the range, velocity, azimuth and elevation angles-of-arrival (AoA) of targets in a traffic scene. Currently, radar-returns from traffic scenes have been mainly obtained through measurement. While measurement is valuable, it can be costly, time consuming, restrictive due to practicality issues and also unsafe in some corner-case traffic scenarios. Simulation has emerged as a potential alternative source of synthetic radar-returns that can be used to develop, test and refine signal processing techniques and detection algorithms. A key challenge in simulation has been to retain an accurate representation of actors in full-scale traffic scenes while still being able to solve electrically large problems efficiently. The introduction of MIMO arrays further increases the complexity and computational load demands of such simulations. In this paper, we present a computationally efficient, high fidelity, physics-based simulation workflow for a 77 GHz frequency-modulated continuous-waveform (FMCW)-based, 512-channel MIMO radar sensor. We demonstrate how the synthetic radar returns obtained from full-scale traffic scene simulations can be used to create 4D-radar point clouds. The accuracy of the synthetic radar returns is then evaluated by overlaying the resulting 4D-radar point clouds on 4 corresponding full-scale traffic scenes with varying levels of complexity. Results from this study demonstrate how accurate radar returns obtained from simulation can be used to develop next-generation radar sensors for autonomous vehicles.

Doppler-Spread Targets Detection for FMCW Radar Using Concurrent RDMs

An amplitude weighted cross-correlation detector is proposed for the detection of the Doppler-spread target (DST), based on the frequency modulated continuous waveform (FMCW) radar. The concurrent range-Doppler matrices (RDM) make cross-correlation optimized with higher accumulation gain and significantly reduced computation effort. A novel measurement of signal sparseness is defined. The quantitative analysis of the proposed detector, compared with the energy integration detector, is realized based on the signal sparseness. Two generalizations of proposed detector are discussed for the number of antennas greater than two. Data from a walking pedestrian are got and used to verify the effectiveness of the proposed detector.

Intra-Pulse Frequency Coding Design for a High-Resolution Radar against Smart Noise Jamming

Smart noise jamming forms active jamming by intercepting, modulating, and forwarding radar signals into the radar receiver, which seriously affects the radar range recovery performance. In this paper, we propose a novel waveform design approach and an efficient range recovery method for high-resolution radar in the jamming scenario. Firstly, we propose an intra-pulse frequency-coded frequency-modulated continuous waveform (IPFC-FMCW), which contains multiple FMCW chips with different widths and frequencies, to combat the smart noise jamming. After the jamming suppression, the proposed waveform has a low sidelobe level, which is different from traditional FMCW signals for which the observations are periodically missing, resulting in high sidelobe levels. Then, to improve the range recovery performance of the waveform after jamming suppression, we optimize the range profile by designing the transmit waveform and then solve it by a simulated annealing algorithm. Next, based on the designed waveform, we derive the echo model after jamming suppression and propose a gridless compressed sensing (CS) method to recover the range of the targets. Compared with the existing waveforms and methods, the proposed waveform and the processing method achieve better range recovery performance in the jamming scenario. Numerical simulations are utilized to demonstrate the range recovery effectiveness of the proposed waveform and method in smart noise jamming.

Length Prediction of Moving Vehicles Using a Commercial FMCW Radar

The classification of moving vehicles on roadways is an important application of intelligent transportation systems. For successful vehicle classification using radar, suitable feature extraction with high accuracy from the returned echoes of moving vehicles is necessary. In this paper, a novel framework for predicting the length of each moving vehicle is presented based on two different scenarios of the frequency-modulated continuous-waveform (FMCW) radar, i.e., the radar is stationary or moving. Different clutter reduction techniques were considered depending on the motion of the FMCW radar. Using clutter-free signals, moving vehicles are separated on a range-angle map, rather than on a range-Doppler (R-D) map, enabling the successful separation whether the vehicles are overlapping or not on the R-D map. Finally, the scattering center information across multiple frames, rather than a single frame, is fused to significantly increase the accuracy of the predicted length. The proposed framework was demonstrated through experiments in an actual road environment using a commercial FMCW radar. The results indicated a good match with the true lengths of the vehicles.

Low-Complexity Joint 3D Super-Resolution Estimation of Range Velocity and Angle of Multi-Targets Based on FMCW Radar.

Multi-dimensional parameters joint estimation of multi-targets is introduced to implement super-resolution sensing in range, velocity, azimuth angle, and elevation angle for frequency-modulated continuous waveform (FMCW) radar systems. In this paper, a low complexity joint 3D super-resolution estimation of range, velocity, and angle of multi-targets is proposed for an FMCW radar with a uniform linear array. The proposed method firstly constructs the size-reduced 3D matrix in the frequency domain for the system model of an FMCW radar system. Secondly, the size-reduced 3D matrix is established, and low complexity three-level cascaded 1D spectrum estimation implemented by applying the Lagrange multiplier method is developed. Finally, the low complexity joint 3D super-resolution algorithms are validated by numerical experiments and with a 77 GHz FMCW radar built by Texas Instruments, with the proposed algorithm achieving significant estimation performance compared to conventional algorithms.

Sectorized FMCW MIMO Radar by Modular Design With Non-Uniform Sparse Arrays

Automotive radars are designed to enhance road user safety and reduce the number of accidents on public roads and there is a constant demand to improve the performance for these radars. In this paper, a novel proof-of-concept multiple-input multiple-output (MIMO) radar architecture is presented by frequency modulated continuous waveform (FMCW) transmission. For enhanced angular resolution, the radar uses a two-tier antenna setup leading to a sparse array arrangement, mainly in an effort to mitigate grating lobes and to offer different illuminations of the same scenario. Also, the experimentally verified sparse radar antenna designed for target detection at the receiver, achieves modest sidelobe levels and grating lobes well below 12 dB from the main beam maximum, whilst still maintaining competitive half-power beamwidths when compared to more conventionally spaced arrays. Moreover, the high impedance bandwidth of this receiver array and the supporting radar electronics (more than 6%) allows for the detection of targets at only a 10 cm spatial separation. The complete system is also capable of seeing a wide field-of-view (FOV) since it utilizes a network of radar modules to cover the forward −90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> to +90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> angular range by sectorization. In the best case, the measured radar system can resolve targets that are a distance of just <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm 2^\circ$</tex-math></inline-formula> apart in angular separation.

MRPT: Millimeter-Wave Radar-Based Pedestrian Trajectory Tracking for Autonomous Urban Driving

Pedestrians represent agile and low-observable targets, especially under adverse weather conditions, whose trajectory tracking plays a crucial role in determining pedestrian behavior in autonomous urban driving. Thus, this article presents a millimeter-wave radar-based pedestrian trajectory-tracking (MRPT) system that enables all-weather trajectory perception with high precision. More specifically, to improve the tracking performance in the presence of strong background clutters, a track-before-detection-based algorithm is proposed to address pedestrian detection and tracking jointly, instead of regarding them as two separate phases in conventional methods. After that, a continuous detection method based on the integration of target existence probability and the Markov transition matrix is proposed to achieve superior pedestrian detection performance by directly using unthresholded radar data. In addition, based on the binary-phase-multiplex (BPM) strategy, the proposed system is validated using a low-cost automotive frequency-modulated-continuous-waveform (FMCW) multiple-input–multiple-output (MIMO) radar sensor. Consequently, extensive simulation and experimental results demonstrate that MRPT exhibits better pedestrian detection and tracking performance under low signal-noise-ratio (SNR) conditions compared with traditional methods.

Automotive radar interference study for different radar waveform types

Mutual interference between different radar waveforms used in automotive radar applications is studied. The existing interference analysis is extended to a generalised radar-to-radar interference equation that covers most of the common interference scenarios for automotive radar systems. The outcome of the generalised equation is demonstrated for a number of typical scenarios where radars with different continuously transmitting waveforms are involved. The proposed equation can be used to characterise the received interference and its features by analysing the instantaneous beat frequency of the victim radar. Moreover, an interference analysis of phase-coded frequency-modulated continuous waveforms is performed and demonstrated experimentally by using real-time automotive radars for the first time in the literature. The experimental results corroborate the interference analysis of different waveforms and validate the proposed generalised interference equation under various conditions.

A small vessel detection using a co-located multi-frequency FMCW MIMO radar

Small vessels detection is a known issue due to its low radar cross section (RCS). An existing shore-based vessel tracking radar is for long-distance commercial vessels detection. Meanwhile, a vessel-mounted radar system known for its reliability has a limitation due to its single radar coverage. The paper presented a co-located frequency modulated continuous waveform (FMCW) maritime radar for small vessel detection utilising a multiple-input multiple-output (MIMO) configuration. The radar behaviour is numerically simulated for detecting a Swerling 1 target which resembles small maritime’s vessels. The simulated MIMO configuration comprised two transmitting and receiving nodes. The proposal is to utilize a multi-frequency FMCW MIMO configuration in a maritime environment by applying the spectrum averaging (SA) to fuse MIMO received signals for range and velocity estimation. The analysis was summarised and displayed in terms of estimation error performance, probability of error and average error. The simulation outcomes an improvement of 2.2 dB for a static target, and 0.1 dB for a moving target, in resulting the 20% probability of range error with the MIMO setup. A moving vessel's effect was observed to degrade the range error estimation performance between 0.6 to 2.7 dB. Meanwhile, the proposed method was proven to improve the 20% probability of velocity error by 1.75 dB. The impact of multi-frequency MIMO was also observed to produce better average error performance.

FRaC: FMCW-Based Joint Radar-Communications System Via Index Modulation

Dual function radar communications (DFRC) systems are attractive technologies for autonomous vehicles, which utilize electromagnetic waves to constantly sense the environment while simultaneously communicating with neighbouring devices. An emerging approach to implementing DFRC systems is to embed information in radar waveforms via index modulation (IM). Implementation of DFRC schemes in vehicular systems gives rise to strict constraints in terms of cost, power efficiency, and hardware complexity. In this paper, we extend IM-based DFRC systems to utilizing sparse arrays and frequency modulated continuous waveforms (FMCWs), which are popular in automotive radar for their simplicity and low hardware complexity. The proposed FMCW-based radar-communications system (FRaC) operates at reduced cost and complexity by transmitting with a reduced number of radio frequency modules, combined with narrowband FMCW signalling. This is achieved via array sparsification in transmission, formulating a virtual multiple-input multiple-output array by combining the signals in one coherent processing interval, in which the narrowband waveforms are transmitted in a randomized manner. Performance analysis and numerical results show that the proposed radar scheme achieves similar resolution performance compared with a wideband radar system operating with a large receive aperture, while requiring less hardware overhead. For the communications subsystem, FRaC achieves higher rates and improved error rates compared to dual-function signalling based on conventional phase modulation.

The Penetration Analysis of Airborne Ku-Band Radar Versus Satellite Infrared Lidar Based on the Height and Energy Percentiles in the Boreal Forest

The paper investigates the penetration properties of an airborne Ku-band frequency modulated continuous waveform (FMCW) profiling radar named Tomoradar and a satellite near-infrared lidar into the boreal forest of Finland. We achieve the accumulative energy distributions based on the Tomoradar waveforms and the satellite lidar waveforms generated from the high-density airborne lidar data within Tomoradar footprints. By comparing two groups of the height percentiles and energy percentiles derived from the accumulative energy distributions, we evaluate the relationship of penetrations between the Ku-band microwave and near-infrared laser according to the coefficients of the determination (COD), and the root mean square errors (RMSE) of linear regression analyses. The quantitative analysis results demonstrate that the height and energy percentiles derived from Tomoradar waveforms correlate well with those from satellite lidar waveforms with the mean correlation coefficients of more than 0.78 and 0.85. The linear regression models for the height and energy percentile produce excellent fits with the mean CODs of 0.95 and 0.90 and the mean RMSEs of 1.25 m and 0.03, respectively. Less than 15% of height percentiles and 87.54% of the energy percentiles in the sixth stratum near the ground derived from Tomoradar waveforms surpass those from satellite lidar waveforms. Hence, the Ku-band microwave can penetrate deeper into the forest than the near-infrared laser at the same spatial scale. In addition, quadratic fitting models are established to describe the differences of the height percentile (DHP) and the energy percentile (DEP) to expound the canopy height and closure contributions numerically. The facts that the CODs of the DHP and DEP individually are more than 0.96 and 0.89 and the fitting residual histograms approximate to normal distributions reveal the reliabilities of the proposed fitting models. Thus, the penetration analyses are valid for the explorations on the FMCW radar applications and the data fusion of the Ku-band radar and near-infrared lidar in the forest investigations.

High-Frequency Radar Cross Section of Ocean Surface for an FMICW Source

The frequency-modulated interrupted continuous waveform (FMICW) has been widely used in remotely sensing sea surface states by high-frequency ground wave radar (HFGWR). However, the radar cross section model of the sea surface for this waveform has not yet been presented. Therefore, the first- and second-order cross section models of the sea surface about this waveform are derived in this study. The derivation begins with the general electric field equations. Subsequently, the FMICW source is introduced as the radar transmitted signal to obtain the FMICW-incorporated backscattered electric field equations. These equations are used to calculate range spectra by Fourier transforming. Therefore, Fourier transformation of the range spectra calculated from successive sweep intervals gives the Doppler spectra or the power spectral densities. The radar cross section model is obtained by directly comparing the Doppler spectra with the standard radar range equation. Moreover, the derived first- and second-order radar cross section models for an FMICW source are simulated and compared with those for a frequency-modulated continuous waveform (FMCW) source. Results show that the cross section models for the FMICW and FMCW cases have different analytical expressions but almost the same numerical results.

OFDM Waveform Design for Interference Resistant Automotive Radars

Autonomous driving is experiencing a monumental technological shift, making it one of the most highly discussed topics of research. With novel technologies, like advanced driver assistance systems (ADAS), the automotive industry is making significant advancements in both driving comfort and safety. ADAS rely heavily on Frequency Modulated Continuous Waveform (FMCW) radar sensors for speed and range estimation. However, this technology suffers from interference management problem and an inherent ghost target detection with few specific waveforms. This paper introduces a new technique that utilizes Orthogonal Frequency-Division Multiplexing (OFDM) waveform to overcome ghost detection and increase robustness against interference. OFDM leverages maximum length sequence (MLS) (i.e., m-sequence) and provides orthogonality between different transmitted waveforms. Also, incorporating an appropriate scrambled sequence to the proposed waveform further reduces the impact of interference and improves the reliability of target detection. Simulations were conducted to demonstrate proposed algorithm performance for different scenarios, like single- and multiple-neighbors. Furthermore, results were compared and analyzed relative to conventional FMCW in similar scenarios.

Lane-by-Lane Traffic Monitoring Using 24.1 GHz FMCW Radar System

In this article, we propose a method to monitor traffic on the road using a 24.1 GHz radar system. In our work, we design our own traffic monitoring radar system from radar hardware, such as antenna systems and the transceiver, to radar software, such as detection and tracking algorithms. The radar we developed uses frequency-modulated continuous waveforms with a center frequency of 24.1 GHz, and its range resolution is tens of centimeters, which is enough to distinguish vehicles in multiple lanes. In addition, we can effectively distinguish vehicles by estimating their angles using an array antenna system. To evaluate the performance of our radar, we install it on the pedestrian overpass facing the road. From actual measurement results, our radar system can reliably estimate the distance to the vehicle, the speed of the vehicle, and the angle of the vehicle for a total of four lanes up to 300 m in real time. The traffic monitoring system using a radar sensor can compensate for the disadvantages of the camera-based road monitoring method.

FPGA-Based Wigner–Hough Transform System for Detection and Parameter Extraction of LPI Radar LFMCW Signals

Low probability of intercept (LPI) radars using linear frequency-modulated continuous waveforms (LFMCWs) is rapidly proliferating in the military domain and posing a serious challenge to conventional electronic support receivers (ESRs). In this article, we provide a practical approach that can be used in digital ESR systems for real-time LFMCW radar signal detection and parameter extraction under challenging SNR levels. We proposed a modified version of the Wigner–Ville distribution (WVD), namely, short-time Wigner distribution (STWD), postprocessing steps, e.g., column-based thresholding of STWD combined with the Hough transform (HT), and a field-programmable gate array (FPGA) design as an embedded solution with low computational complexity. The performance of the proposed system is tested with MATLAB and hardware simulations using a signal database generated with different signal-to-noise ratio (SNR) levels. The system is then embedded into a Virtex-7 FPGA where the performance evaluations showed satisfactory detection results down to −6-dB SNR level, and parameter extraction results down to −8-dB SNR level.

Multi-Person Recognition Using Separated Micro-Doppler Signatures

It is challenging to recognize individuals when they move in the radar field of view due to the superimposition of micro-Doppler signatures. This paper presents a multi-person recognition approach by separating micro-Doppler signatures of multiple persons into their individual components. The preliminary separation can be obtained by their range difference in a high resolution radar. A multi-task learning network is designed for both the fine separation of micro-Doppler signatures and the personnel recognition. A frequency modulated continuous waveform (FMCW) radar that operates at 77 GHz for automotive applications is used in experiments. The proposed deep-neural-network-based approach gives a convincing result in the test.

A real-time three-dimensional coherent ladar demonstration: System structure, imaging processing, and experiment result

A new real-time, high-precision three-dimensional (3D) laser radar with a coherent system is presented. A self-designed arbitrary waveform generator (AWG), an electro-optic modulator, and an optical filter are proposed to generation a linear frequency modulated continuous waveform (FMCW) with a bandwidth of 2.4 GHz and pulse repetition frequency (PRF) of 100kHz. The heterodyne coherent detection and Micro-Electro-Mechanical System (MEMS) scanner are implemented with high sensitivity and fast 3D imaging. At the same time, multi-phase FFT algorithm is proposed to realize the frequency calculation with real-time. In this paper, we present the system structure, signal processing, and the experimental results in detail. The system achieves static 3D target and dynamic target imaging verification, and the experiment result validates the performance of system for real-time 3D imaging. It acquires 3D imaging at 40 frames per second (fps), which can satisfy the demand of high imaging speed requirements for special situation.

Lidar-aided analysis of boreal forest backscatter at Ku band

This paper analyzes the backscatter of the microwave signal in a boreal forest environment based on a Ku -band airborne Frequency-Modulated Continuous Waveform (FMCW) profiling radar—Tomoradar. We selected a half-managed boreal forest in the southern part of Finland for a field test. By decomposing the waveform collected by the Tomoradar, the vertical canopy structure was achieved. Based on the amplitude of the waveform, the Backscattered Energy Ratio of Canopy-to-Total (BERCT) was calculated. Meanwhile, the canopy fraction was derived from the corresponding point cloud recorded by a Velodyne VLP-16 LiDAR mounted on the same platform. Lidar-derived canopy fraction was obtained by counting the number of the first/ the strongest returns versus the total amount of returns. Qualitative and quantitative analysis of radar-derived BERCT on lidar-derived canopy fraction and canopy height are investigated. A fitted model is derived to describe the Ku-band microwave backscatter in the boreal forest to numerically analyze the proportion contributed by four factors: lidar-derived canopy fraction, radar-derived canopy height, the radar-derived distance between trees and radar sensor and other factors, from co-polarization Tomoradar measurements. The Root Mean Squared Error (RMSE) of the proposed model was 0.0958, and the coefficient of determination R2 was 0.912. The fitted model reveals that the correlation coefficient between radar-derived BERCT and lidar-derived canopy fraction is 0.84, which illustrates that lidar surface reflection explains the majority of the profiling /waveform radar response. Thus, vertical canopy structure derived from lidar can be used for the benefit of radar analysis.