Target Doppler Research Articles

Enhancing the correlation between azimuth and Doppler frequency for speed estimation of nearby tangential targets

With the development of intelligent transportation systems, traffic supervision radar with wide coverage plays a crucial role in traffic management and vehicle-road coordination. The correlation between Doppler frequency and azimuth has been widely validated in wide coverage traffic supervision radar for high-precision velocity measurement. However, angular glint and noise of the nearby targets lead to a decrease in correlation between the azimuth and Doppler frequency, which negatively impacts the accuracy of velocity estimation. Currently, adopting separate filtering strategies for target azimuth and Doppler frequency has limited performance in enhancing correlation. This paper presents a joint observation model for azimuth and Doppler frequency to achieve the extraction of interrelated components from subspaces, which improves the accuracy of velocity measurement. The effectiveness of this approach is validated using data obtained from X-band and Ku-band sensors.

A novel direct detection lidar compatible with software-defined radio

This paper proposes a novel direct detection lidar compatible with software-defined radio, which can effectively absorb the advantages of radio frequency technology. The radio frequency signal is firstly loaded onto the output light of the lidar through a fiber phase modulator and then demodulated from its backward reflected light through a combination of fiber Fabry-Perot interferometer and photodetector. The generation of the transmitting radio frequency signal and the sampling and processing of the receiving radio frequency signal are completed by a software-defined radio. Theoretically, it has been proven that the range and velocity of the target cause phase delay and Doppler frequency shift of the radio frequency signal loaded on the lidar, just like using the radio frequency signal to directly detect the target in radar. In the experiment, by programming the software-defined radio, the ranging and velocimetry algorithms of radar have been applied to this lidar. The theoretical and experimental results indicate that the lidar proposed in this paper has software definable characteristics. Through programming, different functions can be achieved, and in the future, more advanced algorithms of radar can be easily transplanted to further improve its performance.

Reduced-complexity multiple parameters estimation via toeplitz matrix triple iteration reconstruction with bistatic MIMO radar

In this advanced exploration, we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output (MIMO) radar systems, a crucial technique for target localization and imaging. Our research innovatively addresses the joint estimation of the Direction of Departure (DOD), Direction of Arrival (DOA), and Doppler frequency for incoherent targets. We propose a novel approach that significantly reduces computational complexity by utilizing the Temporal-Spatial Nested Sampling Model (TSNSM). Our methodology begins with a multi-linear mapping mechanism to efficiently eliminate unnecessary virtual Degrees of Freedom (DOFs) and reorganize the remaining ones. We then employ the Toeplitz matrix triple iteration reconstruction method, surpassing the traditional Temporal-Spatial Smoothing Window (TSSW) approach, to mitigate the single snapshot effect and reduce computational demands. We further refine the high-dimensional ESPRIT algorithm for joint estimation of DOD, DOA, and Doppler frequency, eliminating the need for additional parameter pairing. Moreover, we meticulously derive the Cramér-Rao Bound (CRB) for the TSNSM. This signal model allows for a second expansion of DOFs in time and space domains, achieving high precision in target angle and Doppler frequency estimation with low computational complexity. Our adaptable algorithm is validated through simulations and is suitable for sparse array MIMO radars with various structures, ensuring higher precision in parameter estimation with less complexity burden.

AUV instantaneous velocity estimation method based on azimuth and Doppler shift

Autonomous underwater vehicle (AUV), as a new underwater operation platform, has been widely used in the field of ocean engineering. AUV underwater condition monitoring is an important guarantee for the safety and effective execution of AUV operations. To meet the requirement of real-time and high-precision underwater instantaneous velocity monitoring of AUV, this paper proposed an AUV instantaneous velocity estimation method. The method takes the target azimuth and Doppler shift as the observation information. It comprehensively uses the geometric relationship among the target azimuth, heading angle, radial velocity angle, and the physical relationship between Doppler and radial velocity to construct an AUV instantaneous velocity estimation model based on distributed dual acoustic observation nodes. We derive the analytical solution of AUV speed and heading angle and analyze the influence of each observation error on the estimation precision of the algorithm. The research and practical data processing results show that the proposed method can effectively estimate the instantaneous velocity of AUV. Compared with the traditional method based on position difference, it has the advantages of high estimation precision, is not limited by frame interval, and is adaptive to many kinds of motion modes. When AUV moves in a straight line at a uniform speed, the precision of speed estimation and heading angle estimation of the algorithm proposed in the paper is 0.506 m/s and 6.146°, which is equivalent to that of the traditional method based on position difference with 50 frame intervals.

Polarimetric Autocorrelation Matrix: A New Tool for Joint Characterizing of Target Polarization and Doppler Scattering Mechanism

Polarimetric Autocorrelation Matrix: A New Tool for Joint Characterizing of Target Polarization and Doppler Scattering Mechanism

A design of a high‐resolution frequency modulated continuous wave radar for drone detection based on spurious phase noise and discrete clutter reduction

AbstractThe authors deal with the problem of the design and manufacture of a high‐resolution FMCW radar for drone detection and classification. The difficulties of the problem are discrete clutter reduction and spurious phase noise mitigation. The discrete clutter is due to the reflected signals from land vehicles, birds etc., while the spurious phase noise is inherent in the radar signal due to the phase‐locked loop component and leakage between the transmitting and receiving paths. Both spurious phase noise and clutter will increase the system noise level and hence reduce the probability of detection of small targets such as drones and induce false alarms on the radar screen. In order to reduce discrete clutter, the authors propose a method to separate a drone from discrete clutter based on the design of the radar system parameters for a drone and its propeller detection, target's Doppler dispersion and moving characteristics. For spur mitigation, a method that focuses on the design of the isolation coefficient between transmitting and receiving paths to decrease the power of spurs below the minimum power requirement at the input of the analogue‐to‐digital converter is introduced. The results were applied by the authors to the development and manufacture of a radar with the given specifications for drone detection and classification. Different laboratory and field tests show that the spurs are mitigated and the drones are separated from discrete clutter with a range and accuracy better than the one recently published.

A spawning particle filter for defocused moving target detection in GNSS-based passive radar

Global Navigation Satellite System (GNSS)-based passive radar (GBPR) has been widely used in remote sensing applications. However, for moving target detection (MTD), the quadratic phase error (QPE) introduced by the non-cooperative target motion is usually difficult to be compensated, as the low power level of the GBPR echo signal renders the estimation of the Doppler rate less effective. Consequently, the moving target in GBPR image is usually defocused, which aggravates the difficulty of target detection even further. In this paper, a spawning particle filter (SPF) is proposed for defocused MTD. Firstly, the measurement model and the likelihood ratio function (LRF) of the defocused point-like target image are deduced. Then, a spawning particle set is generated for subsequent target detection, with reference to traditional particles in particle filter (PF) as their parent. After that, based on the PF estimator, the SPF algorithm and its sequential Monte Carlo (SMC) implementation are proposed with a novel amplitude estimation method to decrease the target state dimension. Finally, the effectiveness of the proposed SPF is demonstrated by numerical simulations and preliminary experimental results, showing that the target range and Doppler can be estimated accurately.

Time Reversal Detection for Moving Targets in Clutter Environments

Moving target detection plays a pivotal role in many applications, especially in radar systems. In addition, time-reversal (TR) technology can improve radar detection probability by effectively solving the problem of multipath signals. However, detection performance can be affected when clutter exists and target Doppler shifts are unknown. For this reason, we propose a novel moving target detection algorithm that leverages TR technology and a novel waveform optimization method in clutter and multipath signal environments. In this paper, we establish a TR signal model for a moving target in such environments and derive the TR average likelihood ratio test (TR-ALRT) detector. Additionally, we introduce a waveform optimization method that adapts to clutter and unknown Doppler information further to enhance the performance of the TR-ALRT detector. Consequently, a TR-ALRT waveform optimization (TR-ALRT-WO) detector is derived to adapt to the unknown Doppler and environment information, thereby improving the detection probability. Comparative analysis with other detectors under the same conditions validates the effectiveness of our proposed TR-ALRT-WO detector. Furthermore, numerical experiments demonstrate the superior performance of the proposed detection algorithm.

A Computationally Efficient Estimator for the Target Angle, Delay, and Doppler Parameters in Passive Radar

The azimuth, elevation, time delay (TD), and Doppler shift (DS) of the target echo signal are important parameters for target localization in passive radar. In this paper, for the problem of joint estimation of target azimuth, elevation, TD, and DS parameters in passive radar, a computationally efficient estimator is proposed based on segmented coherent integration and uniform circular array (UCA) interferometer direction finding. First, according to the parameters of the target motion and illuminators of opportunity, the reference and surveillance signals are divided into segments, with fast time within a segment and slow time across the segments; then, matching filtering along the fast time and fast Fourier transform (FFT) along the slow time are performed to accumulate the target echo energy to the same delay-Doppler cell; finally, the UCA interferometer two-dimensional direction finding is performed at the delay-Doppler cell where the target echo is located, to achieve the target azimuth and elevation estimate. Simulation results demonstrate that the proposed algorithm has low computational complexity, high real-time processing capability, and can achieve efficient and real-time estimation of the azimuth, elevation, TD, and DS parameters of weak target echoes in passive radar.

Simulation of a Radio Signal Scattered from a Rough Surface by the Method of Stationary Points

Introduction. Many radar and radio navigation problems require adequate (i.e., corresponding to the physics of radio wave scattering under a certain set of conditions) simulation of a radio signal scattered from a rough surface. At present, four categories of mathematical models are used for these purposes, with each corresponding to a particular group of methods for solving the scattering problem. These methods include those based on a rigorous solution of the electrodynamic equations (methods of moments, finite elements, finite differences); approximate methods for solving electrodynamic problems (methods of small perturbations, tangent plane (Kirchhoff method), two-scale model); those of statistical equivalents; and phenomenological models. In the former two categories, the electromagnetic field is computed on a surface grid, whose step must be much smaller than the signal wavelength. This makes the simulation of a scattered radio signal effectively unrealizable for two-dimensional rough surfaces. The method of statistical equivalents is based on the calculation of the probabilistic characteristics of a scattered signal. Difficulties arising in this case narrow the possibilities of simulation, largely due to the impossibility of obtaining closed expressions for the required characteristics without significant simplifications. In addition, carrying out analytical calculations, the solution is restricted to the class of surfaces with the Gaussian distribution of roughness heights. Phenomenological models, although providing for a qualitative analysis of signal scattering from a rough surface, fail to take the physics of the scattering process into account.Aim. To create a simple and adequate model of a signal scattered from a two-dimensional rough surface using the tangent plane approximation of the boundary conditions (the Kirchhoff method), which can be realized on modern computers.Materials and methods. The proposed model of the scattered signal is based on its representation as a sum of signals reflected from stationary points on the surface, i.e., those points where the law of specular reflection of the incident electromagnetic wave is locally fulfilled.Results. The implementation of the model is reduced to determination of the positions of stationary points on a twodimensional rough surface. This problem is solved on modern computers quite simply without computations with complex numbers. It turns out that the number of stationary points, even with a large surface roughness, rarely exceeds two. This fact, along with the simplicity of finding the coordinates of stationary points, makes this model suitable for long series of statistical runs.Conclusion. The proposed model is easy to implement and can be used to study the accuracy of low-altitude target estimation, altimeters, Doppler and correlation speed and drift angle estimators.

A novel sea clutter suppression method based on SVD‐FRFT at low signal‐to‐clutter ratio

AbstractThe target and sea clutter Doppler domains frequently overlap due to the frequent passage of slow ship targets through the sea clutter zone. In this letter, a novel sea clutter suppression method is suggested as a solution to this issue, whose key is a novel singular value zeroing criterion guided by the search results of two‐dimensional spectral peaks. Verified by simulations, the method proposed can improve signal‐to‐clutter ratio (SCR) from −8 to 41 dB in the frequency domain and be more effective than the conventional SVD‐FRFT (a method for joint application of singular value decomposition and fractional Fourier transformer) and improved SVD‐FRFT.

Sea Surface Moving Target Detection Using a Modified Keystone Transform on Wideband Radar Data

The echoes collected by wideband radar systems provide abundant information on target scatterers, which is beneficial to target detection, classification, and recognition. However, as the radar range resolution increases, range cell migration (RCM) during the coherent integration (CI) period happens much easier, which may cause a degradation of target detection probability. In addition, due to the target’s orientation and structure relative to the radar, the distribution characteristics of the target scatterers in high-resolution range profiles (HRRPs) and the detection window length may vary from pulse to pulse, which may reduce the performance of traditional energy integration (EI) detectors. To solve those problems, moving range-spread target (RST) detection combining the modified keystone transform (MKT) and improved EI (IEI) is proposed in this paper. Firstly, based on waveform entropy minimization, MKT using hunter–prey optimization (HPO) is introduced to reduce the CI gain loss. The target Doppler ambiguity factor is estimated using such an effective optimization technique. Then, the IEI detector optimized by the adaptive threshold and detection window is utilized to achieve target detection, which minimizes the sensitivity of the traditional EI detector to the detection window length. The proposed method significantly improves the performance of moving RSTs in sea clutter without prior knowledge of the target Doppler ambiguity factor. Experiments are conducted by comparing the proposed method with other competing methods on both simulation data and real sea clutter data. The results demonstrate that the proposed method can obtain the CI more efficiently and has a higher detection probability.

Non-Linear Signal Processing Methods for UAV Detections from a Multi-Function X-Band Radar

This article develops the applicability of non-linear processing techniques such as Compressed Sensing (CS), Principal Component Analysis (PCA), Iterative Adaptive Approach (IAA), and Multiple-input-multiple-output (MIMO) for the purpose of enhanced UAV detections using portable radar systems. The combined scheme has many advantages and the potential for better detection and classification accuracy. Some of the benefits are discussed here with a phased array platform in mind, the novel portable phased array Radar (PWR) by Agile RF Systems (ARS), which offers quadrant outputs. CS and IAA both show promising results when applied to micro-Doppler processing of radar returns owing to the sparse nature of the target Doppler frequencies. This shows promise in reducing the dwell time and increases the rate at which a volume can be interrogated. Real-time processing of target information with iterative and non-linear solutions is possible now with the advent of GPU-based graphics processing hardware. Simulations show promising results.

Method of 2-D Range-Doppler Imaging for Plasma Wake Based on Range Walk Correction

When a hypersonic vehicle flies in the atmosphere, plasma sheath and trails are configured around the vehicle due to aerodynamic heating and ablation, which distort the radar echo signal and then influence the target range information and Doppler frequency extracted from the echoes. In order to examine the impact of plasma laminar wake on radar detection, this article is primarily aimed to establish a laminar wake echo model based on a linear frequency-modulated continuous wave (LFMCW) radar, which takes into account the reaction control system of laminar wake by employing the Born approximation solution and the characteristics of the inhomogeneous and nonunique velocity of the wake medium. An echo signal processing method is also developed, in which the stretch processing technique is combined with the keystone transform of the target range walk correction. Such a treatment not only reduces the sampling rate and bandwidth, but also eliminates the cross-range cells phenomenon caused by hypersonic; finally, the range-Doppler (RD) image of the wake is achieved by 2-D fast Fourier transform (2-D FFT) processing, and radar range and velocity measurement results in the presence of plasma are obtained.

Coherent Multi-Dwell Processing of Un-Synchronized Dwells for High Velocity Estimation and Super-Resolution in Radar

This paper describes a coherent multi-dwell processing (CMDP) method for high velocity estimation and super-resolution in search and track, while search (TWS) radar modes use an un-conventional signal processing algorithm that exploits multi-dwell transmissions. The existence of the multi-dwell waveform is necessary for visibility needs by un-folding the target’s velocity and range ambiguity and is proposed to be utilized for high velocity estimation and super-resolution. In this paper, the proposed scheme is shown to result in improved velocity estimation and doppler resolution performance for un-ambiguous targets in comparison to classical radar processing. The processing concept uses the same transmitted waveform (WF) and time duration without the need to increase the time on target (TOT) through sophisticated coherent concatenation of the received dwells with velocity compensation between the dwells. The phase compensation in receive mode is implemented for each target according to its characteristics, which means that target velocities are estimated in each dwell separately. The notable result of the CMDP is the linear doppler resolution improvement obtained with the given search resources and without knowing the target characteristics in advance or the dwell delay time. Other possible benefits of this process are the ability to achieve larger detection ranges and high-angle measurement precisions in search mode due to the higher signal-to-noise ratio (SNR) of the extended dwell and the ability to track more targets due to efficient time and resource management. An outstanding opportunity to exploit the CMDP is by combining missions in phased array (PA) radars, meeting the multi-objective needs of both high spatial scan rates for illuminating the target and high doppler estimation and resolution performance.

Information embedding in DFRC networks through chirp waveform diversity

We consider a dual function radar communications network and propose a waveform diversity-based approach to embed both target and scheduling data in the transmitter pulses. The scheduling data of radar signal bandwidth, carrier frequency, and waveform along with the information on target detection, range, and Doppler are shared among the network radar nodes without a need for designated communication links. Scheduling and target information are encoded to bits and transmitted over the radar coherent processing interval using up- and down-chirps. We derive exact expressions of probability of bit error and false alarm rate for sharing scheduling and target information data between two radar nodes. The effect of different parameters and data sharing on the radar performance is analyzed and validated by computer simulations. Using the cross-ambiguity function, we show the capability of the up- and down- chirp sequence transmission scheme in reducing range sidelobes compared to the conventional transmission of only the up- (or down-) chirps. To overcome the limited data rate associated with only using two chirp waveforms, we consider orthogonal chirp division multiplexing and derive its CAF. We demonstrate the suitability of OCDM for high data rate radar operations by examining the mainlobe width and sidelobe levels of the zero-Doppler and zero-delay cuts of the CAF.

RCFusion: Fusing 4-D Radar and Camera With Bird’s-Eye View Features for 3-D Object Detection

Camera and millimeter-wave (MMW) radar fusion is essential for accurate and robust autonomous driving systems. With the advancement of radar technology, next-generation high-resolution automotive radar, i.e., 4D radar, has emerged. In addition to the target range, azimuth, and Doppler velocity measurements of traditional radar, 4D radar provides elevation measurement to create a denser “point cloud.” In this study, we propose a camera and 4D radar fusion network called RCFusion, which achieves multimodal feature fusion under a unified bird’s-eye view (BEV) space to accomplish 3D object detection tasks. In the camera stream, multi-scale feature maps are obtained by the image backbone and feature pyramid network; they are then converted into orthographic feature maps by an orthographic feature transform. Next, enhanced and fine-grained image BEV features are obtained via a designed shared attention encoder. Meanwhile, in the 4D radar stream, a newly designed component named Radar PillarNet efficiently encodes the radar features to generate radar pseudo-images, which are fed into the point cloud backbone to create radar BEV features. An interactive attention module is proposed for the fusion stage, which outputs a valid fusion of the two-modal BEV features. Finally, a generic detection head predicts the object classes and locations. The proposed RCFusion is validated on the TJ4DRadSet and View-of-Delft datasets. The experimental results and analysis show that the proposed method can effectively fuse camera and 4D radar features to achieve robust detection performance.

Joint Target and Ionosphere Parameter Estimation in Over-the-Horizon Radar

Target localization, especially the estimation of target altitude, is a challenging task in over-the-horizon radar (OTHR) because of the narrow signal bandwidth as well as the complexity and uncertainty involved in the ionosphere conditions. This task becomes further complicated when the height of the ionosphere layer varies over time. Therefore, it is important to jointly estimate the instantaneous height of the ionosphere and the target altitude as well as other motion parameters. In this article, we achieve these objectives by analyzing the Doppler frequencies of the target local-multipath signals and the clutter. We reveal that the change of the ionosphere height can either enhance or deteriorate the performance of target parameter estimation depending on its direction of motion relative to the target’s motion profile. In addition, it is found that the received target and clutter Doppler signatures follow the chirp signal profile at the OTHR receiver. Based on these observations, we develop a general framework that achieves joint target and ionosphere parameter estimation and accounts for the velocity and accelerations of both target and ionosphere layer. Unlike existing time-frequency-based strategies for target localization and tracking in OTHR, where the Doppler signatures only directly determine target vertical velocity and the target altitude is estimated indirectly, the proposed model enables direct estimation of target altitude and ionosphere parameters. The parameter estimation problem in the proposed strategy is analytically derived and the effectiveness is verified using extensive simulation results.

Space-Time Adaptive Processing Clutter-Suppression Algorithm Based on Beam Reshaping for High-Frequency Surface Wave Radar

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam reshaping (BR). In this method, clutter components were estimated and maximally suppressed in the side lobe while ensuring that the main lobe remained intact. The results of the proposed algorithm were evaluated by comparison with those of standard STAP and sparse-representation STAP (SR-STAP). Among the tested algorithms, the proposed BR algorithm had the best suppression performance and the most accurate main-lobe peak response, thereby preserving the target angle and Doppler frequency information. The BR algorithm can assist with target detection and tracking despite a background with ionospheric clutter.

Measurement of target range and doppler shift by incorporating PDM-enabled FMCW-based photonic radar

Measurement is an essential part of modern science as well as of engineering, commerce, and daily life. As per recent reports of world health organization (WHO), most accidents occur due to human negligence and hence need of a system which can access the surrounding environment and reflect exact measurement of obstacle is a must to enable fully autonomous vehicle (AV). Photonic radars have materialized as promising candidate to realize AVs via distinguishing traffic pattern, navigation, lane detection and self-parking etc. In this work, we have developed coherent detection-based linear frequency modulated continuous wave (LFMCW) photonic radar to detect multiple targets multiplexed over a single free space channel via polarization division multiplexing (PDM). Also, the proposed system is tested for detection under zero visibility conditions under the impact of atmospheric turbulences up to 140 m of range distance. The reported results in terms of range frequency peaks and signal to noise ratio (SNR) indicate successful detection of both the targets at 4 GHz of bandwidth. The system is further tested for moving traffic condition with the target speed of 50 km/hr and 100 km/hr, respectively. Lastly, the range resolution of the proposed system is tested and reported as 15 cm at 1 GHz and 6.75 cm with 4 GHz of bandwidth at a distance of 100 m.