Radar Algorithm Research Articles

An Approach to High Frame Rate Radar Imaging Through Electronically Displaced-Phase-Center Antenna

This article presents a novel radar imaging approach through an electronically displaced-phase-center antenna (eDPCA). In this approach, a reflectarray is used to directly scan a beam across a parabolic reflector, where the antenna phase center is displaced electronically. The proposed eDPCA-based imaging radar can provide a very high frame rate and does not depend on the motion between the target and the radar platform. The feasibility of the eDPCA-based imaging radar is investigated and validated through a designed example eDPCA at 157 GHz and the simulation experiments for a point target and a complex target, respectively. The effects of practical impairments such as errors in phase centers and position-dependent fluctuations in antenna patterns on the imaging quality are examined. Furthermore, by applying the precompensation method suggested in this article, the imaging procedure could be performed by using classic synthetic aperture radar (SAR) algorithms.

Image Distortion Characterization due to Equivalent Monostatic Approximation in Near-Field Bistatic SAR Imaging

The $\omega $ – $k$ synthetic aperture radar (SAR) algorithm is a computationally efficient algorithm for near-field 3-D monostatic SAR imaging in nondestructive testing (NDT) applications. However, bistatic measurements are preferred in order to obtain high dynamic range, in particular when real-time imaging arrays are used. This article investigates the image distortion caused by using an equivalent monostatic imaging algorithm for bistatic measurements. Simulations and measurements at millimeter-wave frequencies in the Ka-band (26.5–40 GHz) are used to investigate the resultant image distortion. Furthermore, the image distortion is quantified through the root-mean-square (rms) error, which is calculated as a function of the bistatic transmitter–receiver separation distance, range, and noise power. Simulations and measurements are conducted for imaging using the raster scanning of a pair of antennas and for nonuniform imaging arrays. In addition, an approximate method for phase compensation is introduced to improve the image error from the monostatic approximation of bistatic measurement.

Inverse Radon transform-based large micromotion target detection algorithm in synthetic aperture radar

When the micromotion (MM) target has a large MM amplitude, the bandwidth of the synthetic aperture radar (SAR) azimuth echo will be greater than the pulse repetition frequency (PRF) of SAR. According to Nyquist sampling theorem, spectrum aliasing will occur at this time, and conventional MM target detection and estimation algorithms may lose effect. To solve the problem of large MM target detection with echo bandwidth greater than the PRF, an inverse Radon transform (IRT)-based large MM target detection algorithm is proposed. The study found that the time-frequency (TF) spectrum of large MM target azimuth echo is usually a folded sinusoidal curve. Then we spliced enough identical TF spectrums to restore an intact sinusoidal curve. The intact sinusoidal curve can be mapped into a peak in the parameter space using IRT. Hence, the algorithm can detect large MM target and estimate its parameters accurately. Data processing results prove the effectiveness of the algorithm. At the same time, the performance analysis proves that the operation speed and antinoise performance of the algorithm are better than that of the Hough transform algorithm.

Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review.

Attention to the natural environment is equivalent to observing the space in which we live. Plant roots, which are important organs of plants, require our close attention. The method of detecting root system without damaging plants has gradually become mainstream. At the same time, machine learning has been achieving good results in recent years; it has helped develop many tools to help us detect the underground environment of plants. Therefore, this article will introduce some existing content related to root detection technology and machine detection algorithms for root detection, proving that machine learning root detection technology has good recognition capabilities.

An FPGA-Based Signal Processor for FMCW Doppler Radar and Spectroscopy

We report on the design and development of a field programmable gate array (FPGA)-based radar and spectrometer signal processor. It leverages an efficient implementation of a fixed-point frequency-modulated continuous-wave (FMCW) Doppler radar algorithm and has been demonstrated in a short-range W-band radar system developed for mapping plume and jet phenomena in the solar system. The signal processor also performs Fourier transform-based spectroscopy with a resolution, bandwidth, and averaging time appropriate for millimeter- and submillimeter-wave passive remote sensing in space. With a dc power consumption totaling only 3 W, the processor helps make the Doppler radar and spectrometer instrument well-suited for spaceborne, airborne, or portable platforms. Here, we describe the fixed-point implementation of the radar and spectrometer algorithm, emphasizing its computational efficiency for low resource utilization, and provide examples of Doppler radar sensing of rain and spectrometer sensitivity to hot and cold blackbody loads.

Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa

This study provides a comparative analysis of two Sentinel-1 and one Sentinel-2 burned area (BA) detection and mapping algorithms over 10 test sites (100 × 100 km) in tropical and sub-tropical Africa. Depending on the site, the burned area was mapped at different time points during the 2015–2016 fire seasons. The algorithms relied on diverse burned area (BA) mapping strategies regarding the data used (i.e., surface reflectance, backscatter coefficient, interferometric coherence) and the detection method. Algorithm performance was compared by evaluating the detected BA agreement with reference fire perimeters independently derived from medium resolution optical imagery (i.e., Landsat 8, Sentinel-2). The commission (CE) and omission errors (OE), as well as the Dice coefficient (DC) for burned pixels, were compared. The mean OE and CE were 33% and 31% for the optical-based Sentinel-2 time-series algorithm and increased to 66% and 36%, respectively, for the radar backscatter coefficient-based algorithm. For the coherence based radar algorithm, OE and CE reached 72% and 57%, respectively. When considering all tiles, the optical-based algorithm provided a significant increase in agreement over the Synthetic Aperture Radar (SAR) based algorithms that might have been boosted by the use of optical datasets when generating the reference fire perimeters. The analysis suggested that optical-based algorithms provide for a significant increase in accuracy over the radar-based algorithms. However, in regions with persistent cloud cover, the radar sensors may provide a complementary data source for wall to wall BA detection.

Distribution of hydrometeors in monsoonal clouds over the South American continent during the austral summer monsoon: GPM observations

ABSTRACTThe Global precipitation measurement (GPM) was launched in February 2014 and provides the three-dimensional attenuated corrected radar reflectivity factor (Ze) along with the raindrop size distribution (DSD) parameters. The DSD parameters consist of the mass-weighted hydrometeors size (Dm in mm) and normalized hydrometeors concentration (Nw in mm−1 m−3). The present study investigates the vertical and spatial distribution of hydrometeors in intense convective clouds that form over South America (SA) during Austral summer monsoon seasons. We defined Cumulonimbus towers (CbTs) and intense convective cells based on 8 km (ICC8s) and 3 km (ICC3s), using vertical profile of radar reflectivity algorithm and then their properties are explored over eight selected areas over SA. CbT is defined by using the Ze≥20 dBZ at 12 km with base height less than 3 km altitude. The ICCs are defined by using the Ze thresholds at 8 and 3 km altitude, and Ze threshold value belongs to the top 5% of the Ze value at the reference height. Subtropical areas including Sierra de Cordoba (SDC) and La Plata basin (LPB) consist of a higher frequency of CbTs and ICC8s, whereas ICC3 is nearly uniformly distributed over the SA continent and the Atlantic Ocean (AO). Eastern foothills of the Andes mountain also consist of a higher frequency of CbTs and ICC8s. Irrespective of the height and Ze thresholds used in the present study, the SDC and LPB consist of the most intense convective clouds with higher echo top altitude, and similar to that observed over Western Himalaya foothills over South Asia. Land and ocean differences are visible in the cloud cells based on the 3 km reference height, which is well below the freezing level. CbTs and ICC8s do not show the land and ocean differences as in both the cases the AO has comparable Ze in average vertical profiles compared to the land areas, but in ICC3, the AO has the weakest cloud cells with the least cloud top height. The absolute slope of Ze in mixed-phase altitude is highest in ICC3 and reflect the local precipitation fallout in mixed-phase regions and suggested that fewer hydrometeors of larger sized are lifted in the upper atmosphere. The least slope over SDC and Central Foothills indicates that higher sized of hydrometeors are lifted in the upper atmosphere in the monsoonal clouds compared to other parts of SA continent. The DSD parameters indicate that in general, intense cloud cells consist of large-sized hydrometeors although their concentration is low. The spatial averages of the DSD parameters also indicate larger sized of hydrometeors exist on average for all types of clouds at 3 and 8 km in southeastern areas of SA including SDC, LPB, and Brazilian highlands. The single meteorological feature is not responsible for the intense and deep convection, but the combined meteorological variables are responsible for producing the intense and deeper convection.

A sensitivity study on the response of convection initiation to in situ soil moisture in the central United States

Soil moisture can affect precipitation, however there is significant debate about the sign and strength of land–atmosphere coupling. This may be due to the differences regarding the selection of data sources, the presence of atmospheric persistence, and the spatial and temporal scales of the analysis. This study is a sensitivity analysis that quantifies how each of these factors influences land–atmosphere coupling in the central United States from 2005 to 2007. Four different sources of soil moisture are used to represent soil moisture: in situ measurements and three satellite products. The Thunderstorm Observation by Radar (ThOR) algorithm is used to identify convective events. The results show that warm-season afternoon convection initiates preferentially over dry soil, if in situ measurements are used. However, when satellite data are used the sign and strength of the soil moisture-precipitation coupling varies depending on which satellite dataset is used. This demonstrates that evaluations of land–atmosphere coupling strength are sensitive to the source of soil moisture. Our results also show that accounting for atmospheric persistence tends to weaken apparent land–atmosphere coupling. Changing the spatial scale of the analysis can also influence the sign and coupling strength. Analyses that are performed at a coarser spatial resolution tend to indicate a wet preference because they do not capture the local negative feedbacks. This study demonstrates that contradictions in the literature regarding the sign and strength of soil moisture-precipitation feedbacks can be partly attributed to differences in datasets, methods and scale of the analysis.

Joint Scan Rate and Emitter Location Estimation Algorithm in SBR System Based on AOA and DTOST Measurements

A joint scan rate and emitter location estimation algorithm in scan-based radar (SBR) system based on the angle-of-arrival (AOA) and the difference time of scan time (DTOST) measurements is proposed in this paper. SBR localization is a kind of passive geolocation technique for stationary pulsed radars. To date, a huge number of passive localization techniques using AOA, DTOST or other measurements for SBR have been developed assuming prior knowledge of the scan rate. However, it is usually hard to obtain the accurate scan velocity in most of practical civilian and military applications. In this paper, by using the linear correlation between AOA and DTOST, the maximum likelihood (ML) estimation algorithm based on linear constraints is given to obtain the scan rate of SBR. And the new AOA information after fusion is acquired on this basis. Then the weighted least square (WLS) method is used for SBR localization and the Cramer-Rao lower bound (CRLB) is derived for SBR location estimation. A detailed analysis of scan rate and emitter location estimation process is given and the effectiveness of the proposed localization algorithm is illustrated with computer simulations.

Robust Cognitive Radar Tracking Based on Adaptive Unscented Kalman Filter in Uncertain Environments

This article presents an approach for cognitive radar to track the target state despite unknown statistics or sudden changes in noise exposed to uncertain environments. Given the prior knowledge or accurate estimation of the noise, cognitive radar can adaptively adjust the waveform in the transmitter by perceiving the environment. However, the measurement noise usually contains a priori unknown parameters which are difficult to be estimated, or the process noise without empirical model might be improperly configured. Besides, they may change abruptly or time-varying in practice. These issues are prone to occur in cognitive radar, leading to performance decline or failure in tracking ultimately. To alleviate this problem, a robust cognitive radar tracking method based on adaptive unscented Kalman filter (AUKF) is proposed in this article. Specifically, UKF is used to derive the cognitive mathematical model and estimate the states in nonlinear systems. Moreover, a robust adaptive mechanism is designed in the cognitive framework, which is independent of prior information. When the mismatch between the noise in the model and the noise in the environment emerges, the noise covariance can be corrected adaptively. To verify the efficiency of the scheme, maneuvering target tracking experiments are carried out in three uncertain noise scenarios. Simulation results show that the scheme outperforms the existing adaptive UKF and cognitive radar algorithms in terms of intelligence and robustness.

Low-Complexity Joint Range and Doppler FMCW Radar Algorithm Based on Number of Targets.

A low-complexity joint range and Doppler frequency-modulated continuous wave (FMCW) radar algorithm based on the number of targets is proposed in this paper. This paper introduces two low-complexity FMCW radar algorithms, that is, region of interest (ROI)-based and partial discrete Fourier transform (DFT)-based algorithms. We find the low-complexity condition of each algorithm by analyzing the complexity of these algorithms. From this analysis, it is found that the number of targets is an important factor in determining complexity. Based on this result, the proposed algorithm selects a low-complexity algorithm between two algorithms depending the estimated number of targets and thus achieves lower complexity compared two low-complexity algorithms introduced. The experimental results using real FMCW radar systems show that the proposed algorithm works well in a real environment. Moreover, central process unit time and count of float pointing are shown as a measure of complexity.

Development and Calibration of a Low-Cost Radar Testbed Based on the Universal Software Radio Peripheral

For a cognitive radar algorithm to be useful, it must run on radar hardware that is flexible. Typically, this has been viewed as requiring a software-defined radio. With the emergence of cheap, digital transceiver systems, such as the Universal Software Radio Peripheral (USRP) from Ettus, these systems are cheaper than ever to produce. This article reports on the development of a USRP-based radar system. Calibration testing for radar cross section is undertaken. The phase noise is characterized as having a standard deviation of 0.02 radians. Detections of commercial aircraft at ranges up to 5.5 km are demonstrated. An adaptive update interval tracking experiment, using the fully adaptive radar framework for cognitive radar, is reported on. In this experiment, a small unmanned aerial vehicle is detected at ranges of up to 300 m. Using the radar equation with a signal loss proportional to R−4, the interpolated maximum detection range is approximately 600 m.

A NEW STRATEGY FOR PHASE UNWRAPPING IN INSAR TIME SERIES OVER AREAS WITH HIGH DEFORMATION RATE: CASE STUDY ON THE SOUTHERN TEHRAN SUBSIDENCE

Abstract. The primary step in all timeseries interferometric synthetic aperture radar (T-InSAR) algorithms is the phase unwrapping step to resolve the inherent cycle ambiguities of interferometric phases. In areas with a high spatio-temporal deformation gradient, phase unwrapping fails due to the aliasing problem, and so it can result in an underestimation of deformation signal. One way to handle this problem is to use the so called Small-Baseline Subset (SBAS) algorithms; in these algorithms, by using only small-baseline interferograms – hence interferograms with small deformation gradients – the chance of unwrapping error gets reduced. However, due to more number of the used interferograms, SBAS method is computationally more expensive and more time-consuming compared to algorithms that exploit Single-Master (SM) stacks. Moreover, the existence of sufficiently small temporal baseline interferograms is not guaranteed in all SAR stacks. In this paper, we propose a new method to take advantage of short temporal baseline interferograms but effectively using SM approach. We treat the phase unwrapping step as a Bayesian estimation problem while the prior information, required by the Bayesian estimator, is extracted from few short coherent interferograms that are unwrapped separately. Results from the proposed approach and a case study over the southwest of Tehran, with a high subsidence rate (reaching to 25 cm/year), demonstrates that utilizing the proposed method overcomes the aliasing problem and produces the results equal to the conventional SBAS results, while the proposed method is computationally much more efficient than SBAS.

Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors

This work proposes a novel Ground Penetrating Radar (GPR) system to detect landmines and Improvised Explosive Devices (IEDs). The system, which was numerically evaluated, is composed of a transmitter placed on a vehicle and looking forward and a receiver mounted on a drone and looking downwards. This combination offers both a good penetration and a high resolution, enabling the detection of non-metallic targets and mitigating the clutter at the air–soil interface. First, a fast ray tracing simulator was developed to find proper configurations of the system. Then, these configurations were validated using a full wave simulator, considering a flat and a rough surface. All simulations were post-processed using a fast and accurate Synthetic Aperture Radar (SAR) algorithm that takes into account the constitutive parameters of the soil. The SAR images for all configurations were compared, concluding that the proposed contribution greatly improves the target detection and the surface clutter reduction over conventional forward-looking GPR systems.

ISAR imaging resource‐scheduling algorithm in network radar based on information fusion

Aiming at the application requirements of three-dimensional (3-D) fusion imaging of network radar, this study proposes an ISAR imaging resource-scheduling algorithm for network radar. Based on the feature recognition of each radar to each target, a projection-based method is first used to fuse the 3-D information of targets. Then according to the fused information, the demand of time resources for each radar is analysed. The radars selected for each target imaging are determined. Finally, the scheduling model of the imaging task for network radar is established, and time resources for each radar are randomly allocated. The simulation results show that the scheduling success rate of the proposed algorithm is two times on average than that of the traditional algorithm under the situation when the resources are near-saturated for both algorithms, which can improve the efficiency of network radar.

Fast Fourier-Based Implementation of Synthetic Aperture Radar Algorithm for Multistatic Imaging System

Multistatic millimeter-wave imaging structures are superior to their monostatic counterparts for imaging natural objects of sudden profile variations. Multistatic image reconstruction is conventionally performed via synthetic aperture radar (SAR)-based methods which, in spite of their high accuracy, are computationally burdensome. On the other side, the Fourier-based image reconstruction in multistatic systems also faces few challenges including multidimensional interpolation, a plane-wave approximation of spherical waves, and $k$ -space partitioning. This paper presents a fast implementation of the SAR-based image reconstruction method in case of 1-D and 2-D multistatic arrays. The proposed implementation is Fourier based across the uniform direction in $k$ -space and SAR based along the nonuniform direction. Both methods are fully parallelizable and are arranged into a vector format to maximize memory usage and minimize the computational time. The extensive validation and benchmarking have been performed with both simulation and experimental data, which proves a $256\times $ improvement in the reconstruction time for the worst case scenario compared to that of in SAR-based methods with the same image quality. Furthermore, the reconstructed image performance is around $10\times $ better than the most recent Fourier-based reconstruction method, in terms of root-mean-square error metric, and with $28\times $ less computational time.

Grating-Lobe Clutter Suppression in Uniform Subarray for Airborne Radar STAP

This paper aims to address grating-lobe clutter suppression in a uniform subarray (US) for the airborne radar system. Due to the sparsity at the subarray level and different scanning angles between the inner subarray and the subarray, grating lobes are produced. The grating-lobe clutter then enters the main lobe, defined as main grating-lobe clutter (MGLC). When the target falls into the MGLC with the same Doppler frequency, the clutter cannot be effectively suppressed by the conventional space-time adaptive processing (STAP) algorithm. Hence, we design a transmit beamforming (TB)-STAP algorithm based on a modified phased array (MPA) radar system, where TB suppresses the MGLC, while STAP suppresses the main-lobe and sidelobe clutter; the goal is to maximize the output signal-to-clutter-plus-noise ratio (SCNR). The optimal solution of TB-STAP can be obtained through several iterations, and in each iterative algorithm, the TB is a quadratically constrained quadratic problem (QCQP) solved via the semidefinite relaxation (SDR) technique, and the STAP can obtain a closed solution by the minimum variance distortionless response (MVDR) algorithm. The simulation results show that compared with traditional STAP in PA and multiple-input multiple-output (MIMO) radars, the proposed algorithm in MPA radar greatly improves the output SCNR.

Design and implementation of MIMO radar real‐time imaging system based on multicore DSP

Multiple-input multiple-output (MIMO) imaging radar is a new kind of new imaging radar in recent years. It can achieve faster data acquisition speed than the traditional ground-based SAR due to the ability to get a large number of multi-channel data in a short period of time. However, traditional single-core digital signal processors (DSPs) have limited space and ability to deal with such large amount of data. To solve this problem, a real-time image processing software architecture for MIMO imaging radar algorithm is designed and carried out based on TMS320C6678 DSP of Texas Instruments (TI), which is the highest-performance fixed/floating-point DSP based on TI's KeyStone multicore architecture. The efficient imaging algorithms mapping from Matlab to DSP is realised, with efficient task allocation, resource reuse of echo data, and processing variable, and using the method of multicore parallel processing.

A Microwave-Induced Thermoacoustic Imaging System With Non-Contact Ultrasound Detection.

Portable and easy-to-use imaging systems are in high demand for medical, security screening, nondestructive testing, and sensing applications. We present a new microwave-induced thermoacoustic imaging system with non-contact, airborne ultrasound (US) detection. In this system, a 2.7 GHz microwave excitation causes differential heating at interfaces with dielectric contrast, and the resulting US signal via the thermoacoustic effect travels out of the sample to the detector in air at a standoff. The 65 dB interface loss due to the impedance mismatch at the air-sample boundary is overcome with high-sensitivity capacitive micromachined ultrasonic transducers with minimum detectable pressures (MDPs) as low as 278 μ Pa rms and we explore two different designs-one operating at a center frequency of 71 kHz and another at a center frequency of 910 kHz. We further demonstrate that the air-sample interface presents a tradeoff with the advantage of improved resolution, as the change in wave velocity at the interface creates a strong focusing effect alongside the attenuation, resulting in axial resolutions more than 10× smaller than that predicted by the traditional speed/bandwidth limit. A piecewise synthetic aperture radar (SAR) algorithm modified for US imaging and enhanced with signal processing techniques is used for image reconstruction, resulting in mm-scale lateral and axial image resolution. Finally, measurements are conducted to verify simulations and demonstrate successful system performance.

Hybrid particle swarm optimization algorithm based on entropy theory for solving DAR scheduling problem

An efficient task-scheduling algorithm in the Digital Array Radar (DAR) is essential to ensure that it can handle a large number of requested tasks simultaneously. As a solution to this problem, in this paper, we propose an optimization model for scheduling DAR tasks using a hybrid approach. The optimization model considers the internal task structure and the DAR task-scheduling characteristic. The hybrid approach integrates a particle swarm optimization algorithm with a genetic algorithm and a heuristic task-interleaving algorithm. We introduce the chaos theory to optimize initialized particles and use entropy theory to indicate the diversity of particles and adaptively adjust the inertia weight, the crossover probability, and the mutation probability Then, we improve both the efficiency and global exploration ability of the hybrid algorithm. In the framework of the swarm exploration algorithm, we include a heuristic task-interleaving scheduling algorithm, which not only utilizes the wait interval to transmit or receive subtasks, but also overlaps the receive intervals of different tasks. In a large-scale simulation, we demonstrate that the proposed algorithm is more robust and effective than existing algorithms.