Radar Tracking Research Articles

Short communication: Potential of Sentinel-1 interferometric synthetic aperture radar (InSAR) and offset tracking in monitoring post-cyclonic landslide activities on Réunion

Abstract. This study examines the results of an interferometric synthetic aperture radar (InSAR) and SAR offset tracking (OT) study in Cirque de Salazie (CdS), Réunion Island, France, within the context of the RENOVRISK project, a multidisciplinary programme to study the cyclonic risks in the south-western Indian Ocean. Despite numerous landslides in this territory, CdS is one of the more densely populated areas on Réunion Island. One of the aims of the project is to assess whether Sentinel-1 SAR methods can be used to measure landslide motion and/or accelerations due to post-cyclonic activity in CdS. We concentrate on the post-2017 cyclonic activity. We use the Copernicus Sentinel-1 data, acquired between 30 October 2017 and 6 November 2018. Sentinel-1 is a C-band SAR, and its signal can be severely affected by the presence of changing vegetation between two SAR acquisitions, particularly in CdS, where the vegetation canopy is well developed. This is why C-band radars such as the ones on board RADARSAT or Envisat, characterized by low acquisition frequency (24 and 36 d, respectively), could not be routinely used in CdS to measure landslide motion with InSAR in the past. In this study, we use InSAR and OT techniques applied to Sentinel-1 SAR. We find that C-band SAR on board Sentinel-1 can be used to monitor landslide motion in densely vegetated areas, thanks to its high acquisition frequency (12 d). OT stacking reveals a useful complement to InSAR, especially in mapping fast moving areas. In particular, we can highlight ground motion in the Hell-Bourg, Ilet à Vidot, Grand-Ilet, Camp Pierrot, and Le Bélier landslides.

Linear sequential filtering based on the debiased measurement matrix of the converted pseudo measurement for radar target tracking

For radar target tracking with nonlinear position and range rate measurements, a linear sequential filtering method based on the debiased measurement matrix of the converted pseudo measurement (DMM-LSF) is proposed. A novel and simple linear measurement matrix is constructed based on the relationship between the pseudo measurement and target's state vector. To estimate the involved unknown parameters in the measurement matrix, the filtering results from the linear position filter are utilized in the sequential linear filter, where a totally linear sequential filtering framework is formed. The measurement matrix estimation error is fully considered, which is incorporated into original converted pseudo measurement error and a new synthetical measurement error is formed. The statistic characteristics of the synthetical measurement error are deduced, resulting a debiased measurement matrix. Simulation results demonstrate the effectiveness of the proposed DMM-LSF algorithm. Compared with existing algorithms, the proposed one can achieve the best tracking performance for non-maneuvering and maneuvering target tracking in different scenes.

Marine Object Segmentation and Tracking by Learning Marine Radar Images for Autonomous Surface Vehicles

This paper proposes a radar image segmentation and tracking method by learning radar images for autonomous surface vehicles. To identify marine objects from radar images, we propose a deep neural network named the dual path squeeze and excitation network (DPSE-Net). By learning the radar images, the proposed DPSE-Net is designed to segment every pixel of the radar images into four classes: marine objects, land, noise, and background. The proposed DPSE-Net shows the best performance in radar image segmentation while operating in real-time, compared to state-of-the-art real-time image segmentation network models. In addition, we design a real-time moving object tracking algorithm for estimating the position and velocity of marine objects based on deep simple online real-time tracking with a deep association metric (DeepSORT), a widely used tracking algorithm. The existing DeepSORT algorithm uses the intersection over union (IoU) metric and a deep appearance descriptor for data association, but since they are not suitable for radar images, successive tracking is difficult. To solve this problem, a new data association metric suitable for radar images is proposed. The field tests in ocean environments confirm that the proposed method performs better in marine object segmentation and tracking.

Varied Histories of Outlier Polar Ice Deposits on Mars

AbstractMartian ice holds an important key to interpreting Mars' past climate, but much is still unknown regarding the distribution and properties of Mars' ice deposits. Previous surveys identified craters that contain “outlying” deposits of ice separate from, but nearby, the north and south polar layered deposits (NPLD and SPLD). There are many differences between the characteristics of the NPLD and SPLD, which may or may not be shared by these outlying deposits, and may provide clues to the climate conditions under which ice in the polar regions formed. Ground penetrating radar is one of the crucial datasets for understanding Martian ice, as it can probe the subsurface and place constraints on the properties of buried materials. We have analyzed 517 SHARAD radar tracks across 24 ice deposits housed within craters, including quantifying surface reflectivity and identifying the presence of subsurface reflectors. After examining the subsurface radar observations, we determined that the northern outlier deposits share many common characteristics with the NPLD, and thus may have been emplaced concurrently or at least under similar environmental conditions. The southern outlying crater deposits exhibit a variety of subsurface characteristics, and likely represent two or more populations of ice‐rich deposits that may have differing emplacement histories.

Development of a Chaff Dispense Program for Target Tracking Radar Deception

This study aims to develop an appropriate chaff dispensing program to deceive the target tracking radar (TTR) effectively. Chaff is a countermeasure commonly used by fighter aircraft to deceive TTR. However, there has been a lack of methodology for calculating chaff dispense programs that take into account the specific characteristics of the fighter, chaff, and TTR. This study proposes a methodology that considers these variables to calculate chaff dispense programs and addresses this gap. The proposed method is demonstrated through TESS engagement, which shows its effectiveness in various engagement situations.

State estimation of radar tracking system using a robust adaptive unscented Kalman filter

State estimation of radar tracking system using a robust adaptive unscented Kalman filter

InSARTrac Field Tests—Combining Computer Vision and Terrestrial InSAR for 3D Displacement Monitoring

InSARTrac is an innovative method for 3D displacement monitoring that combines terrestrial interferometric synthetic aperture radar (InSAR) and computer vision-based feature tracking. The 3D measurements obtained are considered far superior to 1D or 2D data and facilitate evaluations concerning the mechanisms controlling kinematics. This study presents the results of InSARTrac measurements at the Mölltal Glacier in Carinthia, Austria. The duration of glacier monitoring was four weeks and involved two instrument setup positions to obtain comparative measurements of supraglacial rock debris from different angles without utilizing retroreflectors. The mean displacement rate of the resultant vector is 22 mm/day and includes ~11 mm/day in the downgradient ice surface direction and 6 to 18 mm/day vertically downward. Additionally, the entire glacier surface was measured three times using a LIDAR-equipped UAV, revealing mean vertical displacements of 16 mm/day. The measurements indicate an InSARTrac accuracy of 4.2 ppm, which is 27% lower than in the initial controlled tests utilizing retroreflectors. The field test demonstrates the capability of InSARTrac to provide meaningful 3D displacement measurements of supraglacial rock debris. The material monitored has texture and reflectivity similar to certain classes of landslides, rock glaciers, and other alpine processes, indicating that InSARTrac has promising applications for monitoring a variety of geologic phenomena.

Application of kalman filter for radar target tracking.

In this paper, the principles of recursive tracking and prediction filters are used to track a single maneuvering aerial target and implemented using instantaneous noisy measurements. Kalman filter is used to estimate (predict) the system state (bearing, range, target course, and speed) as they cannot be measured directly from measurements. Furthermore, after mathematical formulation of the equations for the Kalman filter fitting the application, it was then applied and evaluated. Monte Carlo Simulation technique was applied to quantify the results and the simulation results are obtained using a python program.

Radar and AIS Track Association Integrated Track and Scene Features Through Deep Learning

Radar and AIS Track Association Integrated Track and Scene Features Through Deep Learning

МЕТОДИКА СИНТЕЗА АЛГОРИТМА ПЕЛЕНГАЦИИ ПОСТАНОВЩИКА КОМБИНИРОВАННОЙ ИМИТИРУЮЩЕЙ И МАСКИРУЮЩЕЙ ПОМЕХИ САМОПРИКРЫТИЯ В МОНОИМПУЛЬСНЫХ ИМПУЛЬСНО-ДОПЛЕРОВСКИХ РАДИОЛОКАТОРАХ СОПРОВОЖДЕНИЯ

For meters of angular coordinates of pulse-Doppler tracking radar of escort that implement the method of amplitude instantaneous comparison of radio signals, a methodology for the synthesis of the algorithm of the diversion of the combined imitating and masking jamming (CIM) has been developed. The calculation and comparative analysis of errors of evaluating the angular coordinates of the air target of the CIM-jamming when choosing as sources of veil information: retaliatory noise interference, which leads interference and the reflected air target signal.

Deep Learning Derived Object Detection and Tracking Technology Based on Sensor Fusion of Millimeter-Wave Radar/Video and Its Application on Embedded Systems

This paper proposes a deep learning-based mmWave radar and RGB camera sensor early fusion method for object detection and tracking and its embedded system realization for ADAS applications. The proposed system can be used not only in ADAS systems but also to be applied to smart Road Side Units (RSU) in transportation systems to monitor real-time traffic flow and warn road users of probable dangerous situations. As the signals of mmWave radar are less affected by bad weather and lighting such as cloudy, sunny, snowy, night-light, and rainy days, it can work efficiently in both normal and adverse conditions. Compared to using an RGB camera alone for object detection and tracking, the early fusion of the mmWave radar and RGB camera technology can make up for the poor performance of the RGB camera when it fails due to bad weather and/or lighting conditions. The proposed method combines the features of radar and RGB cameras and directly outputs the results from an end-to-end trained deep neural network. Additionally, the complexity of the overall system is also reduced such that the proposed method can be implemented on PCs as well as on embedded systems like NVIDIA Jetson Xavier at 17.39 fps.

Fusion of Inverse Synthetic Aperture Radar and Camera Images for Automotive Target Tracking

Automotive targets undergoing turns in road junctions offer large synthetic apertures over short dwell times to automotive radars that can be exploited for obtaining fine cross-range resolution. Likewise, the wide bandwidths of the automotive radar signal yield high-range resolution profiles. Together, they are exploited for generating inverse synthetic aperture radar (ISAR) images that offer rich information regarding the target vehicle's size, shape, and trajectory which is useful for object recognition and classification. However, a key requirement for ISAR is translation motion compensation and estimation of the turning velocity of the target. State-of-the-art algorithms for motion compensation trade-off between computational complexity and accuracy. An alternative low complexity method is to use an additional sensor for tracking the target motion. In this work, we propose to exploit computer vision algorithms to identify the radar target object in the sensor field-of-view (FoV) with high accuracy. Further, we propose to track the target vehicle's motion through fusion of vision and radar data. Vision data facilitates the accurate estimation of the lateral position of the target and its lateral velocity which complements the radar's capability of accurate estimation of range and radial velocity. Through simulations and experimental evaluations with a monocular camera and Texas Instrument's millimeter wave radar, we demonstrate the effectiveness of sensor fusion for accurate target tracking for translational motion compensation and generation of high quality ISAR images.

Анализ показателей качества устройства обнаружения и оценивания параметров протяженной по дальности коррелированной ответной шумовой помехи и в импульсно-доплеровских РЛС сопровождения

The device structure was designed for detecting and measuring the parameters of correlated cover-pulse jamming (CPJ) as a part of combined simulating and masking jamming (CIM jamming). It was made for pulse-Doppler tracking radar (PD radar). For the designed device the detection specification was calculated as well as the dependence of the estimation error of the CPJ parameters on the CPJ-to-noise ratio for various CPJ parameters.

Predictors of Human Efficiency in Radar Detection Tasks

Aegis operators simultaneously locate and monitor the activity of several hostile targets, intervening and alerting their team when appropriate. Utilizing the Aegis Combat System, operators find, track, and respond to dynamic targets on a radar screen. The demand that operators undergo is often high, inevitably causing strain on cognitive functions and detriments to performance. We applied model-based measures, Cost and Multitasking Throughput, to quantify the influence of external factors on processing efficiency in radar task(s). We captured the influence of three experimental manipulations, each of three levels, on human efficiency to track the location of hostiles and/or detect brief radar interference. We collected participants’ performance to complete a multiple object tracking (MOT) task and an electronic attack detection task (EA) using a radar display. A factorial manipulation of conditions comprised changes to task(s) (EA, MOT, or both), the number of targets to track (2, 4, or 6) and the presence or absence of distractors, deemed 'friendlies' (between 500-1000 total tracks). Our novel individual- and model-based approach provided quantitative estimates of human efficiency. We compared the observed variation in efficiency among predictors including target quantity, visual load, and the presence of one or two interrelated tasks. Through quantifying the relationship of these variables to radar detection tasks, we discuss implications of our findings and provide a framework to examine how system designers may develop tools to alleviate observed cognitive demands and/or counter potential threats of electronic attacks in radar detection and tracking tasks.

Joint allocation of transmit power and signal bandwidth for distributed cognitive tracking radar network using cooperative game

The radar network can achieve high target tracking accuracy by felicitously distributing the transmitting resources of each node. However, the influence of multiple transmitting resources of each radar node on target tracking accuracy is coupled with each other. We propose a joint allocation strategy of transmit power and signal bandwidth based on the cooperative game for the distributed cognitive tracking radar network. We employ the determinant of the Fisher information matrix to evaluate the target tracking accuracy and analyze the influence of each radar node's transmit power and signal bandwidth on target tracking accuracy. The joint allocation problem of transmit power and signal bandwidth is modeled as a cooperative game. We use the algorithm combining the cyclic minimization with the Shapley value to solve the cooperative game model. The weighted graph is used to simplify the calculation of the Shapley value. Simulation experiments are carried out in single-target tracking and multi-target tracking scenes. The results demonstrate that the proposed method can approach the centralized resource allocation method on the target tracking performance with a lower computational load.

Bistatic Radar Tracking With Significantly Improved Bistatic Range Accuracy

Tracking with bistatic radar measurements is a challenging problem due to the nonlinear relationship between the radar measurements and the Cartesian coordinates, especially for long distances. This nonlinearity leads, for 3-D bistatic radar, to a nonellipsoidal measurement uncertainty region in Cartesian coordinates, similar to a thin contact lens, that causes consistency problems for a tracking filter. The recently developed conversion of the bistatic radar measurements into Cartesian coordinates enables to maintain consistency by using a converted measurement Kalman filter. However, such a filter suffers from a poor bistatic range accuracy, limiting the multitarget tracking performance in a dense environment of targets or clutter. A solution is suggested by using a filter in the measurement coordinate system and converting its results into Cartesian coordinates. Consistent Cartesian estimation is obtained together with significantly improved filtered bistatic range accuracy. The latter is important in data association, resulting in a measurement-to-track association gates that are over an order of magnitude smaller compared to the Cartesian filters. It is shown that if the data association is performed in sensor coordinates, the (Cartesian) volume of the contact lens association region is over 20 times smaller than if it is performed in Cartesian coordinates because the ellipsoid in the latter case is extremely conservative.

Narrowband Passive RF Tags for Frequency-Selective Harmonic Doppler Radar Tracking

A design for frequency-selective radio frequency (RF) harmonic tags for harmonic Doppler radar tracking is presented. Harmonic RF tags receive an incident microwave signal and generate a retransmitted signal at a harmonic of the incident frequency, typically the second harmonic. Since environmental backscatter occurs at the fundamental frequency, the second harmonic can be easier to detect. Typical harmonic tags operate over a reasonably wide fractional bandwidth, and thus to detect multiple tags within the same field of view, additional circuitry must generally be added to the tag to differentiate the responses. We propose a different approach based on frequency selectivity implemented by the tag antenna. Based on narrowband split-ring printed antennas, the tags accept incident signals over a specified narrow fractional bandwidth, after which the second harmonic is generated by a diode and retransmitted with a standard dipole antenna. The narrowband filtering occurs at the fundamental frequency antenna, and thus with tags designed at adjacent narrowband frequency channels, no additional circuitry is necessary to differentiate multiple tags. Fabricated tags achieved fractional bandwidths below 0.5% at carrier frequencies below 2.5 GHz while maintaining gains above 2 dBi. The construction of the tag is simple and low-cost, requiring only a single component (a diode) on a printed antenna structure. We demonstrate the feasibility of simultaneously measuring the time-varying harmonic Doppler signature of multiple tags in the field of view by experimentally verifying the frequency selectivity of tags in motion using a 2.4/4.8 GHz harmonic Doppler radar.

Joint Use of Optical and Radar Remote Sensing Data for Characterizing the 2020 Aniangzhai Landslide Post-Failure Displacement

The ancient Aniangzhai (ANZ) landslide in Danba County, Sichuan Province of southwest China was reactivated after a series of complex hazard events that occurred in June 2020. Since then, and until June 2021, emergency engineering work was carried out to prevent the further failure of the reactivated landslide. This study investigates the potential of joint use of time series Interferometric Synthetic Aperture Radar (InSAR) and optical pixel offset tracking (POT) to assess deformation characteristic and spatial-temporal evolution of the reactivated ANZ landslide during the post-failure stage. The relationships between sun illumination differences, temporal baseline of correlation pairs and the uncertainties were deeply explored. Surface deformation along the line-of-sight (LoS) direction was retrieved by the time series InSAR processing with the two Sentinel-1 datasets, revealing a maximum deformation rate up to 190 mm/year. The large horizontal displacements were also detected from the POT processing using 11 optical images acquired by the PlanetScope satellite (3 m spatial resolution), showing a significant increase of about 24 m between 24 June 2020 and 11 June 2021. The time series analysis from the InSAR and optical POT results revealed that the reactivated ANZ landslide body is gradually slowing down to a steady deformation status since its occurrence in August 2020, indicating the effectiveness of engineering work on the prevention of further landslide. A slight acceleration was detected from both InSAR and optical POT time series analysis between May 2021 and June 2021, which could be caused by the increased rainfall in May 2021.

Multi-Object Tracking with mmWave Radar: A Review

The boundaries of tracking and sensing solutions are continuously being pushed. A stimulation in this field over recent years is exploiting the properties of millimeter wave (mmWave) radar to achieve simultaneous tracking and sensing of multiple objects. This paper aims to provide a critical analysis of the current literature surrounding multi-object tracking and sensing with short-range mmWave radar. There is significant literature available regarding single-object tracking using mmWave radar, demonstrating the maturity of single-object tracking systems. However, innovative research and advancements are also needed in the field of mmWave radar multi-object tracking, specifically with respect to uniquely identifying multiple target tracks across an interrupted field of view. In this article, we aim to provide an overview of the latest progress in multi-target tracking. In particular, an attempt to phrase the problem space is made by firstly defining a typical multi-object tracking architecture. We then highlight the areas for potential advancements. These areas include sensor fusion, micro-Doppler feature analysis, specialized and generalized activity recognition, gait, tagging and shape profile. Potential multi-object tracking advancements are reviewed and compared with respect to adaptability, performance, accuracy and specificity. Although the majority of the literature reviewed has a focus on human targets, most of the methodologies can be applied to targets consisting of different profiles and characteristics to that of humans. Lastly, future research directions are also discussed to shed light on research opportunities and potential approaches in the open research areas.

Motion-Regularized Background-Aware Correlation Filter for Marine Radar Target Tracking

Marine radar target tracking in atrocious sea conditions is often challenging, as the strong sea clutter makes conventional trackers non-robust in the marine environment, where it is very common. To improve the ability to identify targets from background clutter, the background-aware correlation filter (BACF) of the visual tracker is applied to marine radar target tracking. In addition, the BACF is optimized by adding the motion regularization term and the temporal consistency constraint to avoid tracking drift and stabilize filter variations. The experiment results on X-band Marine radar data demonstrate that the proposed algorithm performs favorably.