Ground Moving Target Research Articles

A video speed measurement method for UAV targeting ground targets based on kalman filtering

Abstract Measuring the velocity information of moving targets, as an application scenario for drone target velocity measurement, is an important focus in the process of drone intelligence reconnaissance. To improve the speed and accuracy of unmanned aerial vehicles (UAVs) in locating and measuring moving targets, a reliable target localization and velocity model based on UAV reconnaissance video frames and Kalman filtering is proposed. This algorithm model can complete the localization and velocity measurement tasks of moving targets under complex conditions without the need to obtain ranging information from drones. To verify the algorithm, the Monte Carlo simulation method was used for simulation experiments. The experimental results show that the error of this velocity measurement algorithm can meet the accuracy requirements of unmanned aerial vehicles for ground-moving targets.

Application of Digital Twin Technology in Synthetic Aperture Radar Ground Moving Target Intelligent Detection System

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously.

Hybrid Control System for a Vertically Guided Bomb During Self-guidance in Turbulent Conditions

The article presents a mathematical model and an algorithm for hybrid control of a bomb aimed at a moving ground target. The guided bomb flight control system subject to the study combines a conventional PD controller and a quasi-sliding mode (QSM) controller. The target trajectory was determined based on the kinematic relations of the mutual motion of the bomb and the ground target using the proportional approach method. The main aim of the article is to analyse the impact of atmospheric turbulence on the flight of a guided bomb, and then to determine its impact on homing parameters, such as homing time and accuracy of hitting a ground target. The numerical studies covered three types of controllers: conventional PID, quasi-sliding and hybrid. The effectiveness of the proposed control system was analysed without and during random atmospheric turbulence. The article examines the hybrid system’s properties for controlling guided bombs. Numerical studies were performed using the Matlab/Simulink software suite. The article presents selected results of this computer simulation.

Deep learning and quantization for accurate and efficient multi-target radar inference of moving targets

Real-time, radar-based human activity and target classification is useful for wide-area ground surveillance. However, the feasibility of deploying deep learning (DL) models in radar-based systems with limited computational resources remains unexplored. This paper investigated the effect of quantization on model throughput and accuracy for deployment in radar systems. A seven-layer residual network was proposed to classify ground-moving targets and achieved a test accuracy of 87.72%. The model was then quantized to 16-bit and 8-bit precision, resulting in a 3.8 times speedup in inference throughput, with less than a 0.4% drop in test and validation accuracy. The results showed that quantization can improve inference throughput with a negligible decrease in target classification accuracy. The increase in throughput and reduction in computational expense that comes with quantization promotes the feasibility of the deployment of DL models in systems with limited computational resources. The findings of this paper hold significant promise for the successful use of quantized models in modern radar systems, while adhering to stringent size, weight and power consumption constraints.

Miniaturization Design of High-Integration Unmanned Aerial Vehicle-Borne Video Synthetic Aperture Radar Real-Time Imaging Processing Component

The unmanned aerial vehicle (UAV)-borne video synthetic aperture radar (SAR) possesses the characteristic of having high-continuous-frame-rate imaging, which is conducive to the real-time monitoring of ground-moving targets. The real-time imaging-processing system for UAV-borne video SAR (ViSAR) requires miniaturization, low power consumption, high frame rate, and high-resolution imaging. In order to achieve high-frame-rate real-time imaging on limited payload-carrying platforms, this study proposes a miniaturization design of a high-integration UAV-borne ViSAR real-time imaging-processing component (MRIPC). The proposed design integrates functions such as broadband signal generation, high-speed real-time sampling, and real-time SAR imaging processing on a single-chip FPGA. The parallel access mechanism using multiple sets of high-speed data buffers increases the data access throughput and solves the problem of data access bandwidth. The range-Doppler (RD) algorithm and map-drift (MD) algorithm are optimized using parallel multiplexing, achieving a balance between computing speed and hardware resources. The test results have verified that our proposed component is effective for the real-time processing of 2048 × 2048 single-precision floating-point data points to realize a 5 Hz imaging frame rate and 0.15 m imaging resolution, satisfying the requirements of real-time ViSAR-imaging processing.

Estimation of tangential speed of ground moving targets in SAR

In parallel with the development of systems of highly detailed mapping of the underlying surface, methods of ground moving targets indication (GMTI) were also improved. A dramatic improvement in the resolution characteristics of onboard radars occurred after the introduction of mapping modes with synthetic artificial antenna aperture. The advent of synthetic aperture radars (SAR) made it possible to detect ground moving targets (GMT) against the background of reflections from the underlying surface, which made it possible to further implement GMTI algorithms for detecting low-speed moving ground objects. The simple way to carry out GMTI is if the observed target has a radial component of the velocity vector, which generates a Doppler frequency shift in the reflected signal. A more complex situation arises when it is necessary to detect targets moving at a tangential speed relative to the onboard radar. The difficulty of detecting targets moving at tangential speed is due to the fact that target movement in the direction perpendicular to the line of sight leads to deformation of the phase structure of the signal. This particular deformation manifests itself in the appearance of quadratic phase modulation of the reflected trajectory signal. With consistent signal processing in mapping mode, due to the appearance of quadratic phase modulation, the response of the observed moving target will be defocused. To detect the presence of a moving target with tangential speed, it is proposed to use an algorithm similar to the well-known phase difference algorithm for autofocusing radar images. It consists in dividing the implementation of the trajectory signal into two parts, after which the bottom of them is subjected to a complex conjugation procedure. Next, the two implementations are multiplied with each other and the amplitude spectrum of the result of the multiplication is calculated. The shift of the maximum spectral density relative to the origin is proportional to the tangential velocity of the observed target. The sign of the shift (to the right or to the left) depends on the direction of movement of the GMT relative to the velocity vector of the carrier. This algorithm was justified by an analytical method, then its mathematical modeling was carried out. In the case of observation of spruce trees by on-board radar with a high tangential velocity, the error in its estimation increases, which is due to the spreading of the spectrum of the signal reflected from the GMT to reduce the influence of this effect.

Method for Estimating SAR Ground-Moving Target Parameters With Azimuth Missing Data Based on Contrast Maximization

Method for Estimating SAR Ground-Moving Target Parameters With Azimuth Missing Data Based on Contrast Maximization

Simultaneous search and monitoring by multiple aerial robots

This paper studies simultaneous search and monitoring (SSM) between multiple unmanned aerial vehicles (UAVs) and multiple moving ground targets. Searching for unknown targets and monitoring known ones are two intrinsically related problems, but they have mostly been addressed in isolation. We combine the two tasks and exploit their interconnection as a synergy rather than a trade-off. We construct the single-robot SSM as a partially observable Markov decision process (POMDP) and the multi-robot SSM as a semi-decentralised POMDP (semi-Dec-POMDP). A novel heuristic reactive policy planning is proposed to solve the POMDP. It is then extended for semi-Dec-POMDP with game-theoretical methods. In simulations and experiments, the searchers will successfully locate unknown targets without losing known ones and cooperate by partitioning their tasks. With theoretical proofs, simulations, and experiments, we demonstrate that our method can perform better than conventional approaches and the state-of-the-art.

AN INTEGRATED METHOD FOR REFOCUSING OF MOVING TARGETS IN SPOTLIGHT SAR

A new target-refocusing technique based-on re-centering phase computation of previously recorded moving target raw data is implemented to the Spotlight SAR data in order to obtain refocused moving targets. The technique is tested on the integrated simulated data; background real spotlight SAR Raw data with the synthetically generated data domes of civilian moving targets. First Polar format Algorithm is applied to detect and estimate the speed of ground-moving targets on the integrated raw data. At the next step, re-organize the integrated raw data by selecting and arranging target focusing center with a new technique based on re-centering phase computation to each moving target speed. At the third step re-organize the raw data by re-centering the phase computation to each moving target location. Finally, Polar Format Algorithm is applied to each reorganized raw data to obtain highly focused moving targets individually

A Priori Knowledge Based Ground Moving Target Indication Technique Applied to Distributed Spaceborne SAR System

Through formation flying, the distributed spaceborne SAR(synthetic aperture radar) system can increase the number of spatial degree of freedoms (DOFs) and provide flexible multi-baselines for SAR-GMTI (ground moving target indication), which improves the system performance. This paper proposes an a priori knowledge-based adaptive clutter cancellation and moving target detection technique applied to the distributed spaceborne SAR-GMTI systems. Firstly, the adaptive clutter cancellation technique is exploited to suppress the ground clutter. A priori knowledge, such as road network information, is integrated to the adaptive clutter cancellation processor to reduce any moving target steering vector mismatch. Secondly, adaptive matched filter (AMF) and adaptive beamformer orthogonal rejection test (ABORT) are exploited as adaptive detection techniques for moving target detection. Due to the dense road network, the moving target steering vector estimation may be ambiguous for the different position and orientation of the roads. The multiple hypothesis testing (MHT) technique is proposed to detect the moving targets and resolve the potential ambiguities. A scheme is exploited to detect, classify, and relocate the moving targets. Finally, simulation experiments and performance analysis have demonstrated the effectiveness and robustness of the proposed technique.

Development of a YOLO-KCF Coupling Algorithm for Miniature Fixed-Wing UAVs in Target Detection and Tracking

Target detection and tracking represent key challenges facing miniature fixed-wing unmanned aerial vehicles (UAVs), particularly at high cruising speeds. Therefore, this paper proposes a vision-based target detection and tracking algorithm that systematically couples two mainstream methods, namely, you only look once (YOLO) and kernel correlation filter (KCF) algorithms. This combination enables small fixed-wing UAVs to achieve reliable target detection and rapid target tracking. A customized vision-guidance module is constructed to implement this algorithm, and a dual-thread execution mechanism is developed to ensure that the computational resources are used effectively. A miniature fixed-wing UAV experimental platform is also constructed and evaluated. Flight experiments are performed, and the results demonstrate that the developed algorithm can achieve satisfactory detection and tracking accuracy for stationary and moving ground targets in complex environments.

Dual-Channel Airborne SAR Imaging of Ground Moving Targets on Perturbed Platform

A dual-channel airborne SAR imaging approach is proposed to acquire well focused images of ground moving targets after compensating the platform trajectory deviation. The phase error attributed to either platform trajectory deviation or target motion is given by a polynomial of slow time. The SAR image before motion compensation is segmented into multiple sub-images, and a compressive-sensing (CS) technique is applied on each sub-image to estimate the polynomial coefficients featuring platform trajectory deviation. A range-frequency reversal transform (RFRT) and fractional Fourier transform (FrFT) are combined to estimate the motion parameters and position of each moving target. The proposed approach is verified with real data and simulated data, respectively, and the moving targets in the acquired image are well recognized.

Optimal Tracking & Evasive Algorithms For Fixed-Wing UAV & Target In 3-Dimensional Space

We present a three-dimensional optimal target tracking algorithm that enables an Unmanned Aerial Vehicle (UAV) to track a smart evasive ground-moving target. The UAV and target control algorithms are designed using two-step prediction schematics. For the UAV, the control strategy assumes a worst-case target evasion and derives a cost function to be continuously minimized. Conversely, the target evasion algorithm is developed by assuming a best-case UAV tracking scenario and establishing a cost function to persistently maximize their relative distance. In addition to acceleration and velocity constraints, the UAV and target control strategies are constrained by turn radius, turn rate, and bank angle limits. Simulations show that the UAV is able to pursue, maintain proximity and keep the target within its camera field of view despite frequent sharp evasive turns performed by the target.

Reduced-Complexity Subarray-Level Sparse Recovery STAP for Multichannel Airborne Radar WGMTI Application

For multichannel airborne radars, wide-area ground-moving target indication (WGMTI) processing can quickly obtain the dynamic distribution of moving targets in a wide area, which holds considerable significance in many fields. Nevertheless, the WGMTI mode suffers from the interference of powerful ground clutter, which frequently submerges slow-moving targets and causes many false alarms in subsequent moving target detection. Space–time adaptive processing (STAP) can successfully suppress clutter, but its performance depends critically on the available training samples. Consequently, an effective STAP method characterized by fast processing and a small sample size for WGMTI application in multichannel airborne radars must be developed. In this paper, a subarray-level sparse recovery STAP (SR-STAP) processing framework is proposed for multichannel airborne radars. First, the characteristics of the subarray-level received clutter are discussed in detail. Second, on the basis of this analysis, we further designed a joint space–time dictionary and developed a separable tensor-based sparse Bayesian learning (STSBL) method. In this method, two-stage decomposition is proposed to ensure that large-scale data can be degraded into small-scale data in processing, which significantly improves computation efficiency. Finally, the effectiveness of the proposed STSBL-STAP method in WGMTI processing was verified using real measurement data obtained from a developed dual-channel Ku-band airborne radar.

Energy-efficient joint UAV secure communication and 3D trajectory optimization assisted by reconfigurable intelligent surfaces in the presence of eavesdroppers

We consider a scenario where an unmanned aerial vehicle (UAV), a typical unmanned aerial system (UAS), transmits confidential data to a moving ground target in the presence of multiple eavesdroppers. Multiple friendly reconfigurable intelligent surfaces (RISs) help to secure the UAV-target communication and improve the energy efficiency of the UAV. We formulate an optimization problem to minimize the energy consumption of the UAV, subject to the mobility constraint of the UAV and that the achievable secrecy rate at the target is over a given threshold. We present an online planning method following the framework of model predictive control (MPC) to jointly optimize the motion of the UAV and the configurations of the RISs. The effectiveness of the proposed method is validated via computer simulations.

Improving the automatic target recognition algorithm’s accuracy through an examination of the different time-frequency representations and data augmentation

Abstract This research focuses on an improved automatic target recognition algorithm for solving the classification challenge of ground-moving targets from pulsed-Doppler radar. First, it was studied how decision-making intervals affect the proposed algorithm. Second, the altering of the data augmentation process was investigated. Third, a consideration of the three time-frequency signal representations and finally the use of different deep learning models for the classification issues were examined. It is proven that the proposed algorithm can efficiently recognize all targets enclosed in the publicly available RadEch dataset, with 4 s of radar echoes. When the decision-making time is only 1 s, a classification probability of 99.9% was obtained, which is an improvement related to the other research studies in this area. Furthermore, when the decision-making time is reduced 16 times the classification accuracy is reduced by only 1.3%. Moreover, the proposed algorithm was successful on another dataset enclosing ground-moving targets from comparable pulsed-Doppler radar.

Route Coordination of UAV Fleet to Track a Ground Moving Target in Search and Lock (SAL) Task Over Urban Airspace

Drone has become more and more popular in various civil applications due to the open of the low-altitude airspace and its easy operation. Unlike the common search and track task for the target, the new search and lock (SAL) task is focused on in this article. In this task, multiple drones first try to detect the moving ground target cooperatively. Then, they must lock the target by covering all the surrounding area of it at a low flight altitude, which indicates that the target can be watched clearly in all directions from then on. The SAL task can be applied in the panorama shot for the moving ground target. First, the low-altitude urban airspace is discretized into cubes, based on which the flight rules of drone are defined. The field of view (FOV) of drone is modeled considering the flight altitude and the block of buildings. For the cooperation among multiple drones, the constraints on the patten of waypoint, the communication distance, and the collision avoidance are all included. The goal in the search phase is to cover more area, which have not been visited recently to increase the probability of detecting the target, and it is expected to lock the target as soon as possible in the lock phase. A new swarm-based imitative learning optimization (SBILO) algorithm is proposed to determine the waypoint of drone in the search phase. To have a quick response to the escape behavior of the target in the lock phase, the waypoint of drone is generated in a distributed way to cover more surrounding area of the target and lock it gradually. The case of losing the target in the FOV of all drones is also addressed by covering more possible places where the target may appear. Simulation results demonstrate that the SAL task can be performed efficiently by the drones with the flight routes obtained by the proposed SBILO algorithm and the distributed asynchronous decision-make (DADM) approach.

Sparse SAR Imaging Method for Ground Moving Target via GMTSI-Net

Ground moving targets (GMT), due to the existence of velocity in range and azimuth direction, will lead to the deviation from their true position and defocus in the azimuth direction during the synthetic aperture radar (SAR) imaging process. To address this problem and compress the amount of echo data, a sparse SAR imaging method for ground moving targets is proposed. Specifically, we first constructed a two-dimensional sparse observation model of the GMT based on matched filter operators. Then, the observation model was solved by a deep network, GMT sparse imaging network (GMTSI-Net), which was mainly obtained by unfolding an iterative soft threshold algorithm (ISTA)-based iterative solution. Furthermore, we designed an adaptive unfolding module in the imaging network to improve the adaptability of the network to the input of echo data with different sampling ratios. The proposed imaging network can achieve faster and more accurate SAR images of ground moving targets under a low sampling ratio and signal-to-noise ratio (SNR). Simulated and measured data experiments were conducted to demonstrate the performance of imaging quality of the proposed method.

A Novel Intelligent Ground Moving Target Indication Using Meta-Heuristic-Based Simplified Fractional Fourier Transform

One of the most significant subfields in “Synthetic Aperture Radar (SAR)” research is considered to be target detection. Numerous studies have been conducted on target identification, with the majority of them favoring filter-oriented methods. The fundamental goal of radar systems is to “detect moving targets on the ground.” Decomposing a complex matrix into a structured sparse matrix and a low-rank matrix is a fundamental mathematics issue. Surveillance and reconnaissance rely heavily on “Ground Moving Target Indication (GMTI),” but it's not a simple task. The SAR ATI was first developed for calculating the radial velocity of ground-moving objects. Yet, overlapping stationary clutter can corrupt the recorded differential phase, resulting in mistakes in position and velocity calculations. The main concept of this paper is to propose a novel “Adaptive Simplified Fractional Fourier Transform (A-SFrFT)” using the intelligent meta-heuristic improvement. This adaptive SFrFT efficiently estimates the “Doppler parameters of the moving targets.” The improved “Harris Hawks Optimization (HHO)” termed Trio Updating HHO (TU-HHO) is used as the meta-heuristic algorithm that enhances the performance of the SFrFT-based target estimation. The mathematical analysis and simulation findings show that the suggested methods recommended strategy is successful.

Multi-UAVs collaborative tracking of moving target with maximized visibility in urban environment

Unmanned aerial vehicles (UAVs) with limited field of view are utilized to track a moving ground target continuously in urban environment. In urban environment, the sight lines of UAVs to the target are easily blocked by dense obstacles. To overcome this difficulty, the model predictive control (MPC) based collaborative tracking control is proposed with the goal of the maximum visibility of target. First, a visible probability based performance index is proposed, and the flight planning strategy of maximum the phase difference is obtained as a consequence. Then a centralized MPC based collaborative control problem is solved to obtain the optimal control signals. The joint cost function consists of four parts which aims at tracking target, avoiding collision, avoiding the blind area and maintaining the maximum visibility, respectively. The effectiveness of the proposed collaborative strategy is verified by simulation. Compared with the traditional MPC-based collaborative method, the proposed maximum visible probability index provides an optimal dynamic formation structure for multi UAVs to guarantee the tracking of the moving ground target in urban environment.