Target Motion Parameters Research Articles

High Maneuvering Target Long-time Coherent Integration and Motion Parameters Estimation Based on Bayesian Compressive Sensing

This paper intends to address the long-time coherent integration (LTCI) of high maneuvering targets in low signal-to-noise (SNR) environments with high computational efficiency and accuracy. The high-order motion parameters estimation, considering the acceleration and jerk, is modeled as the under-estimated linear regression and the complex-field Bayesian compressive sensing (BCS) algorithm is introduced to resolve the sparse recovery. To correct the high-order range migration (RM) and reduce the order of Doppler frequency migration (DFM), the adjacent cross correlation (ACCF) is applied. An effective method based on non-coherent integration is proposed to extract the self-term from ACCF result. Further, with the proper design of sensing matrix, the acceleration and jerk motion parameters are estimated by sparse reconstruction based on the complex-field BCS. Compared with the traditional methods based on time-frequency transform, like Lv's distribution (LVD), the proposed BCS algorithm is free from the interference of cross-terms and maintains the super-resolution ability, which provides better performance in parameters estimation and multi-targets discrimination. Finally, the numerical experiments validate the advantages of the proposed method in motion parameters estimation, super-resolution ability and computational efficiency.

TDOA –FDOA method for tracking of a moving target in a distributed sensor scenario

Tracking of an underwater target in ocean environment is very important in both military and civilian applications. This paper focuses on the localization and tracking of a moving target by a passive multi-static sonar system using TOA, Time Difference and Frequency Difference of Arrival measurements. An algorithm is developed for an acoustic sensor distributed network of five sensors and an acoustic moving source. TDOAFDOA method is used to track a moving source usingTime Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) measurements, as a closed form solution. The highly nonlinear measurement equations of TDOA and FDOA are first converted into pseudo linear equations. This is achieved by introducing extra parameters. These equations are solved using a weighted least squares (WLS) method. Five distributed stationary receivers are used to track a moving acoustic target. The moving target parameters estimated from the above WLS method are improved by an Unscented Kalman Filter. The mean square errors of these estimates are compared and the results are analyzed.

Moving Targets Detection for Video SAR Surveillance Using Multilevel Attention Network Based on Shallow Feature Module

In this article, a novel method for the moving target detection through multilevel spatial and channelwise attention network based on shallow feature channel module (MSCA-SFCM) is presented, and the circular spotlight (CSL) video synthetic aperture radar ground moving target indication (Video-SAR-GMTI) mode of the Nanjing University of Aeronautics and Astronautics miniature SAR (NUAA MiniSAR) system is introduced. However, due to the lack of moving target samples, MSCA-SFCM cannot be directly applied to the CSL Video-SAR-GMTI mode in the real system. To this end, this article proposes a training sample library construction scheme for moving targets of high verisimilitude. In this scheme, based on the radar system parameters, after the traversal of moving target parameters and SAR imaging, the scattering line characteristic of all possible moving targets under the current system parameters is simulated and then used for MSCA-SFCM network training. Afterward, the properly trained network can be used for moving target detection in real radar data. The effectiveness of the proposed method is verified by the NUAA MiniSAR system.

Autofocusing and imaging algorithm for moving target by vortex electromagnetic wave radar

Electromagnetic (EM) vortex imaging, which combines with spiral wavefront phase modulation and computational imaging concept introduced by orbital angular momentum (OAM), has demonstrated superior performance in target information extraction and two-dimensional (2-D) or even three-dimensional (3-D) imaging capability in forward-looking observation geometry. However, due to the unknowns and complexity of target motion, the translational component will lead to the disturbance of echo phase, which hinders the motion compensation between target echo pulses and causes imaging defocus. In this paper, a high-resolution imaging algorithm based on Fourier ptychography (FP) is proposed for moving target autofocusing imaging by vortex electromagnetic forward-looking radar (VEMFLR). Firstly, based on the spatial geometric model and echo model of VEMFLR, high-order phase terms caused by target motion are derived, which provides a theoretical basis for applying FP phase retrieval to autofocusing imaging; Then, the spectrum information in FP sub-aperture is obtained; Thirdly, the imaging method, based on FP phase retrieval, is exploited to compensate phase error and obtain a well-focused image. Simulation results show that this method can achieve fine imaging performance without estimating target motion parameters.

Image-based finite-time visual servoing of a quadrotor for tracking a moving target

This paper proposes an image-based visual servoing control method for a moving target of a quadrotor UAV (QUAV). Firstly, the dynamic image model with moving target parameters is established based on the image moment features in the virtual camera plane. For the unpredictability of the moving target in space, we use a high-order differentiator to estimate the state parameters of the moving target. In order to solve the problem of image depth information caused by a monocular camera, we derive a nonlinear finite-time linear velocity observer from the virtual image plane, which can not only estimate the linear velocity information of QUAV but also avoid the measurement of image depth. Based on the above information, we design the global finite-time controller and use Lyapunov theory to prove the finite-time stability of the system. Finally, the numerical simulations verify the convergence of the proposed control scheme, and the ROS gazebo simulations demonstrate the improved performance of the proposed control scheme in tracking error.

A Coherent Integration Segment Searching Based GRT-GRFT Hybrid Integration Method for Arbitrary Fluctuating Target

Long-time integration is an effective method for improving the signal–to–noise ratio (SNR) of an echo. However, if the target radar cross-section (RCS) fluctuates over the long integration time, the traditional coherent integration and noncoherent integration methods will produce significant performance losses, making it impossible to achieve a favorable integration performance at low SNRs. This study proposes a new hybrid integration method based on the generalized Radon–Fourier transform (GRFT) and generalized Radon transform (GRT) for targets with which echoes are partially coherent. First, a coherent integration is performed with GRFT within the optimal coherent processing segment using optimal coherent processing segmented matching. Then, the GRT is used for noncoherent integration between the coherent processing sections, and the target motion parameters are obtained through a global search. Compared with the GRFT, GRT, and moving target detection (MTD)-GRT methods, the proposed method applies to targets with arbitrary RCS fluctuations, arbitrary cross-range cells, and cross-Doppler cells, and offers the best detection performance. Finally, both simulation results and measured data processing results demonstrate the effectiveness of the algorithm.

An Improved Ground Moving Target Parameter Estimation and Imaging Method for Multichannel High Resolution SAR

With increasing demands from both military and civilian applications, ground moving-target imaging is becoming one of the important research topics for high-resolution SAR systems. However, the existing moving-target imaging methods are not suitable for high-resolution SAR because of their low parameter estimation accuracy and high computational complexity. To solve the problem, an improved ground moving-target parameter estimation and imaging method is proposed. First, the third-order phase model of the uniformly accelerated target signal is constructed, and the Hough transform and the second-order Keystone transform (SOKT) are used to correct the range cell migration into one range cell to achieve target coherent accumulation. Secondly, a delayed cross-correlation function (DCCF) is constructed to reduce the order of the range migration phase response in the slow time domain, and the coupling degree between the cross-correlation peak position and the range migration is reduced, so that the obtained DCCF has a higher gain, which ensures the accuracy of parameter estimation. Parameter estimation is simplified to peak detection by the Shift-And-Correlation (SAC) algorithm and two-dimensional Fourier transform (2D-FFT), avoiding parameter search. Compared with the existing methods, the proposed method has better focusing effect and lower computational complexity. Finally, simulation and measured data are given to verify the effectiveness of the proposed method.

Parameter Estimation for Uniformly Accelerating Moving Target in the Forward Scatter Radar Network

Passive radar based on the global navigation satellite positioning system (GNSS) has become the focus of attention in the field of radar. A parameter estimation method is proposed in the forward scatter radar (FSR) network based on GNSS to extend the application scenarios. For uniformly accelerating moving targets, only the instant times when the target crosses the individual baselines are used to retrieve the target motion parameters. The target position, velocity, and acceleration information can be obtained. Firstly, the minimum network configuration is derived theoretically. Then, the effects of crossing time error, station location error, transmitting/receiving station deployment, and target height on the accuracy are analyzed through Monte Carlo simulations. Finally, the simulation results indicate that the target position estimation error is in the order of 100 m. This paper provides the fundamental theory of aerial target positioning with a GNSS-based FSR network.

Motion parameter estimation of high‐speed manoeuvering targets based on hybrid integration and synchrosqueezing transform

In this study, we address the problem of estimating the motion parameters of multiple high-speed manoeuvering targets under low signal-to-noise ratio (SNR) environment with a low-range resolution radar, where the target movement could cause the echo signal energy seriously spread across both the range and Doppler frequency coordinates, and the returned signals from different targets would merge into each other. Based on the subaperture feature enhancement, Hough transform (HT) and the second-order short-time Fourier-based synchrosqueezing transform (FSST2), a novel method, named as EHFSST2, is proposed. The proposed method can robustly extract the targets of interest from environment and accurately estimate the targets' motion parameters. Thanks to its ability to compensate the effects of the range walk, curvature, and the Doppler ambiguity, spreading, wrapping, and the energy of echo signal can be coherently integrated along the target's individual moving trajectory. In addition, several numerical experiments are provided to demonstrate the effectiveness. The results show that the proposed method is superior to some existing methods in terms of the anti-noise ability, measurement accuracy and integration performance.

A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

The essential purpose of radar is to detect a target of interest and provide information concerning the target's location, motion, size, and other parameters. The knowledge about the pulse trains’ properties shows that a class of signals is mainly well suited to digital processing of increasing practical importance. A low autocorrelation binary sequence (LABS) is a complex combinatorial problem. The main problems of LABS are low Merit Factor (MF) and shorter length sequences. Besides, the maximum possible MF equals 12.3248 as infinity length is unable to be achieved. Therefore, this study implemented two techniques to propose a new metaheuristic algorithm based on Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA) using Inverse Filtering (IF) and clipping method to achieve better results. The proposed algorithms, LABS-IF and HMSCACSA-IF, achieved better results with two large MFs equal to 12.12 and 12.6678 for lengths 231 and 237, respectively, where the optimal solutions belong to the skew-symmetric sequences. The MF outperformed up to 24.335% and 2.708% against the state-of-the-art LABS heuristic algorithm, xLastovka, and Golay, respectively. These results indicated that the proposed algorithm's simulation had quality solutions in terms of fast convergence curve with better optimal means, and standard deviation.

A Novel Iterative Inner-Pulse Integration Target Detection Method for Bistatic Radar

A large time-bandwidth product bistatic radar offers several advantages in high-resolution target detection and motion parameter estimation, but the scale factor and inner-pulse Doppler will also be introduced in the radar echoes when detecting high-speed targets. Under this condition, the conventional matched filter will cause a certain energy loss and a non-negligible shift of the range center, which is called mismatch effect. Considering the special geometries of the bistatic radar, we first establish the precise echo signal model to describe the radar echoes for high-speed targets in a space-air based bistatic radar system. By analyzing the mismatch effect within the pulse and the migration between pulses, we have obtained the mathematic relationship between the scale factor, inner-pulse Doppler shift, range, equivalent velocity and accelerate. Following that, we perceive that the parameters of the matched filter are related to the target motion parameters, i.e., a priori unknown, which inspires us to propose an iterative coherent integration method to achieve the intra-pulse and inter-pulse integration. A precise echo signal model matched filter with initial parameters is defined and a roughly motion parameter estimation is acquired by the precise echo signal model-based Keystone transform and inner-pulse chirp Fourier transform. The estimated parameters are used for matched filter construction and coherent integration. This process is performed over multiple iterations to provide an accurate motion parameter result. In the end, a target detection experiment is given to show the effectiveness of the proposed method using space-air based bistatic radar.

Fully Coherent Integration and Measurement of Optimized Frequency Agile Waveform for Weak Target High-Resolution ISAR Imaging

Frequency agile waveform effectively decreases interception probability and increases the anti-jamming ability for radar probing in the electromagnetic countermeasure environment. However, the transmitting agile frequency introduces jitter phases causing de-coherence of echoes, which decreases signal-to-noise ratio (SNR) accumulation gain and leads to difficult motion estimation. Therefore, stable target detection, motion parameters estimation, and inverse synthetic aperture radar (ISAR) imaging for frequency-agile radar are still intractable problems in the low SNR environment. Aiming at these problems, we proposed a fully coherent processing method for weak target detection and ISAR imaging for frequency agile waveform in this paper. The agile waveform with low range and Doppler side lobes is designed to improve motion parameters estimation and ISAR imaging performance. Then, the joint jitter phases and motion parameters estimation based on the generalized likelihood ratio test (GLRT) is proposed to realize robust target detection and motion parameters estimation in low SNR conditions. The translational motion compensation and sparse ISAR imaging methods are also presented based on the detection and motion parameters estimation results. Finally, both simulated and real-measured data are used to verify the remarkable detection, motion parameters estimation, and ISAR imaging performance compared with the traditional non-coherent accumulation and mixture of coherent and non-coherent accumulation methods.

A Phase-Derived Velocity Measurement Method Based on the Generalized Radon–Fourier Transform With a Low SNR

The phase-derived velocity measurement (PDVM) technique can achieve a high measurement accuracy at the phase level and thus has great application prospects in the field of micromotion feature extraction and target recognition. To achieve a PDVM with a low signal-to-noise ratio (SNR), a PDVM method based on the generalized Radon–Fourier transform (GRFT) is proposed in this article. The main challenges that we overcome are phase extraction and phase ambiguity resolving under the condition of a low SNR. By utilizing the GRFT to estimate the target motion parameters, the echo peak position can be reconstructed, and then the peak phase value can be extracted. In the meantime, the phase ambiguity integer can be resolved based on the rough velocity estimation results obtained by the GRFT, and the phase ambiguity resolving can be realized at a low SNR. In addition, to suppress the influence of noise on the extracted phase, a filter design method based on the target motion characteristics is proposed to further improve the accuracy of the PDVM. In the simulation, the performance of the proposed method under different motion models and different SNR conditions is analyzed, and the effectiveness of the proposed method under low-SNR conditions is verified. Compared with directly using the GRFT, the proposed method has the advantages of strong applicability to different motion models and low computational load.

Information technologies for creating spatiotemporal modems multiposition active-passive radar systems

The methods of synthesis of the directional diagram of active transmitting antenna arrays when receiving signals reflected from radar targets are considered. It is shown that when using multifrequency orthogonal coherent signals in the elements and addressable access at their reception it is possible to provide a small level of the side lobes of the spatial uncertainty function in a given sector of observation by selecting the type of intrapulse modulation of partial signals. Orthogonalization of antenna basis of transmitting and receiving antennas allows digital spectral-correlation processing of samples of aggregate signal from each target to solve the technological problem of multidimensional observation space in multiposition systems of coherent radiolocation when detecting, resolving, estimating coordinates and motion parameters of targets. The results of simulation modeling of spatio-temporal radar modems implemented according to the stated principles are given.

Pose Estimation of Non-Cooperative Space Targets Based on Cross-Source Point Cloud Fusion

On-orbit space technology is used for tasks such as the relative navigation of non-cooperative targets, rendezvous and docking, on-orbit assembly, and space debris removal. In particular, the pose estimation of space non-cooperative targets is a prerequisite for studying these applications. The capabilities of a single sensor are limited, making it difficult to achieve high accuracy in the measurement range. Against this backdrop, a non-cooperative target pose measurement system fused with multi-source sensors was designed in this study. First, a cross-source point cloud fusion algorithm was developed. This algorithm uses the unified and simplified expression of geometric elements in conformal geometry algebra, breaks the traditional point-to-point correspondence, and constructs matching relationships between points and spheres. Next, for the fused point cloud, we proposed a plane clustering-method-based CGA to eliminate point cloud diffusion and then reconstruct the 3D contour model. Finally, we used a twistor along with the Clohessy–Wiltshire equation to obtain the posture and other motion parameters of the non-cooperative target through the unscented Kalman filter. In both the numerical simulations and the semi-physical experiments, the proposed measurement system met the requirements for non-cooperative target measurement accuracy, and the estimation error of the angle of the rotating spindle was 30% lower than that of other, previously studied methods. The proposed cross-source point cloud fusion algorithm can achieve high registration accuracy for point clouds with different densities and small overlap rates.

Motion Parameter Estimation and HRRP Construction for High-Speed Weak Targets Based on Modified GRFT for Synthetic-Wideband Radar With PRF Jittering

The synthetic-wideband and pulse repetition frequency jittering radar systems are receiving increasing attention due to the relatively low construction costs and good anti-jamming performance. However, the inter-pulse random hopping of the carrier frequency and pulse repetition frequency introduces challenges to the long-time coherent integration and motion parameter estimation of the high-speed weak targets for these radar systems. A modified generalized Radon Fourier transform for weak target detection and motion parameter estimation is proposed. The proposed method can achieve full coherent integration gain for the high-speed weak target and has a good parameter estimation accuracy. After the target motion has been well compensated, the well-focused high-resolution range profile of the target can be obtained by synthetic-wideband processing. The effectiveness of the proposed method is verified by Monte Carlo simulations. In addition, experimental data collected using an X-band radar system are processed and analyzed to illustrate the practical performance of the proposed method.

Improved fingerprinting performance in indoor positioning by reducing duration of the training phase process

<span>Wireless <span>sensor network (WSN) can be used as a solution to find out the position of an object that cannot be reached by global positioning system (GPS), for example to find out the position of objects in a room known as Indoor Positioning. One method in indoor positioning that can be used is fingerprinting. Inside there are two main work phases, namely training and positioning. The training phase is the process of collecting received signal strength indication (RSSI) data levels from each sensor Node reference that will be used as a reference value for the positioning phase. The more sensor Nodes used, the longer the processing time needed in the training phase. This research focussed on the duration of the training phase, the implementation of which are used 4 sensor Nodes, namely Zigbee (IEEE 802.15.4 protocol) arranged according to mesh network topology, one as Node X (positioning target) and 3 as reference Nodes. There are two methods used in the training phase, namely fixed target parameter (FTP) and moving target parameter (MTP). MTP took 5 seconds faster than FTP in terms of the duration of RSSI data collection from each reference Node. </span></span>

Unscented Particle Filter Approach for Underwater Target Tracking

This research aims to find an appropriate approach to improve system accuracy in the Doppler-bearing tracking (DBT) problem for target estimation. The topic of DBT problem is to achieve a target trajectory using bearing and frequency measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. The unscented particle filter approach is proposed to estimate the accuracy in the target motion parameters (TMP). This approach requires the observer maneuver so that the target trajectory is observable. Although in recent research papers, DBT has been proven to work efficiently without observer maneuver, TMP is unknown to the observer, and consequently, there is a need for observer maneuver. So, the algorithm is simulated with observer following s-maneuver and without any maneuver executed by the observer, and results are compared. The effectiveness of the solution and results are determined by using MATLAB simulation. It is shown that the truthfulness of the outcome is superior when the observer performs s-maneuver while compared to that without observer maneuver.

Generalized Radon transform approach to target motion parameter estimation using a stationary underwater vector hydrophone.

Underwater platforms provide long-term detection of undersea targets. In this paper, we propose a method for the estimation of target motion parameters by submerged static acoustic detection equipment. The proposed method is based on the Radon transform of modeling the target moving in a uniform straight line. The heading angle, the time to the closest point of approach (CPA), and the ratio of velocity to the horizontal range of the target at the CPA to the sensor are obtained by applying the generalized Radon transform (GRT) to bearing-time records. The velocity of the target is determined by applying the GRT to the line-spectrum-time records. Furthermore, the motion trajectory of the target with respect to the detection equipment can be calculated from the above parameters. To validate the feasibility and performance of the proposed method, computer simulations and sea trials based on a fixed single vector measurement system were analyzed in this paper. The results suggest that the proposed method can accurately estimate the motion parameters and can calculate the trajectory of the moving vessel along a straight line at constant velocity.

Application of Cubature Information Filter for Underwater Target Path Estimation

Bearings-only tracking plays a pivotal role in passive underwater surveillance. Using noisy sonar bearing measurements, the target motion parameters (TMP) are extensively estimated using the extended Kalman filter (EKF) because of its simplicity and low computational load. The EKF utilizes the first order approximation of the nonlinear system in estimation of the TMP that degrades the accuracy of estimation due to the elimination of the higher order terms. In this paper, the cubature Kalman filter (CKF) that captures the system nonlinearity upto third order is proposed to estimate the TMP. The CKF is further extended using the information filter (IF) to provide decentralized data fusion, hence the filter is termed as cubature information filter (CIF). The results are generated using Matlab with Gaussian assumption of noise in measurements. Monte-Carlo simulation is done and the results demonstrate that the CIF accuracy is same as that of UKF and this indicates the usefulness of the algorithm for state estimation in underwater with the required accuracy.