Low-speed Targets Research Articles

ROTracker: a novel MMW radar-based object tracking method for unmanned surface vehicle in offshore environments

Unmanned surface vehicles (USVs) offer significant value through their capability to undertake hazardous and time-consuming missions across water surfaces. Recently, as the application of USVs has been extended to nearshore waterways, object tracking is vital to the safe navigation of USVs in offshore scenes. However, existing tracking systems for USVs are mainly based on cameras or LiDAR sensors, which suffer from drawbacks such as lack of depth perception or high deployment costs. In contrast, millimeter-wave (MMW) radar offers advantages in terms of low cost and robustness in all weather and lighting conditions. In this work, to construct a robust and low-cost tracking system for USVs in complex offshore scenes, we propose a novel MMW radar-based object tracking method (ROTracker). The proposed ROTracker combines the physical properties of MMW radar with traditional tracking systems. Specifically, we introduce the radar Doppler velocity and a designed motion discriminator to improve the robustness of the tracking system toward low-speed targets. Moreover, we conducted real-world experiments to validate the efficacy of the proposed ROTracker. Compared to other baseline methods, ROTracker achieves excellent multiple object tracking accuracy in terms of 91.9% in our collected dataset. The experimental results demonstrated that the proposed ROTracker has significant application potential in both accuracy and efficiency for USVs, addressing the challenges posed by complex nearshore environments.

Model-based optimal action selection for Dyna-Q reverberation suppression cognitive sonar

The Doppler shift of low-speed targets is frequently disturbed by the reverberation Doppler spread clutter under the shallow sea. The clutter is generated by underwater scatterers, which increases the difficulty of Doppler estimation. To solve this problem, a reverberation target resolution function based on the Doppler spread clutter statistical model is proposed in this paper. Through the width of reverberation Doppler clutter, this function adjusts the waveform parameters by determining whether the target is discriminable. In addition, the reverberation Doppler spread clutter is time-spatial varying and affected by grazing angle, waves, wind speed, fish and other effects. Thus, the sonar waveform parameters need to be adjusted constantly. Therefore, this paper combines the cognitive sonar based on reinforcement learning with the reverberation target resolution function to evaluate different waveforms in different environments. Consequently, the sonar can adjust the waveform parameters in real-time and obtain the optimal waveform in different environments. Meanwhile, in this paper, the action selection strategy of Dyna-Q reinforcement learning is optimized, and the model-based maximum action selection Dyna-Q algorithm (Dyna-Q-Max-Action) is proposed. Compared with the traditional Dyna-Q and Q-learning algorithms, the proposed algorithm needs fewer episodes. Finally, numerical simulation verified the effectiveness of the proposed algorithm.

A Reverberation Suppression Method Based on the Joint Design of a PTFM Waveform and Receiver Filter.

Transmitting waveform design and signal processing method optimization are effective ways to improve a sonar system's detection performance. In this study, the spectrum and ambiguity function characteristics of pulse trains of frequency modulation (PTFM) signals were deduced and analyzed to address the problem of serious reverberation interference in the detection of low-speed targets in shallow water environments. The action mechanisms of PTFM signal parameters on the comb spectrum and bed of nails ambiguity function were identified. PTFM signal parameters were designed according to reverberation suppression requirements. The threshold was calculated using the estimate-before-detect method, and the comb spectrum waveform cognitive filtering detection algorithm is proposed. The simulation and lake experimental results show that the PTFM signals' reverberation suppression ability for low-speed targets was better than it was for stationary or high-speed targets. The proposed method has good universality, which can improve the output signal-to-reverberation ratio (SRR) by more than 6 dB.

Experimental Study of Trajectory Features for the Recognition of Low-Flying Low-Speed Radar Targets Using Passive Coherent Radar Systems

Introduction. Small unmanned aerial vehicles (UAVs) are a growing threat due to their possible use for illegal activities. Currently, passive coherent radar systems are widely used to detect, track and recognize moving targets, including small UAVs, which makes them a promising tool for use in modern airspace radar monitoring systems. At the same time, recognition of small UAVs becomes a challenging task due the possibility of confusing them with birds, particularly in maritime areas with large bird populations. In a search for new solutions to the problem of recognizing small UAVs, trajectory features can be used.Aim. To analyze differences between the trajectory features of low-flying low-speed targets in order to verify the possibility of their use for recognition purposes.Materials and methods. Real radar measurements of UAVs and birds obtained by a passive coherent radar system were used. Specific characteristics of the trajectory parameters of target classes were built using computer statistical modeling in the MatLab environment. Differences in the movement trajectory of targets were established by comparative analysis.Results. Significant differences between the flight path of UAVs and birds were found. Specific features of the trajectory of small aerial targets of each type were investigated. On the basis of radar measurement, graphs of the characteristic trajectory parameters of UAVs and birds were plotted. The conducted comparative analysis allowed identification of the characteristics of the flight path of each target type in each movement segment. Trajectory features that can be used for recognition purposes were identified.Conclusion. The practical significance of the proposed trajectory features and the possibility of their implementation in the development of an algorithm for recognizing low-flying low-speed radar targets using passive coherent radar systems was established. The knowledge of differences between the flight path of UAVs and birds can improve the quality of the UAV recognition problem.

A New Side-Looking Scheme for Speed Estimation and Detection of Tangential Slow-Moving Targets.

A single-beam radar system cannot adopt a side-looking installation scheme, which is completely perpendicular to the moving direction of the target in an intelligent transportation system (ITS), because of its own limitations. In this paper, a side-looking radar velocity measurement system that utilizes a new signal processing method and multi-channel radar scheme is proposed. Constant false alarm rate (CFAR) and generalized likelihood ratio test (GLRT) detectors are used to detect the data processing results in different stages in order to reduce the false alarm rate of targets. At the same time, a deconvolution-based clutter map algorithm is proposed to solve the problem of clutter interference in the test environment, and its theoretical performance is verified by simulation. Finally, 77 G commercial radar is used to test the system, and the results show that this algorithm can effectively detect and accurately estimate the speed of tangential low-speed targets under clutter interference.

低空高分辨率激光雷达光学系统设计

Aiming at the problems of optical imaging for low altitude, low speed and small target recognition ability and low signal-to-noise ratio, a low altitude and high resolution lidar optical system was designed. The MEMS mirror was used by the scanning device of the transmitting optical system, and the beam expanding system was designed to ensure the beam quality of the laser emitted at different scanning angles. The digital mirror device combine with objective lens, polarizing device were used for receiving optical system, the background noise was greatly lower than laser receiving system using single-point detector and could realize laser echo receiving and visible light imaging at the same time. The structure parameters of the optical system were given, an optical system was designed using optical design software. The optical system has a spatial resolution of 0.5 mrad/pixel and an array scale of 200×200. The simulation results show that design method is feasible. The detection distance can reach 1000 m, and the background noise can be reduced by about 22162 times compared to the single-point detector receiving system.ystem.

Geolocation Accuracy Evaluation of GF-4 Geostationary High-Resolution Optical Images over Coastal Zones and Offshore Areas

Wei, Y.; Zhang, Z.; Mu, B.; Li, Y.; Wang, Q., and Liu, R., 2020. Geolocation accuracy evaluation of GF-4 geostationary high-resolution optical images over coastal zones and offshore areas. In: Jung, H.-S.; Lee, S.; Ryu, J.H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 326-333. Coconut Creek (Florida), ISSN 0749-0208.GF-4 is the first high-resolution geostationary satellite launched by China in 2015 for disaster prevention and mitigation. It can provide high spatial (up to 50 m) and temporal resolution optical images. Autonomous geolocation accuracy is key for the application of satellite images, particularly for coastal zones, offshore areas, and even oceans with few ground control points (GCPs). Hence, this study characterizes the autonomous geometric accuracy of GF-4 panchromatic and multispectral sensor images over coastal zones and offshore areas, which is of particular importance for the monitoring of moving targets. The evaluation of nine GF-4 images based on 630 GCPs in coastal zones and offshore areas of the Bohai Sea and Yellow Sea indicates a geolocation uncertainty of 1925 ± 976 m (39 ± 20 pixels). For GF-4 images with similar imaging geometry, the geolocation discrepancy is lower than that with different imaging geometry, with the geolocation discrepancy of 1 ± 1 and 30 ± 13 pixels, respectively. Although with the lower geolocation discrepancy, noticeable uncertainty occurs in the estimated velocity of moving targets for images with similar imaging geometry, particularly for low-speed targets, such as macro-algae. This suggests that geometric correction is needed before the speed of the moving target is extracted. The geolocation accuracy of GF-4 images through rational polynomial coefficient orthorectification without GCPs is also poor, with the geolocation uncertainty of 1350 ± 864 m, whereas the geolocation accuracy significantly improved by 8.2 times after five GCPs were added (distributed in the four corners and center of the image).

Using learning time as metrics: an artificial intelligence driven risk assess framework to evaluate DDoS cyber attack

The cloud computing and Internet of Things (IoT) have become two key technologies to meet future business requirements. However, a massive scale of Distributed Denial-of-Service (DDoS) has been widely applied to congest network critical links and to paralyze the cloud and IoT service. This is mainly due to DDoS is easily implemented, obfuscated, and occulted by launching large-scale legitimate low-speed flows and rolling target links to paralyze target network areas. Many metrics and risk access management frameworks to evaluate the impact of DDoS are proposed. However, they all lack time granularity to evaluate the cost of different scales of attacks in IoT or large-scale network structure. This study proposes an AI Driven Evaluation framework, called ADE, that applies Convolution Neural Networks to statistically evaluate the network status through end-to-end functionality (Input: network status; Output: DDoS detected or not) without any manual intervention. ADE provides quantitative security risk analysis by using learning time as the control variable, network structure as the independent variable, and time to identify DDoS as the dependent variable. The learning time to detect DDoS event and recover the system is then applied to evaluate the scale of this DDoS, the reasonability of the regulated RTO, and the vulnerability of the current net-work topology and the improvement due to the new security solution. The experiment results demonstrate the contributions of ADE are (1) providing objective and quantitative analytical security risk assessment indicator, (2) providing an autonomic DDoS defense framework without any manual intervention which allows cloud computing and Internet of Things company focuses on their service and leaves security defending to ADE, and (3) demonstrating the possibility of AI assisted risk assessment which enables security defense solution buyer with less security domain experts to evaluate suitable network defense strategy.

Research of electronic compensation methods of reserence signal in the surveillance channel of semi-active coherent bistatic DVB-T2 radar

This paper provides an overview of different electronic compensation methods of a reference signal. An algorithm for electronic compensation of the DVB-T2 direct signal in a semi-active coherent radar has been proposed. A filter that provides direct signal suppression of at least 50 dB has been developed. The results of experimental studies on the detection of low-speed target with low effective scattering surface in the presence of a powerful reference signal have been presented.

基于高信背比的视频低速暗弱目标增强

基于高信背比的视频低速暗弱目标增强

Extraction of sea‐clutter and RFI regions based on image segmentation for high‐frequency sky‐wave radar

To monitor the sea-state and detect low-speed targets beyond the line-of-sight limitation, high-frequency (HF) sky-wave radar needs to extract the sea-clutter Doppler spectrum and suppress radio-frequency interference (RFI). However, this is hard for traditional radar processing techniques in practice. Instead, this study proposes to tackle these problems based on image recognition methods. By converting the range-Doppler (RD) map into a grey image, the extraction of sea-clutter and RFI regions becomes two problems of image segmentation. The sea-clutter region is extracted by the distance regularised level set evolution algorithm, based on a binary image which uses threshold segmentation to roughly classify sea-clutter pixels and noise pixels. To extract the RFI region, the authors develop a straight line detection algorithm which employs the LOG operator to mitigate the sea-clutter and highlight the RFI lines. Simulation results demonstrate that the proposed approaches are effective in the RD map with undesired RFI lines near the sea-clutter.

Inversion of Position for Low Speed Target in Near Space Using Non-resolved Photometric and Astrometric Data

A method for estimating the position parameters of a low speed target (LST) in near space was presented. The estimation method utilizes apparent magnitude, azimuthal angle, and angle of elevation to invert the position of a typical LST, such as a high altitude balloon (HAB). A photometric observation model composed of specular and diffuse reflection components for the LST was established. An astrometric observation model was discussed to deduce the azimuth and elevation of a ground-based opto-electronic detection system for the LST. An extended Kalman filter (EKF) was designed to invert the state parameters of the position and velocity of the LST from photometric and astrometric data. Two sets of simulations were carried out to validate this inversion method. The simulation results show that the simulation errors for the estimated parameters are small, the estimated positions agree with GPS very well, and the non-resolved photometric and angular data can be used to estimate the indirect observable position state parameters for a LST in near space.

A novel fast detection method of infrared LSS-Target in complex urban background

LSS-Target (the Low altitude, Slow speed and Small Target) is likely to be a threat to the observation platform, thus infrared LSS-Target detection is an urgent task. LSS-Target is a challenging issue due to the low Signal-to-Noise Ratio (SNR) and sophisticated background. Motivated by the analysis of infrared imaging characteristics, this paper proposes a novel fusion method for IR LSS-Target detection with complex urban background, which is suitable for precise guidance and self defense. First, an adaptive threshold segmentation based on accumulative histogram and maximum likelihood estimation are utilized to eliminate the clutter and improve SNR of the initial image. Second, a template is set up to identify the seed points in the image. Third, a constrained four criteria region growth algorithm is performed to separate the entire regions. Finally, the confidence measure is constructed, which can eliminate false targets and the background edges. Experimental results show that the method in this paper can screen out the real LSS-Target in real time with high accuracy under sophisticated background.

Clutter‐suppressing performance estimation methods of active sonar waveform based on reverberation statistical models in littoral environment

Detecting low-speed quiet targets such as divers and underwater-unmanned-vehicles in littoral environment by using active sonar is becoming increasingly attractive. When using a Doppler insensitive pulsed linearly frequency modulated signal, the high-level clutters which might arise from underwater physical scatterers will lead to excessive false alarm rates and limit the detection performance. However, Doppler sensitive waveforms such as binary phase shift keying have the capability of filtering clutters and degrading false alarms. Thus, the question how to estimate the clutter-suppressing performance of waveforms is essential for sonar system design. In this study, the clutter-rejecting principle of waveform is theoretically introduced firstly. Then, based on the reverberation statistical features, this study proposes two methods, the Doppler-statistic method and envelope-statistic method, separately, to estimate and evaluate the clutter suppressing performance of waveforms. Finally, the methods are verified by lake experiments. It is proved that the first method has the capacity of calculating the confidence probability of suppressing clutters by a waveform, and through the second method, the clutter rejecting performance of waveforms can be evaluated and verified. The methods can be used for selecting and designing waveforms to reduce false alarms and improve detection performance.

Sparse recovery-based STAP method using prior information of azimuth-elevation

By exploiting the prior information of azimuth-elevation (AE), a sparse recovery (SR)-based space-time adaptive processing (STAP) method called AESR-STAP is proposed. First, the supercomplete properties matrix in the SR is built based on flight configuration prior information such as elevation and azimuth to estimate the azimuth-elevation spectrum of the ground clutter in different range cells. Then, the range ambiguity STAP clutter can be eliminated according to the difference of the elevation of the clutter in different range cells. Finally, the AESR-STAP utilizes the spatial and temporal coupling relation of the ground clutter, namely, the nonlinear relation among the azimuth, the elevation, Doppler frequency, and spatial frequency, to estimate the clutter distribution in time and space dimensions and to calculate the clutter covariance matrix. Theoretical analysis and simulation experiments have shown that the proposed method can estimate the temporal and spatial distribution more accurately, eliminate the range ambiguity STAP clutter to improve the clutter suppression of the airborne radar, and effectively detect ground low-speed targets.

Detection of weak maneuvering target based on keystone transform and matched filtering process

Detection of weak maneuvering target often suffers from the problems of the range migration (RM) and the Doppler frequency migration (DFM) within the coherent pulse interval. In order to eliminate the RM and DFM, a new coherent integration method based on keystone transform (KT) and matched filtering process (MFP), i.e., KT-MFP, is proposed. Specifically, this method firstly corrects the linear RM caused by the unambiguous velocity via KT and then the MFP, which jointly searching in the fold factor and acceleration domain, is carried out to remove the residual RM and compensate the DFM. At last, coherent integration of the target’s energy is achieved through the slow time Fourier transform (FT). The advantage of the proposed method is that it can obtain superior coherent integration and detection performance for both the slow-moving and fast-moving target, in comparison with the existing algorithms. In addition, KT-MFP could avoid the blind speed sidelobe (BSSL) effect. Numerical simulations are performed to assess the performance from different aspects, i.e., computational complexity, coherent integration for a low-speed target, detection performance for a low-speed target, coherent integration for a high-speed target, detection performance for a high-speed target and coherent integration for multi-targets.

Performance analysis of polarization-space-time adaptive processing for airborne polarization array multiple-input multiple-output radar

In order to further improve the capabilities of clutter suppression and target detection in airborne multiple-input multiple-output (MIMO) radar space-time adaptive processing (STAP), the polarization-space-time adaptive processing (PSTAP) method based on polarization array MIMO radar is proposed. Firstly, by applying the novel polarization array to airborne MMO radar, the signal model of airborne polarization array MIMO radar PSTAP is established. Then based on the idea of resolution grid, the influence of clutter can be equivalent to the formation of independent point sources of clutter related to the clutter degree of freedom, and an equivalent expression for the covariance matrix in polarization array MIMO radar PSTAP is obtained. Next, combined with the equivalent covariance matrix, the signal-to-clutter-plus-noise ratio (SCNR) performance of the polarization array MIMO radar PSTAP is derived and analyzed. The effects of the polarization, spatial and temporal matching coefficients are discussed. When the target is located in the side-looking direction of the airborne radar, the normalized spatial frequency of the target is zero. Then the spatial transmit and spatial receive matching coefficients between the target and the clutter point source in the center of the space-time plane both approach to one. Meanwhile, the normalized Doppler frequency of the side-looking target is in direct proportion to the target speed. When the target speed decreases to zero, the temporal Doppler matching coefficient between the target and the central clutter source is near to one. Thus taking the spatial and temporal matching coefficients into consideration, the SCNR loss of the traditional MIMO-STAP is approximate to zero. It indicates that for traditional MIMO-STAP, its performance of detecting low-speed target is severely degraded by the clutter source, and target detection can hardly be realized just in space-time domains. However, through utilizing the additional polarization information to take advantage of the polarization matching coefficient, the polarization array MIMO radar PSTAP increases the SCNR loss and remarkably lessens the influence of the central clutter source. According to the above theoretical analysis, we can come to the conclusion that the polarization array MIMO radar PSTAP can effectively promote the capability of clutter suppression compared with the traditional MIMO-STAP, which is beneficial to the detection of the moving target with low-speed. Moreover, the improvement of output SCNR performance becomes more significant with increasing the differences between the polarization parameters of target and those of clutter. Therefore, the polarization array MIMO radar PSTAP has great application value for practical engineering. The simulation results verify the validity and superiority of the proposed polarization array MIMO radar PSTAP method.

Batch Version of Extensive Cancellation Algorithm for Clutter Mitigation in Frequency Domain of Passive Radar

Passive radar experiences a significant problem called multipath clutter. The Batch version of the Extensive Cancellation Algorithm (ECA-B) is an efficient method for clutter mitigation. With the increase in signal bandwidth, a greater number of segments is required to cancel the clutter across the entire frequency range. This affects the processing rate, detrimentally weakening and modulating the signal from low-speed targets. Thus, this paper proposes a method that uses ECA-B to process both reference and echo signals in the frequency domain. This method not only reduces the amount of calculation required but also avoids weakening and modulating the target signal, which is spread across many segments. The simulated and experimental data results confirm the correctness and validity of the proposed method.

Adaptive sparse reconstruction with joint parametric estimation for high-speed uniformly moving targets in coincidence imaging radar

As a complementary imaging technology, coincidence imaging radar (CIR) achieves high resolution for stationary or low-speed targets under the assumption of ignoring the influence of the original position mismatching. As to high-speed moving targets moving from the original imaging cell to other imaging cells during imaging, it is inaccurate to reconstruct the target using the previous imaging plane. We focus on the recovery problem for high-speed moving targets in the CIR system based on the intrapulse frequency random modulation signal in a single pulse. The effects induced by the motion on the imaging performance are analyzed. Because the basis matrix in the CIR imaging equation is determined by the unknown velocity parameter of the moving target, both the target images and basis matrix should be estimated jointly. We propose an adaptive joint parametric estimation recovery algorithm based on the Tikhonov regularization method to update the target velocity and basis matrix adaptively and recover the target images synchronously. Finally, the target velocity and target images are obtained in an iterative manner. Simulation results are presented to demonstrate the efficiency of the proposed algorithm.

Research on the Angle Measurement Algorithm for Low-Speed Target of Radar

In this paper, to solve the problem of measuring angle for broadband single-pulse radar, we proposed the method comparing the magnitude combined with comparing phase to measuring angle which has a more accuracy measurement. It improves the resolution of the radar angle. We analyzed the theoretical effectiveness from the perspective of function. For the amplitude and phase information which used in target angle measurement, we use two-dimensional FFT algorithm to extract them. Simulation results show that this method can accurately measure the angle of radar target.