Moving Target Detection Research Articles

A spawning particle filter for defocused moving target detection in GNSS-based passive radar

Global Navigation Satellite System (GNSS)-based passive radar (GBPR) has been widely used in remote sensing applications. However, for moving target detection (MTD), the quadratic phase error (QPE) introduced by the non-cooperative target motion is usually difficult to be compensated, as the low power level of the GBPR echo signal renders the estimation of the Doppler rate less effective. Consequently, the moving target in GBPR image is usually defocused, which aggravates the difficulty of target detection even further. In this paper, a spawning particle filter (SPF) is proposed for defocused MTD. Firstly, the measurement model and the likelihood ratio function (LRF) of the defocused point-like target image are deduced. Then, a spawning particle set is generated for subsequent target detection, with reference to traditional particles in particle filter (PF) as their parent. After that, based on the PF estimator, the SPF algorithm and its sequential Monte Carlo (SMC) implementation are proposed with a novel amplitude estimation method to decrease the target state dimension. Finally, the effectiveness of the proposed SPF is demonstrated by numerical simulations and preliminary experimental results, showing that the target range and Doppler can be estimated accurately.

Minimum detection velocity performance analysis for air moving target detection in a spaceborne surveillance radar system

AbstractDue to the advantages of wide coverage, continuous remote monitoring, and high measurement accuracy, the spaceborne multi‐channel surveillance radar has been widely used for the air moving target detection and tracking. In this paper, the minimum detection velocity (MDV) definition for the aerial target detection performance evaluation in a spaceborne multi‐channel radar system is analysed, where three kinds of MDV definitions based on empirical formula, output signal‐to‐clutter‐plus‐noise ratio (SCNR) loss criterion, and output SCNR criterion are discussed in detail based on theoretical analysis and simulation verification. The analysis results will provide valuable reference for the practical spaceborne radar system designment with the air target detection mode.

Maritime moving target detection and localisation technique for Global Navigation Satellite Signals‐based passive multistatic radar

AbstractThis article puts forward a ship target detection and localisation technique for GNSS‐based passive multistatic radar. The approach capitalises on the large spatial diversity offered by navigation satellite constellations to provide maritime awareness using short integration time windows, thus bypassing the shortcomings suffered by approaches relying on long dwells typically considered in satellite‐based passive radar systems. The proposed approach entirely operates on the Cartesian plane and it is able to provide in a single stage the detection and localisation of the target in the surveyed area. Theoretical and simulated performance analysis are provided to illustrate as the proposed approach can outperform conventional two‐stages (i.e. bistatic detection then localisation) procedures, with particular regard to the case of significant radar cross section scintillation, likely experienced in the system under consideration. The effectiveness of the approach has been verified via experimental data acquired in a few scenarios of interest comprising opportunistic targets belonging to different types.

Joint fuzzy background and adaptive foreground model for moving target detection

Joint fuzzy background and adaptive foreground model for moving target detection

A Quasi-Coherent Detection Method Based on Radon–Fourier Transform Using Multi-Frequency-Based Passive Bistatic Radar

Passive bistatic radar (PBR)-based moving target detection (MTD) has benefited greatly from multi-frequency (MF) integration, which can effectively improve the detection capability of weak targets. However, with the increase in the coherent processing interval (CPI) and carrier-frequency separation, Doppler spread will appear in the range-Doppler maps (RDMs) over different frequency bands, which severely limits the processing gain of MF integration. In this paper, a novel MTD algorithm is proposed to achieve both long-time integration and quasi-coherent MF integration. More specifically, the proposed method consists of two main steps, where a modified Radon–Fourier transform (RFT), termed as MF-based RFT (MF-RFT), is, firstly, used to eliminate the Doppler spread via designing a sequential of MF-based Doppler filter banks. Following the MF-RFT, a phase-compensation-based method is also developed to further remove the residual phase errors. This method involves formulating an optimization problem based on the minimum-entropy criterion and employing a particle swarm optimization (PSO) algorithm to solve it, after which quasi-coherent MF integration can be achieved with robustness. Both numerical results and field test results based on digital video broadcasting-satellite (DVB-S) signals demonstrate that the proposed algorithm outperforms the existing methods in the scenario of weak MTD.

Underwater weak moving target detection method based on wideband Multi-pulse coherent integration

When active sonar detects a weak moving target in a noisy background, coherent integration can be used to improve the signal-to-noise ratio of echo. The active sonar often transmits wideband signals and the target velocity ambiguity often occurs, in this case, the conventional coherent integration method makes the echo energy disperse and coherent integration gain decrease due to non-constant ambiguity factors in the bandwidth. To solve this problem, an underwater weak moving target detection method using multi-pulse coherent integration based on the wideband keystone transform is proposed. To compensate the non-constant ambiguity factors, a phase compensation function is constructed by replacing a single ambiguity factor with a set of ambiguity factors in the wideband keystone transform. After the proposed method, a well-focused image can be obtained in the delay-Doppler domain, and the coherent integration peak is detected to estimate delay and Doppler of the weak moving target. Simulations and lake test results verify the effectiveness of the proposed method, and show that the coherent integration gain with the proposed method is ca. 7–8 dB higher than that with the conventional method. The simulation results of integrating thirty pulses show that when the detection probability is greater than 0.7, the root-mean-square error of delay estimation of the proposed method is about 1/8 of that of the conventional method, and the root-mean-square error of Doppler estimation is about 1/3 of that of the conventional method, indicating that the proposed method has better parameter estimation accuracy.

Time Reversal Detection for Moving Targets in Clutter Environments

Moving target detection plays a pivotal role in many applications, especially in radar systems. In addition, time-reversal (TR) technology can improve radar detection probability by effectively solving the problem of multipath signals. However, detection performance can be affected when clutter exists and target Doppler shifts are unknown. For this reason, we propose a novel moving target detection algorithm that leverages TR technology and a novel waveform optimization method in clutter and multipath signal environments. In this paper, we establish a TR signal model for a moving target in such environments and derive the TR average likelihood ratio test (TR-ALRT) detector. Additionally, we introduce a waveform optimization method that adapts to clutter and unknown Doppler information further to enhance the performance of the TR-ALRT detector. Consequently, a TR-ALRT waveform optimization (TR-ALRT-WO) detector is derived to adapt to the unknown Doppler and environment information, thereby improving the detection probability. Comparative analysis with other detectors under the same conditions validates the effectiveness of our proposed TR-ALRT-WO detector. Furthermore, numerical experiments demonstrate the superior performance of the proposed detection algorithm.

A GPU‐based real‐time processing system for frequency division multiple‐input‐multiple‐output radar

AbstractMultiple‐Input‐Multiple‐Output (MIMO) radar has the characteristic of multiple antenna channels, bringing on a big data volume for signal processing. Therefore, the trade‐off between detection ability and computational efficiency is always considered. In this article, an optimisation system is proposed to enhance the real‐time performance of frequency division MIMO radar without compromising accuracy. For the scenario of low‐altitude small target detection, a signal processing acceleration method is proposed and a MIMO radar optimisation system based on graphics processing unit (GPU) architecture is built. The signal model of frequency division MIMO radar is first established, improving the classical signal processing flow efficiency from the perspective of reducing fast Fourier transform (FFT) times and windowing operation times. To achieve an advanced acceleration, the parallel architecture of ArrayFire‐library in GPU is then employed. Distinct minimum parallel units are extracted by analysing the principle of digital beamforming (DBF), pulse compression, moving target detection (MTD), and constant false‐alarm rate (CFAR) algorithms. And the corresponding parallel algorithms are designed to constitute a parallel acceleration system of frequency division MIMO. Simulation results indicate that the proposed method significantly improves the efficiency of MIMO system with a maximum acceleration ratio of 65 times, meeting the real‐time processing requirements.

Moving Target Detection Algorithm for Dynamic Image Sequences on The Basis of Artificial Neural Network

<p>Due to the large changes in dynamic image sequence frames and the complex detection scene, it is difficult to accurately detect moving objects. Therefore, the study proposes a moving target detection algorithm based on artificial neural network. First, the algorithm performs standardized grayscale processing and gamma correction processing on the dynamic image to eliminate the noise interference of the dynamic image. After that, the model calculates the gradient of the dynamic image in order to complete the feature extraction of the dynamic image. Then, according to the result of hog feature extraction, the study adopts the inter-frame calculation method to update the background of the dynamic image. Finally, the principle and structure of the neural network are analyzed experimentally, and a channel attention mechanism is introduced to train dynamic image sequences to obtain MTD results. Experimental results show that the proposed algorithm achieves higher accuracy in MTD than conventional detection algorithms. The calculation efficiency of the algorithm in this paper has significant advantages, and the average detection time is 3.69515ms, which can meet the real-time requirements of MTD.</p> <p> </p>

APO-ELM Model for Improving Azimuth Correction of Shipborne HFSWR

Shipborne high-frequency surface wave radar (HFSWR) has a wide range of applications and plays an important role in moving target detection and tracking. However, the complexity of the sea detection environment causes the target signals received by shipborne HFSWR to be seriously disturbed by sea clutter. Sea clutter increases the difficulty of azimuth estimation, resulting in a challenging problem for shipborne HFSWR. To solve this problem, a novel azimuth correction method based on adaptive boosting error feedback dynamic weighted particle swarm optimization extreme learning machine (APO-ELM) is proposed to improve the azimuth estimation accuracy of shipborne HFSWR. First, the sea clutter is modeled and simulated. Then, we study its characteristics and analyze the influence of its characteristics on the first-order clutter spectrum and target detection accuracy, respectively. In addition, the proposed improved particle swarm optimization (PSO) and adaptive neuron clipping algorithm are used to optimize the input parameters of the ELM network. Then, the network performs error feedback based on the optimized parameter performance and updates the feature matrix, which can give a minimum clutter-error estimation. After that, it iteratively trains multiple weak learners using the adaptive boosting (AdaBoost) algorithm to form a strong learner and make strong predictions. Finally, after error compensation, the best azimuth estimation results are obtained. The sample sets used for the APO-ELM network are obtained from field shipborne HFSWR data. The network training and testing features include the wind direction, sea current, wind speed, platform speed, and signal-to-clutter ratio (SCR). The experimental results show that this method has a lower root-mean-square error than the back-propagation neural network and support vector regression (SVR) azimuth correction methods, which verifies the effectiveness of the proposed method.

Effective Non-Stationary Clutter Suppression Method via Elevation Oblique Subspace Projection for Moving Targets Detection with a Space-Based Surveillance Radar

The clutter becomes non-stationary for a space-based surveillance radar (SBSR), which is harmful for the moving targets detection due to the earth’s rotation. The non-stationarity will degrade the accuracy of clutter covariance matrix (CCM) estimation and increase the clutter degree of freedom (DOF), thereby degrading the performance of clutter suppression. To solve this problem, this paper proposes a novel non-stationary clutter suppression method using an elevation oblique subspace projection method. After analyzing the range ambiguity and non-stationarity of the clutter, the proposed method utilized the oblique projection matrix to project the signal onto the subspace spanned by the near-range and far-range clutter components along the subspace spanned by the main lobe clutter component. Then, the projected signal was used to estimate the elevation covariance matrix and calculate the optimal weight vector for the elevation adaptive filter. The proposed method can suppress the non-stationary clutter effectively with a higher improvement factor (IF) and a narrower main lobe width. Finally, the simulation results were given to verify the correctness and effectiveness of the proposed method.

Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios.

Moving target detection (MTD) is a crucial task in computer vision applications. In this paper, we investigate the problem of detecting moving targets in infrared (IR) surveillance video sequences captured using a steady camera in a maritime setting. For this purpose, we employ robust principal component analysis (RPCA), which is an improvement of principal component analysis (PCA) that separates an input matrix into the following two matrices: a low-rank matrix that is representative, in our case study, of the slowly changing background, and a sparse matrix that is representative of the foreground. RPCA is usually implemented in a non-causal batch form. To pursue a real-time application, we tested an online implementation, which, unfortunately, was affected by the presence of the target in the scene during the initialization phase. Therefore, we improved the robustness by implementing a saliency-based strategy. The advantages offered by the resulting technique, which we called "saliency-aided online moving window RPCA" (S-OMW-RPCA) are the following: RPCA is implemented online; along with the temporal features exploited by RPCA, the spatial features are also taken into consideration by using a saliency filter; the results are robust against the condition of the scene during the initialization. Finally, we compare the performance of the proposed technique in terms of precision, recall, and execution time with that of an online RPCA, thus, showing the effectiveness of the saliency-based approach.

DPCA-Based Doppler Radar Measurements from Space: Effect of System Errors on Velocity Estimation Performance

Abstract The displaced phased center antenna (DPCA) method of clutter cancellation for ground moving target detection from airborne platforms has been in use for a number of decades. Application of the DPCA method for spaceborne Doppler weather radar velocity estimation was suggested in 2007. The initial description and analysis of the technique was followed several years ago by demonstration using a multiantenna airborne radar. Recent reviews of methods and technology for spaceborne cloud and precipitation radar have also mentioned possible use of DPCA. However, to date, analyses of the application of DPCA to spaceborne Doppler weather radar have assumed that the two channels and antennas are identical, including perfect alignment, and that the DPCA condition is well-satisfied. This study uses simulation to examine the effects of relaxing these assumptions. The simulation method and its validation are discussed, with companion analytical calculations in the appendix. Next, simulations are used to show the effects on the Doppler estimates from errors in pointing and positioning relative to the ideal DPCA. The DPCA technique is relatively robust to possible errors, indicating that a practical DPCA radar system can provide precise Doppler measurements from space. Significance Statement Analytical and simulation results show that the displaced phase center antenna approach can enable spaceborne atmospheric Doppler radar measurements with good accuracy, even in the presence of antenna mispointing and other system errors.

Sports health information prediction system based on deep learning network

AbstractThis paper adopts the deep network model constructed the results of the training are used to explore the detection of sports, and to verify the deep learning network model from the perspective of reliability and feasibility. The experimental results in this paper show that the comprehensive performance evaluation index FM increased by 2.6%, Pr increased by 0.7%, and Re increased by 4.4%. Therefore, the deep residual network structure used in the DRNTL method proposed in this paper can effectively improve the generalization ability of the network. Through the learning of a large amount of labeled data, the model can be applied to the detection of other untrained complex scenes. The engineering of the moving target detection method is of great significance.

Moving Target Detection Using CA, SO and GO-CFAR detectors in Nonhomogeneous Environment

Modernization of radar technology and improved signal processing techniques are necessary to improve detection systems in complex situations. A fundamental problem in radar systems is to automatically detect targets while maintaining a desired constant false alarm probability. This work studies two detection approaches, the first with a fixed threshold and the other with an adaptive one. In the latter, we have learned the three types of detectors CA, SO, and GO-CFAR. This research aims to apply intelligent techniques to improve detection performance in a nonhomogeneous environment using standard CFAR detectors. The objective is to maintain the false alarm probability and enhance target detection by combining intelligent techniques. With these objectives in mind, implementing standard CFAR detectors is applied to nonhomogeneous environment data. The primary focus is understanding the reason for the false detection when applying standard CFAR detectors in a nonhomogeneous environment and how to avoid it using intelligent approaches

Long-time coherent integration algorithm for high-speed maneuvering target detection

Detection of high-speed maneuvering targets along with motion parameters estimation (MPE) are some of the challenging problems for modern radar systems. Such targets usually induce linear range migration (LRM), quadratic range migration (QRM), and Doppler frequency migration (DFM) problems in signal processing, resulting in severe performance degradation, especially when long time integration is needed for weak target detection. We propose an algorithm for coherent integration and MPE by combining the techniques of generalized dechirp (GD) and new axis rotation moving target detection (NAR-MTD), i.e., the GD-NAR-MTD algorithm, to solve the above problems. Specifically, the GD and NAR approaches can not only correct LRM, QRM, and DFM simultaneously with reduced rotation error in coordinate system transformation but also can abate the computational burden via frequency-domain implementation with rounding and complex addressing operations avoided, so the target’s echo energies can be well accumulated by MTD finally. Both simulation and practical radar experiments are conducted to validate the proposed algorithm. At the same time, the results by the other six typical algorithms are compared for demonstrating the superior performance of the proposed algorithm on detection and MPE. In addition, the equivalence between the practical radar experiment and the simulation experiment is analyzed by comparing the induced RMs and DFMs, which shows that the scaled equivalent experiment is persuasive in algorithm validation.

Static High Target-Induced False Alarm Suppression in Circular Synthetic Aperture Radar Moving Target Detection Based on Trajectory Features

The new mode of Circular Synthetic Aperture Radar (CSAR) has several advantages including multi-aspect and long-time observation, which can generate high-frame-rate image sequences to detect moving targets with a single-channel system. Nonetheless, due to CSAR being sensitive to 3D structures, static high targets are observed in scene display rotational motion within CSAR subaperture image sequences. Such motion can cause false alarms rising when utilizing image sequence-based moving target detection methods like logarithm background subtraction (LBS). To address this issue, this paper first thoroughly analyzes the moving target and static high target’s difference for the trajectory in an image sequence. Two new trajectory features of the rotation angle and moving distance are proposed to differentiate them. Based on the features, a new false alarm suppression method is proposed. The method first utilizes LBS to obtain coarse binary detection results comprising both moving and static high targets, then employs morphological filtering to eliminate noise. Next, DBSCAN and target tracking steps are employed to extract the trajectory features of the target and false alarm. Finally, false alarms are suppressed with trajectory-based feature discriminators to output detection results. The W-band CSAR open dataset is used to validate the proposed method’s effectiveness.

A fractional-order visual neural model for small target motion detection

Detecting small moving targets consisting of one or few pixels is technically demanding due to their limited visual features. Motivated by nature, some bio-inspired models have been developed that simulate the behavior of small target motion detectors (STMDs), a class of specialized neurons found in insects’ visual neural systems dominating in detecting small moving targets during activities such as predation or courtship. However, the existing models’ high dependence on the sampling frequency of input videos becomes a bottleneck that seriously hinders their real-time applications in the physical world. This is because massive computational power is required to capture and process high-sampling-frequency videos. While model detection performance in low-sampling-frequency videos is plagued by significant spatial errors and weak responses. To address these issues, we propose an STMD-based visual neural model with a fractional-order difference operator for small target motion detection. The fractional-order operator captures instantaneous luminance change and integrates it with memory information, where the instantaneous information dominates the integrated signal. The STMD network further separates the rising and falling luminance components, which are aligned in the time domain and then multiplied to predict the location of moving small targets. Due to the rapid response of instantaneous information and the supplement of memory information, the proposed model locates the small moving targets accurately and robustly in low-sampling-frequencies. Numerical experiments show that the proposed model significantly improves the detection performance for low-sampling-frequency videos.

Doppler shift compensation algorithm based on inverse synthetic aperture ladar in binary phase-coded signal

The binary phase-coded signal is used in inverse synthetic aperture ladar (ISAL) as a commonly used pulse compression signal. However, due to its sensitivity to doppler shift, it will lead to poor or even uncompressible pulse compression results. Combined with the principle of inverse synthetic aperture ladar, the doppler shift introduced by target motion will have a very significant impact, further degrading the imaging results. By using the digital moving target detection (DMTD) method, the doppler information of each distance unit is extracted by performing Fourier transform on the same distance unit, and then a compensation factor is constructed in the frequency domain to compensate for this doppler, correcting the adaptation problem brought by it and improving the imaging result.

A Systematic Solution for Moving-Target Detection and Tracking While Only Using a Monocular Camera

This paper focuses on moving-target detection and tracking in a three-dimensional (3D) space, and proposes a visual target tracking system only using a two-dimensional (2D) camera. To quickly detect moving targets, an improved optical flow method with detailed modifications in the pyramid, warping, and cost volume network (PWC-Net) is applied. Meanwhile, a clustering algorithm is used to accurately extract the moving target from a noisy background. Then, the target position is estimated using a proposed geometrical pinhole imaging algorithm and cubature Kalman filter (CKF). Specifically, the camera’s installation position and inner parameters are applied to calculate the azimuth, elevation angles, and depth of the target while only using 2D measurements. The proposed geometrical solution has a simple structure and fast computational speed. Different simulations and experiments verify the effectiveness of the proposed method.