Multiple Moving Targets Detection Research Articles

Maritime Multiple Moving Target Detection Using Multiple-BDS-Based Radar: Doppler Phase Compensation and Resolution Improvement

With the realization of global navigation satellite system (GNSS) completion, GNSS reflectometry (GNSS-R) has become increasingly popular due to the advantages of global coverage and the availability of multiple sources in terms of earth remote sensing. This paper analyzes the Beidou navigation satellite system (BDS) signal reflection detection of multiple satellites and multiple moving targets under multiple-input and multiple-output (MIMO) radar systems and proposes a series of methods to suppress multiple Doppler phase influences and improve the range detection property. The simulation results show the restored target peaks, which match the RCS data more accurately, with the GNSS-R Doppler phase influence removed, which proves the proposed method can improve target recognition and detection resolution performance.

Study of multiple moving targets’ detection in fisheye video based on the moving blob model

This paper discussed some improved algorithms for multiple moving targets detection and tracking in fisheye video sequences which based on the moving blob model. The view field of fisheye lens achieved 183 degree which used in our system, so it has more effective in the no blind surveillance system. However, the fisheye image has a big distortion that makes it difficult to achieve an intelligent function. In this paper we try to establish a moving blob model to detect and track multiple moving targets in the fisheye video sequences, in order to achieve the automation and intelligent ability for no blind surveillance system. It is divided into three steps. Firstly, the distortion model of fisheye lens was established, we are discussing the character of the imaging principle of fisheye lens, and calculate the distortion coefficient which can be used in the moving blob model. Secondly, the principle of the moving blob model was analyzed in detail which based on the fisheye distortion model. It was included four main algorithms, which the first is the traditional algorithm of background extraction; and the background updating algorithm; the algorithm of the fisheye video sequence with the background subtracted in order to get the moving blobs; the algorithm of removing the shadow of blobs in RGB space. Thirdly, we determined that every extracted blob is a real moving target by calculating the pixels with a threshold, which can discard the faulty moving targets. Lastly, we designed the algorithm for tracking the moving targets based on the moving blobs selected through calculating the geometry center. The experiment indicated that every algorithm has a better processing efficiency of multiple moving targets in fisheye video sequences. Compared the traditional algorithm, the improved algorithm can be detected the moving target in a circular fisheye image effectively and stably.

Range sidelobe suppression for using Golay complementary waveforms in multiple moving target detection

We describe a method for processing radar signals, combining the outputs of two – a Weighted average Doppler algorithm and an existing Binominal Design algorithm via a point-wise minimum processor. The method renders a better performance in range sidelobe suppression for the detection of multiple moving targets using Golay complementary waveforms than the existing Binominal Design algorithm. The effectiveness of the method is justified by analyzing the corresponding ambiguity functions. We also show the performance of the new signal processing method by simulations under a realistic scenario.

A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion Imagery via Cloud and Graphic Processing Units.

This paper presents the first attempt at combining Cloud with Graphic Processing Units (GPUs) in a complementary manner within the framework of a real-time high performance computation architecture for the application of detecting and tracking multiple moving targets based on Wide Area Motion Imagery (WAMI). More specifically, the GPU and Cloud Moving Target Tracking (GC-MTT) system applied a front-end web based server to perform the interaction with Hadoop and highly parallelized computation functions based on the Compute Unified Device Architecture (CUDA©). The introduced multiple moving target detection and tracking method can be extended to other applications such as pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis. The cloud and GPUs based computing provides an efficient real-time target recognition and tracking approach as compared to methods when the work flow is applied using only central processing units (CPUs). The simultaneous tracking and recognition results demonstrate that a GC-MTT based approach provides drastically improved tracking with low frame rates over realistic conditions.

Multiple Moving Targets Detection and Parameters Estimation in Strong Reverberation Environments

This paper considers the problem of multiple moving targets detection and parameters estimation (direction of arrival and range) in strong reverberation environments. As reverberation has a strong correlation with target echo, the performance of target detection and parameters estimation is significantly degraded in practical underwater environments. In this paper, we utilize two uniform circular arrays to receive plane wave of the linear frequency modulation signal reflected from far-field targets. On the basis of received signal, we build a variance matrix of multiple beams by using modal decomposition, conventional beamforming, and fractional Fourier transform (FrFT). We then propose a novel detection method and an estimation method of parameters based on the constructed image. A significant feature of the proposed methods is that our design does not involve any a priori knowledge about targets number and parameters of marine environments. Finally, we demonstrate via numerical simulation examples that the detection probability and the accuracy of estimated parameters of the proposed method are higher than the existing methods in both low signal-to-reverberation ratio and signal-to-noise ratio environment.

Improvement in Moving Target Detection Based on Hough Transform and Wavelet

ABSTRACTThe most fundamental problem in radar signal is the detection of a moving object or a physical phenomenon. This problem became more challenging to the signal processing and radar communities under severe interference. In order to effectively detect moving targets in strong noise conditions, we proposed a new method based on wavelet and Hough transform for multiple moving targets detection. First, the noise is removed from the received signal by the wavelet technique. We propose an improved wavelet threshold function. The new threshold function not only reduces constant error of soft thresholding but also improves the discontinuity of hard thresholding. Second, the image processing technique of the Hough transform is used. The benefits of this method include improved performance of detection and implementation flexibility, which indicates the effectiveness and robustness of the algorithm.

Biologically inspired multilevel approach for multiple moving targets detection from airborne forward-looking infrared sequences

In this paper, a biologically inspired multilevel approach for simultaneously detecting multiple independently moving targets from airborne forward-looking infrared (FLIR) sequences is proposed. Due to the moving platform, low contrast infrared images, and nonrepeatability of the target signature, moving targets detection from FLIR sequences is still an open problem. Avoiding six parameter affine or eight parameter planar projective transformation matrix estimation of two adjacent frames, which are utilized by existing moving targets detection approaches to cope with the moving infrared camera and have become the bottleneck for the further elevation of the moving targets detection performance, the proposed moving targets detection approach comprises three sequential modules: motion perception for efficiently extracting motion cues, attended motion views extraction for coarsely localizing moving targets, and appearance perception in the local attended motion views for accurately detecting moving targets. Experimental results demonstrate that the proposed approach is efficient and outperforms the compared state-of-the-art approaches.

Moving Objects Detection Based on the Precise Background Compensation Under Dynamic Scene

In order to detect the moving object under dynamic scenes accurately, this paper proposes a moving object detection method which is based on the precise background compensation. First, the SURF (Speeded-Up Robust Features) algorithm is used to extract feature points, and the BBF(Best-Bin-First) search algorithm is adopted to match feature points, in addition the reverse constraint strategy is employed to remove the mismatching points. To enhance the accuracy of the background compensation, a dynamic threshold is set to remove the target feature points influence for the global motion parameters. At last, a combination of interval three-frame- -differencing and morphological method is used to detect the moving objects. Experimental results show that this method has good performance on multiple moving targets detection.

Real-Time Multiple Moving Targets Detection from Airborne IR Imagery by Dynamic Gabor Filter and Dynamic Gaussian Detector

This paper presents a robust approach to detect multiple moving targets from aerial infrared (IR) image sequences. The proposed novelmethod is based on dynamic Gabor filter and dynamic Gaussian detector. First, the motion induced by the airborne platform is modeled by parametric affine transformation and the IR video is stabilized by eliminating the background motion. A set of feature points are extracted and they are categorized into inliers and outliers. The inliers are used to estimate affine transformation parameters, and the outliers are used to localize moving targets. Then, a dynamic Gabor filter is employed to enhance the difference images for more accurate detection and localization of moving targets. The Gabor filter's orientation is dynamically changed according to the orientation of optical flows. Next, the specular highlights generated by the dynamic Gabor filter are detected. The outliers and specular highlights are fused to indentify the moving targets. If a specular highlight lies in an outlier cluster, it corresponds to a target; otherwise, the dynamic Gaussian detector is employed to determine whether the specular highlight corresponds to a target. The detection speed is approximate 2 frames per second, which meets the real-time requirement of many target tracking systems.

APPROACH OF MULTIPLE MOVING TARGETS DETECTION FOR MICROWAVE SURVEILLANCE SENSORS

Multiple moving targets detection is one of the fundamental problems in information acquisition. In this paper, the use of a transformable period and symmetrical linear frequency modulated (TPS-LFM) waveform for microwave surveillance sensor multiple moving targets identification, is proposed. In order to accurately estimate target's true position and velocity, a relatively unknown yet powerful technique, the so-called fractional Fourier transform (FrFT), is applied to estimate the moving target parameters. By mapping a target's signal onto a fractional Fourier axis, the FrFT permits a constant-velocity target to be focused in the fractional Fourier domain thereby affording orders of magnitude improvement in signal-clutter-ratio. Moving target velocity and position parameters are derived and expressed in terms of an optimum fractional angle and a measured fractional Fourier position, allowing a target to be accurately located. Moreover, to resolve the problem whereby weak targets are covered by the sidelobes of strong ones, the CLEAN technique is also applied. Simulation results show that the method is effective in estimating target velocity and position parameters for microwave surveillance sensors.