A two stage beamforming approach for low complexity CFAR detection and localization for passive radar

An important prerequisite for the radar network detection is that the measurements from local radars are transformed to a common reference frame without systematic or registration errors. For the signal level alignment, only partial signals are available for global decision-making due to power and bandwidth limitations. In this paper, a low-communication-rate spatial alignment in range-Doppler domain is proposed for networked radars without the prior spatial information (positions and attitudes) of radars, which is different from the existing methods in the trajectory domain or echo domain for alignment. To reduce the radar-to-fusion-center communication-rate, the method of initial constant false alarm rate detection is used to censor the signals in range-Doppler domain from local radars. Based on the spatial alignment model for the networked radars in geometry, a maximization problem is formulated. The objective function is the cross-correlation between the range-Doppler domain signals from different local radars. The optimization problem is solved by a genetic algorithm. Simulation results show that the rotation matrix and translation vector are estimated, and the detection probability of the proposed algorithm is improved after alignment and fusion compared with state-of-art methods.

In real time object tracking is an important task in various surveillance application. Nowadays, surveillance system very common in offices, collage campus, shopping malls etc. In this paper, an automated video surveillance system presented. Althrough tracking and counting system are commercially available today; there is need for further reaserach to address the challenges of real world scenarios. There is lot of surveillance cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop a computer vision based technologies that automatically process those images in order to detect situations or unusual behavior. In order to solve the problem of poor real time tracking performance using convolutional neural network. This paper proposes a fast and accurate real time video detection and tracking algorithm. Keyword: - Video surveillance system, Moving object detection, Tracking, Background subtraction. I. Introduction Real time human body detection and tracking in indoor environment deals with the concept of higher level application of image processing and computer vision. The following key components of identify to path of real time tracking are extracting a feature, background image subtraction. If two similar color objects come to close the tracker of one object can jump to associated object. The problem is called hijacking problem. Drifting problem occur when an object abruptly changes its direction to reverse. In that case it’s become very difficult to track the object because the motion model does not work. An object tracking can be understood as the problem of finding the path (i.e. Trajectory).

The poor real-time performance and target occlusion occurred easily when the UAV was tracking the target. In this paper, a target tracking method based on the Back Propagation neural network fusion Kalman filter algorithm was developed to solve the position prediction problem of the UAV target tracking in real time. Firstly, the target tracking algorithm was used to acquire the center position coordinates of the target on the onboard computer, and then the coordinate difference matrix was constructed to train the BP neural network in real time. Secondly, when the target was occluded by the obstacles judged by the Bhattacharyya coefficient, the BP neural network fusion Kalman filter algorithm was used to accurately predict the center position coordinates of the occluded target. Then the flight speed of UAV was calculated by the deviation between the coordinates of the target and the image center. Finally, the velocity command was sent to the UAV by the onboard computer. The experimental results shown that the target position predicted by BP neural network fusion Kalman filter algorithm was more accurate and robust in predicting the center position coordinates of the target, and the UAV can track the moving target on the ground stably.

Being equipped with a millimeter-wave radar allows a low-flying helicopter to sense the surroundings in real time, which significantly increases its safety. However, nonhomogeneous clutter environments, such as a multiple target situation and a clutter edge environment, can dramatically affect the radar signal detection performance. In order to improve the radar signal detection performance in nonhomogeneous clutter environments, this paper proposes a new automatic censored cell averaging CFAR detector. The proposed CFAR detector does not require any prior information about the background environment and uses the hypothesis test of the first-order difference (FOD) result of ordered data to reject the unwanted samples in the reference window. After censoring the unwanted ranked cells, the remaining samples are combined to form an estimate of the background power level, thus getting better radar signal detection performance. The simulation results show that the FOD-CFAR detector provides low loss CFAR performance in a homogeneous environment and also performs robustly in nonhomogeneous environments. Furthermore, the measured results of a low-flying helicopter validate the basic performance of the proposed method.

Radar usually employs several signal processing methods successively for detection. Each processing step influences the radar detection performance. Thus, analysis of the radar detection performance considering the complete signal processing flow provides significant reference for radar detector design. In this paper, with the assumption that matched filtering, square-law detection, non-coherent integration, cell averaging constant false alarm rate detection and double-threshold detection are employed sequentially for radar detection, radar detection performance in K distributed sea clutter plus Rayleigh distributed thermal noise is analyzed. Mathematical expressions of radar false alarm probability and detection probability are derived. Radar detection performances for four types of Swerling fluctuating targets in various circumstances are presented by simulation. And comprehensive effect of K distributed sea clutter plus Rayleigh distributed thermal noise on radar detection performance is analyzed.

Adaptive data processing in modern radar systems is standard approach. The classic kalman filter, extended kalman filter and many other applications are usually used for state estimate and radar target tracking. Signal and data processing technology improvement allows the more effective target tracking in real time. In this paper, we approach the structure and key features of the optimal particle filtering (PF). The optimization criteria for PF are proposed and analyzed. The dependence of mean square error on the number of particles during track prediction by particle filter is discussed. Using this dependence, we were able to determine the minimum required number of particles to correctly predict the trajectory of the target depending on its dynamics (trajectory complexity). The results of off-line real radar data processing are analyzed.

The unmanned aerial vehicles (UAVs) have been extensively used in providing intelligence such as target tracking. In our field experiments, a pre-trained deep neural network (DNN) is deployed on the UAV to identify a target from the captured video frames and enable UAV to keep tracking. However, tracking in real time by the DNN requires a lot of computational resources. This motivates us to consider offloading this type of machine learning (ML) tasks to a mobile edge computing (MEC) server. Specifically, we propose a novel hierarchical ML tasks distribution framework for the UAV tracking system, where the UAV is embedded with lower layers of the pre-trained convolutional neural network (CNN) model due to its limited computing capability, while the MEC server with rich computing resources will handle the higher layers of the CNN model. An optimization problem is formulated to minimize the CNN inference delay while taking into account the communications delay, computing time, and ML error. Insights are provided to understand the tradeoff between communications and ML computing in offloading decisions. Numerical results demonstrate the effectiveness of the proposed ML tasks distribution framework with the optimized offloading strategy.

Traditionally, multiple target tracking and target detection are carried out using separate models, which leads to non-sharing of features and inability to track in real time. Now with the rapid development of target detection and re-identification (re-ID) technology, this paper proposes a conceptually simple and effective joint detection and tracking model. Its detection module and tracking module are isomorphic networks, which are based on anchor- The CenterNet network of the free mechanism is used as the backbone network, and all the convolutional layers in the up sampling are replaced with deformable convolutions, so as to adapt to the geometric deformation of different tracking targets. Second, the tracking module calculates the re-ID feature for each pixel, outputs the calculated similarity between the detection frame and the trajectory frame, uses the Hungarian algorithm and the correlation metric of the measurable bounding box overlap, and uses the Kalman algorithm to perform data association frame by frame. The trajectory filling strategy is used to balance the number of missed and false positives. Finally, experiments are carried out on the open target tracking data set, which proves the superiority and effectiveness of the proposed model, and realizes the tracking of pedestrians, vehicles, bicycles and tricycles in four target categories.

To improve the capability of radar detection in a time-varying maritime environment, an algorithm was proposed and analyzed in this paper that can be applied to determine the shape and scale parameters of the K-distributed model using the radar parameters and environmental information under the context of K-distributed sea clutter, and improved the original algorithm to improve the accuracy of the parameter estimation algorithm. Furthermore, a CFAR detector capable of identifying the characteristics of sea clutter distribution was developed. It can estimate the parameters of K-distributed model for sea clutter in real time using both radar parameters and environmental perception information, thus improving the accuracy of clutter background estimation. When the false alarm rate reaches a certain level, the adaptive threshold factor can be obtained for the CFAR detector proposed in this paper, thus improving the effectiveness of control on the CFAR detector. As suggested by the simulation results, the proposed method outperforms the CFAR detector with fixed parameters in the capability of detection and robustness under the context of K-distributed clutter.

Simple SummaryThe bodily response to cancer treatment is difficult to track in real time. This study aimed to demonstrate the possible application of magnetic resonance imaging (MRI) as a non-invasive and non-radiative modality to evaluate cancer response to therapy. We injected a fluorinated contrast agent in mouse models of breast and colon cancer to tag and track macrophages in real time. Longitudinal tracking of macrophages in tumors following radiation therapy via MRI provided a means of analyzing the tumor microenvironment without the need for a biopsy. It was shown that tumor-associated macrophage recruitment can be quantified through magnetic resonance imaging. Moreover, the findings suggest that macrophage response to radiation therapy is dependent on several factors including tumor origin. Since tumor recurrence following therapy is a major barrier following treatment, this imaging technique will be extremely beneficial in the future analysis of inflammation’s role in that process.Background: Magnetic resonance imaging (MRI) is a non-invasive imaging modality which, in conjunction with biopsies, provide a qualitative assessment of tumor response to treatment. Intravenous injection of contrast agents such as fluorine (19F) nanoemulsions labels systemic macrophages, which can, then, be tracked in real time with MRI. This method can provide quantifiable insights into the behavior of tumor-associated macrophages (TAMs) in the tumor microenvironment and macrophage recruitment during therapy. Methods: Female mice received mammary fat pad injections of murine breast or colon cancer cell lines. The mice then received an intravenous 19F nanoemulsion injection, followed by a baseline 19F MRI. For each cancer model, half of the mice then received 8 Gy of localized radiation therapy (RT), while others remained untreated. The mice were monitored for two weeks for tumor growth and 9F signal using MRI. Results: Across both cohorts, the RT-treated groups presented significant tumor growth reduction or arrest, contrary to the untreated groups. Similarly, the fluorine signal in treated groups increased significantly as early as four days post therapy. The fluorine signal change correlated to tumor volumes irrespective of time. Conclusion: These results demonstrate the potential of 19F MRI to non-invasively track macrophages during radiation therapy and its prognostic value with regard to tumor growth.

Human target detection and tracking have great potential in surveillance, rescue and security applications. Traditional human detection and tracking are performed on the range profile. If targets are close or overlapped in range, it is difficult to distinguish these targets. As velocity provides another aspect to distinguish targets, a novel framework is proposed for human tracking using both range and velocity information. The tracking framework consists of six steps, including clutter reduction, range–Doppler (RD) calculation, target detection, measurement estimation, target localisation and target tracking. Primary attention is devoted to the middle four steps. The calculation process of the RD image is described in detail. The ordered statistics constant false alarm rate detector is extended to a two‐dimensional scenario for the RD target detection. An efficient approach is given for automatic measurement estimation. A minimum root‐mean‐square error pruning algorithm is proposed for multi‐target localisation. As the algorithm combines both range and velocity information for measurement association, it clearly shows a lower wrong association probability than the method using range information only. The effectiveness of the proposed tracking framework is evaluated by the experimental data in the foliage‐penetration environment.

Radiotherapy of moving targets, such as lung, liver or prostate tumors, is prone to treatment errors due to the uncertainties in the target position. Minimizing such error is critical for radiotherapy treatments, especially hypofractionated SBRT treatments. On‐board real time imaging verification and tracking of moving targets has become one of the most exciting areas in radiotherapy. The tremendous developments in this area enable us to improve targeting accuracy and reduce the healthy tissue toxicity, which paves the road to further margin reduction and dose escalation in conventionally fractionated or SBRT treatments.Over the past few years, several real‐time imaging techniques have been clinically implemented, including techniques using ionizing radiation, such as x‐ray based imaging, and radiation‐free techniques such as MRI, imaging based on implanted electromagnetic transponders and optical imaging. The x‐ray imaging techniques are widely accessible in clinics, although they have limited soft tissue contrast and contribute to patient exposure. Recently, MRI has gained wide interests in radiotherapy due to its better soft tissue contrast and no radiation dose. Imaging based on electromagnetic transponders and optical imaging have also gained popularity in many clinical scenarios due to their unique capabilities.Based on these imaging techniques, various tracking approaches have been developed. The room mounted orthogonal 2D x‐ray system (CyberKnife system) was the first medical device capable to perform real‐time target tracking by combining image guidance with robotically targeted radiation delivery. In the Vero system, tumor tracking is achieved by combining image‐guidance with a two‐dimensional pivot assembly of the linear accelerator. In gantry‐based linear accelerators, target motion can be tracked in real time by repositioning the MLC based on the signal from x‐ray images or electromagnetic transponders implanted.The rapid development of new imaging and tracking technologies raises new challenges in revolutionizing our treatment procedures. It becomes important to understand the functionality, limitations and clinical impact of each technique so that they can be chosen wisely for different clinical settings. The goal of this educational session is to compare and contrast different imaging and tracking techniques implemented in CyberKnife, Vero, MRI based, and gantry‐based radiotherapy machines. The general concepts of real‐time imaging and tracking, from imaging the target to adapting the treatment, will be reviewed for each modality. While discussing the technical aspects of the systems including the overall uncertainties and residual imaging and tracking errors, emphasis will be placed on the clinical implementation and workflow. The increased patient dose due to x‐ray imaging and quality assurance procedures will be described. Additionally, the impact of tracking on treatment volume and margin reduction will be discussed. The session will close with an outlook at future developments.Learning Objectives: Understand the opportunities and challenges of real time imaging and tumor tracking. Understand the advantages and limitations of different imaging and tracking techniques, as well as the clinical implementation of each technique. Understand the clinical impact of real time tumor tracking and future directions in this area. Supported by National Institutes of Health, Varian Medical System, Accuray Inc.

The existing tracking and recognition methods concentrate mainly on single‐class targets; however, systems for traffic management or intelligent transport often require multi‐class target tracking and recognition in real time. This study proposes an effective multi‐class moving target recognition method that is based on Gaussian mixture part‐based model, which accurately locates objects of interest and recognises their corresponding categories. The method is multi‐threaded and combines soft clustering approach with multiple mixture part based models to provide stable multi‐class target tracking and recognition in video sequences. The highlight of the method is its ability to recognise multi‐class moving targets and to count their numbers in the video sequence captured by a stationary camera with fixed focal length. Another contribution of this study is that an extended part based model is developed for object recognition in real‐world environments, which can improve the overall system performance, lower time costs, and better meet the actual demand of a video system. Experimental results show that the proposed method is viable in real‐time multi‐class moving target tracking and recognition.

Target detection and tracking is a key technology in intelligent monitoring system. However, it is still difficult to design a robust, accurate and real-time target detection and tracking algorithm. Taking target Tracking as the research focus, this paper focuses on one of the current mainstream algorithms, namely, TLD (tracking-learning-detection) target Tracking algorithm. Based on the theory of TLD target tracking and corner detection, a new target tracking method was proposed to solve the existing problems. A classifier is built online to track and learn the target in real time, and a target contour detector is added in the system to complement the random forest detector to solve the problem of tracking failure. Experimental results show that the proposed algorithm can effectively overcome the influence of fast change or depth occlusion, and improve the tracking efficiency of moving targets.

PurposeUsing 4D magnetic particle imaging (MPI), intravascular optical coherence tomography (IVOCT) catheters are tracked in real time in order to compensate for image artifacts related to relative motion. Our approach demonstrates the feasibility for bimodal IVOCT and MPI in-vitro experiments.Material and methodsDuring IVOCT imaging of a stenosis phantom the catheter is tracked using MPI. A 4D trajectory of the catheter tip is determined from the MPI data using center of mass sub-voxel strategies. A custom built IVOCT imaging adapter is used to perform different catheter motion profiles: no motion artifacts, motion artifacts due to catheter bending, and heart beat motion artifacts. Two IVOCT volume reconstruction methods are compared qualitatively and quantitatively using the DICE metric and the known stenosis length.ResultsThe MPI-tracked trajectory of the IVOCT catheter is validated in multiple repeated measurements calculating the absolute mean error and standard deviation. Both volume reconstruction methods are compared and analyzed whether they are capable of compensating the motion artifacts. The novel approach of MPI-guided catheter tracking corrects motion artifacts leading to a DICE coefficient with a minimum of 86% in comparison to 58% for a standard reconstruction approach.ConclusionsIVOCT catheter tracking with MPI in real time is an auspicious method for radiation free MPI-guided IVOCT interventions. The combination of MPI and IVOCT can help to reduce motion artifacts due to catheter bending and heart beat for optimized IVOCT volume reconstructions.