Sensor Management Issues for SAR Target Tracking and Identification
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Sensor-management in tracking consists of sensor mode control and scheduling, target selection, and situation assessment. In a dynamic environment, airborne radar necessitates active mode control for the acquisition of a synthetic aperture radar (SAR) image of stationary targets. This paper discusses the control, fusion, and management of SAR sensors for target tracking and identification. 1.0 Introduction An airborne or spaceborne platform, that includes radar, requires active control for determining when to collect and integrate radar scans to form a SAR image as shown in Figure 1 and Figure 2. Ground Track Spotlight Mode Figure 1. Spotlight Mode Radar. h b 0 3 3 3 3 . 0 0 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 2 0Keywords:
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This paper presents a single-sensor multiple-target tracking system applicable with air surveillance radars. We describe the development process along with key details of system's design and implementation, including heuristic process of choosing system parameters to meet tracking performance requirements. In addition, we provide evaluation of system's real-time performance on general purpose computers.
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A tactical pilot typically experiences difficulty in maintaining accurate identification on multiple-interacting targets in the presence of clutter. We propose a multilevel feature-based association (MFBA) algorithm to aid a pilot in a dynamic multi-target environment. We investigate MFBA for an air-to-ground scenario in which a plane, equipped with a high-range resolution radar sensor, processes kinematic and target features at different levels, and fuses these features to simultaneously track and identify targets.
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Moving target shadows continuously appear in the sequential images produced by video synthetic aperture radar (SAR), which conducts research on shadow-based detection and tracking of moving targets. This article addresses a typical shadow-based detection and tracking problem of dim high-maneuvering targets in the complex background. Building upon the dynamic programming-based track-before-detect (DP-TBD) algorithm, this article proposes a joint-processing-strategy-based DP-TBD (JP-DP-TBD) algorithm to detect and track multiple high-maneuvering targets in video SAR. Benefiting from both of the location and radial velocity information of the targets in the video SAR image and range-Doppler (RD) spectrum, the JP-DP-TBD algorithm can screen the state candidate region and retain more states that conform to the motion law of the target. Meanwhile, we adopt a dual-frame close-range matching mechanism to complete the automatic initialization of the candidate target state, whose validity has been verified on the real video SAR data. Experimental results on simulated data demonstrate that the proposed algorithm has better detection ability and fewer false alarms in comparison with other TBD algorithms.
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A sensor selection algorithm for a multisensor system which aims at minimization of the system load is presented. The algorithm selects sensor, time of the measurement and dwell time for target tracking in a way to minimize sensor resources involved while providing a required level of tracking performance. The algorithm performance is illustrated by means of a case study.
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This paper presents a novel approach for target detection in radar imagery, which combines an object detector and a multi target particle filter tracker. Object detection is implemented using deep neural networks, as opposed to the traditional radar object detection methods. This technique is applied to a dataset collected with a 79 GHz FMCW radar mounted on a vehicle. In this approach, object detection and tracking of roadside objects are performed in an alternating fashion to reduce the computational load required by the real time processing. The results and the thorough analysis of the parameters showed that this approach is feasible and can be successfully utilised in radar imagery for autonomous driving.
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Modern airborne ground surveillance systems are able to survey extended areas with excellent coverage, due to the optimal line of sight realized by the operating altitude. These surveillance systems or networks generate a high amount of data and it is a challenge to support the operator in the related situation assessment. Automatic tracking of the individual targets and the correlation between targets and geographical items is a first step for the interpretation of the ground picture. Data fusion is essential to take advantage of complementary information for classification and identification. Target aggregation like convoy detection and group tracking helps to focus the attention of the operators and to manage the big data problem. Higher level data fusion condensates the information again, e.g. for traffic flow analysis. Finally, the automated processes are required to the management of sensors mounted on stationary, airborne or space based platforms.
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Recent developments in high resolution sensors have encouraged the use of Extended Target Tracking (ETT) algorithms specifically designed to deal with targets that generate more than one detection per frame. At the same time, the availability of more powerful computational resources enable the use of soft computing techniques that yield a target probability, instead of a hard decision. This paper proposes a realistic target model feasible for an Extended Target - Track before Detect framework. Physical phenomena related to the acquisition of high resolution X-band marine radar data are considered. Real radar data is used to assess the superior performance of the featured model with respect to previous approaches. Results show that the realistic model provides better estimations of the target velocity and size.
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This paper presents a radar and camera sensor fusion framework as a vulnerable road user (VRU) perception system that can automatically detect, track and classify different targets on the road. The first module of the system performs a spatial-temporal alignment on a common plane of detections provided by the radar signal processing and video processing modules. The second module is dedicated to data association of the aligned detections. A centralized fusion algorithm takes the current aligned detection set (locations and labels) as inputs from both sensors and performs multi-object tracking with a joint probabilistic data association (JPDAF) algorithm underlying the Kalman filter. The proposed radar/camera fusion system is experimentally evaluated through multi-object tracking scenarios. The experimental results demonstrate its reliability and effectiveness compared to a single sensor system.
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The traditional traffic speed measuring radar of FSK mode has inherent disadvantages that it cannot identify moving targets with the same speed. So in practical applications, we need to install the radar sensor in each lane for accurate measurements and tracking. In this paper, we put forward a new method to detect and track multiple targets using only one radar sensor by establishing a spatial model and analyzing characteristics of radar data between two adjacent frames. Meanwhile, we analyze the problems that may be encountered in practice when using this method and provide solutions. In the end, the feasibility of the method is verified by simulation.
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