An Efficient Plot Fusion Method for High Resolution Radar Based on Contour Tracking Algorithm

With the development of radar system, the problem of enormous raw data has drawn much attention. A plot fusion method based on contour tracking algorithm is proposed to detect extended targets in a radar image. Firstly, the characteristic of radar image in complex environment is revealed. Then, the steps of traditional method, region growing method, and the proposed method are introduced. Meanwhile, the algorithm of tracking the contour of an extended target is illustrated in detail. It is not necessary to scan all the plots in the image, because the size of target is considered in the proposed method. Therefore, the proposed method is much more efficient than several existing methods. Lastly, the performance of several methods is tested using the raw data of two scenarios in real world. The experiment results show that the proposed method is practical and most likely to satisfy the real-time requirement in various complex environment

Poisson multi-Bernoulli conjugate prior for multiple extended object filtering

This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior for multiple extended object filtering. A Poisson point process is used to describe the existence of yet undetected targets, while a multi-Bernoulli mixture describes the distribution of the targets that have been detected. The prediction and update equations are presented for the standard transition density and measurement likelihood. Both the prediction and the update preserve the PMBM form of the density, and in this sense the PMBM density is a conjugate prior. However, the unknown data associations lead to an intractably large number of terms in the PMBM density, and approximations are necessary for tractability. A gamma Gaussian inverse Wishart implementation is presented, along with methods to handle the data association problem. A simulation study shows that the extended target PMBM filter performs well in comparison to the extended target d-GLMB and LMB filters. An experiment with Lidar data illustrates the benefit of tracking both detected and undetected targets

Converted measurements random matrix approach to extended target tracking using X-band marine radar data

Conventional tracking algorithms rely upon the hypothesis of one detection per target for each frame. However, very fine spatial resolution radars represent widespread systems that provides data for which this hypothesis could be no longer valid. This problem is often called in the literature extended target tracking. In this paper we propose to use the well-established random matrix theory to deal with this issue. A suitable measurement model to address the radar's measurement noise and its conversion into Cartesian coordinates is proposed. The benefits of the proposed converted measurements - extended target tracking with regard to the problem of the targets' size estimation are demonstrated by using both simulated and real data acquired by an X-band marine radar. Average gains of 75% in the estimation of the targets' cross-range size and 31% for the along-range size are observed by comparing the proposed approach with the one that neglects the sensor's noises

Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

Tracking Extended Objects with Active Models and Negative Measurements

Extended object tracking deals with estimating the shape and pose of an object based on noisy point measurements. This task is not straightforward, as we may be faced with scarce low-quality measurements, little a priori information, or we may be unable to observe the entire target. This work aims to address these challenges by incorporating ideas from active contours and exploiting information from negative measurements, which tell us where the target cannot be