A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements

Abstract

<jats:p>Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD).</jats:p>

OL-SLAM: A Robust and Versatile System of Object Localization and SLAM

“…We tend to fuse other sensors such as LiDAR or Radar to locate the target because monocular cameras lack depth information. Yifang [ 21 ] proposes the use of RADAR and Infrared sensor (IR) information for tracking and estimating target state dynamics. To project image-based object detection results and LiDAR-SLAM results onto a 3D probability map, Gong et al [ 22 ] combine visual and range information into a frustum-based probabilistic framework.…”

A New Pseudolinear Filter for Bearings-Only Tracking without Requirement of Bias Compensation

“…Target tracking has been researched for decades with a wide range of applications in civilian and military areas. It refers to estimate a moving target’s state using the noise-corrupted measurements collected by one or more sensors at fixed locations or on moving platforms [ 1 , 2 , 3 , 4 , 5 ]. The typical measurements include target range, Doppler velocity and bearing angles, while in passive bearings-only tracking (BOT) systems [ 6 , 7 , 8 , 9 , 10 , 11 ], the sensors listen for signals emitted by a target and only acquire the bearing data.…”