Multitarget tracking via restless bandit marginal productivity indices and Kalman Filter in discrete time

This paper designs, evaluates, and tests a tractable priority-index policy for scheduling target updates in a discrete-time multitarget tracking model, which aims to be close to optimal relative to a discounted or average performance objective accounting for tracking-error variance and measurement costs. The policy is to be used by a sensor system composed of M phased-array radars coordinated to track the positions of N targets moving according to independent scalar Gauss-Markov linear dynamics, which therefore allows for the use of the Kalman Filter for track estimation. The paper exploits the natural problem formulation as a multiarmed restless bandit problem (MARBP) with real-state projects subject to deterministic dynamics by deploying Whittle's (1988) index policy for the MARBP. The challenging issues of indexability (existence of the index) and index evaluation are resolved by applying a method recently introduced by the first author for the analysis of real-state restless bandits. Computational results are reported demonstrating the tractability of index evaluation, the substantial performance gains that the Whittle's marginal productivity (MP) index policy achieves against myopic policies advocated in previous work and the resulting index policies suboptimality gaps. Further, a preliminary small scale computational study shows that the (MP) index policy exhibits a nearly optimal behavior as the number of distinct objective targets grows with the number of radars per target constant.Multitarget tracking, Sensor management, Phased array radar, Radar scheduling, Scaled track-error variance (STEV), Kalman Filter, Index policy, Marginal productivity (MP) index, Real-state multiarmed restless bandit problems (MARBP)

Multitarget tracking via restless bandit marginal productivity indices and Kalman Filter in discrete time

This paper designs, evaluates, and tests a tractable priority-index policy for scheduling target updates in a discrete-time multitarget tracking model, which aims to be close to optimal relative to a discounted or average performance objective accounting for tracking-error variance and measurement costs. The policy is to be used by a sensor system composed of M phased-array radars coordinated to track the positions of N targets moving according to independent scalar Gauss-Markov linear dynamics, which therefore allows for the use of the Kalman Filter for track estimation. The paper exploits the natural problem formulation as a multiarmed restless bandit problem (MARBP) with real-state projects subject to deterministic dynamics by deploying Whittle's (1988) index policy for the MARBP. The challenging issues of indexability (existence of the index) and index evaluation are resolved by applying a method recently introduced by the first author for the analysis of real-state restless bandits. Computational results are reported demonstrating the tractability of index evaluation, the substantial performance gains that the Whittle's marginal productivity (MP) index policy achieves against myopic policies advocated in previous work and the resulting index policies suboptimality gaps. Further, a preliminary small scale computational study shows that the (MP) index policy exhibits a nearly optimal behavior as the number of distinct objective targets grows with the number of radars per target constant

Radar networks: A review of features and challenges

Networks of multiple radars are typically used for improving the coverage and tracking accuracy. Recently, such networks have facilitated deployment of commercial radars for civilian applications such as healthcare, gesture recognition, home security, and autonomous automobiles. They exploit advanced signal processing techniques together with efficient data fusion methods in order to yield high performance of event detection and tracking. This paper reviews outstanding features of radar networks, their challenges, and their state-of-the-art solutions from the perspective of signal processing. Each discussed subject can be evolved as a hot research topic.Comment: To appear soon in Information Fusio

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

Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking

In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on a vehicle, and the target state should be represented in a global (absolute) coordinate frame. Then it is important to consider the uncertain location of the vehicle on which the sensor is mounted for MTT. In this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states. Measurements collected by different sensors mounted on multiple vehicles with varying location uncertainty are incorporated sequentially based on the arrival of new sensor measurements. In doing so, targets observed from a sensor mounted on a well-localized vehicle reduce the state uncertainty of other poorly localized vehicles, provided that a common non-empty subset of targets is observed. A low complexity filter is obtained by approximations of the joint sensor-feature state density minimizing the Kullback-Leibler divergence (KLD). Results from synthetic as well as experimental measurement data, collected in a vehicle driving scenario, demonstrate the performance benefits of joint vehicle-target state tracking.Comment: 13 pages, 7 figure

Multi-Object Tracking with Interacting Vehicles and Road Map Information

In many applications, tracking of multiple objects is crucial for a perception of the current environment. Most of the present multi-object tracking algorithms assume that objects move independently regarding other dynamic objects as well as the static environment. Since in many traffic situations objects interact with each other and in addition there are restrictions due to drivable areas, the assumption of an independent object motion is not fulfilled. This paper proposes an approach adapting a multi-object tracking system to model interaction between vehicles, and the current road geometry. Therefore, the prediction step of a Labeled Multi-Bernoulli filter is extended to facilitate modeling interaction between objects using the Intelligent Driver Model. Furthermore, to consider road map information, an approximation of a highly precise road map is used. The results show that in scenarios where the assumption of a standard motion model is violated, the tracking system adapted with the proposed method achieves higher accuracy and robustness in its track estimations

Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios

Multi-target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. Besides, the key problem of data association needs to be handled effectively considering the limitations in the computational resources on-board an autonomous car. The challenge of the tracking problem is further evident in the use of high-resolution automotive sensors which return multiple detections per object. Furthermore, it is customary to use multiple sensors that cover different and/or over-lapping Field of View and fuse sensor detections to provide robust and reliable tracking. As a consequence, in high-resolution multi-sensor settings, the data association uncertainty, and the corresponding tracking complexity increases pointing to a systematic approach to handle and process sensor detections. In this work, we present a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management features. These tracking functionalities can help facilitate perception during common events in public traffic as participants (suddenly) change lanes, navigate intersections, overtake and/or brake in emergencies, etc. Various tracking approaches including the ones based on joint integrated probability data association (JIPDA) filter, Linear Multi-target Integrated Probabilistic Data Association (LMIPDA) Filter, and their multi-detection variants are adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The utility of the filtering module is further elaborated by integrating it into a trajectory tracking problem based on model predictive control. To cope with tracking complexity in the case of multiple high-resolution sensors, we propose a hybrid scheme that combines the approaches of data clustering at the local sensor and multiple detections tracking schemes at the fusion layer. We implement a track-to-track fusion scheme that de-correlates local (sensor) tracks to avoid double counting and apply a measurement partitioning scheme to re-purpose the LMIPDA tracking algorithm to multi-detection cases. In addition to the measurement partitioning approach, a joint extent and kinematic state estimation scheme are integrated into the LMIPDA approach to facilitate perception and tracking of an individual as well as group targets as applied to multi-lane public traffic. We formulate the tracking problem as a two hierarchical layer. This arrangement enhances the multi-target tracking performance in situations including but not limited to target initialization(birth process), target occlusion, missed detections, unresolved measurement, target maneuver, etc. Also, target groups expose complex individual target interactions to help in situation assessment which is challenging to capture otherwise. The simulation studies are complemented by experimental studies performed on single and multiple (group) targets. Target detections are collected from a high-resolution radar at a frequency of 20Hz; whereas RTK-GPS data is made available as ground truth for one of the target vehicle\u27s trajectory