https://hal.archives-ouvertes.fr/hal-01383402/document

PELICAN: Panoramic millimeter-wave radar for perception in mobile robotics applications, Part 1: Principles of FMCW radar and of 2D image construction

This paper first proposes a method to estimate the altitude of an aircraft using two 2-D radars for Air Traffic Control (ATC) applications. Assuming that the aircraft flies with a nearly constant velocity and nearly constant height, we first estimate its velocity, initial position in 3-D coordinates from the measurements of two 2-D radars using a maximum likelihood estimator. Next, we incorporate the estimates as initial values for 3-D flight tracking that uses an extended Kalman filter (EKF). Furthermore, a multiple model approach is adopted to further improve the tracking performance. The simulation results are shown to demonstrate the effectiveness of the proposed method.

Altitude estimation for 3-D tracking with two 2-D radars

The desired improvements of a tracker rely on more accurate state estimates and less computation loads. A state-vector multisensor data fusion approach that consists of local processor and global processor is described for the problem of tracking a maneuvering target in the Local Inertial Cartesian Coordinate System (LICCS). For local processor, the sensor-based filtering algorithm utilized in the Reference Cartesian Coordinate System (RCCS) is presented for target tracking when the radar measures range, azimuth and elevation angles in the Spherical Coordinate System (SCS). To reduce the computational loads involved in physical implementation, the decoupling technique that Kalman filter gain formulations are recursively computed in the Line-of-sight Cartesian Coordinate System (LCCS) and then transformed for use in the RCCS is adopted. For global processor, the data fusion algorithm, called covariance matching method, is devised using sensor filter covariance matrices to estimate each sensor weight for obtaining an improved joint state estimate in the LICCS. Simulation results are presented comparing the performance of the proposed algorithm with the sensor-based decoupled Kalman filtering algorithms. That the number of sensors may influence the tracking accuracies of proposed algorithm is discussed.

Multisensor Data Fusion with Estimated Weights

3-D tracking of air targets using a single 2-D radar

ns-3 is the successor of ns-2, the most popular network simulator. Network simulators such as ns-3 play an important role on understanding, designing, and building Internet systems. But simulations are only as good as their models, and the simulation of large scale Internet systems using accurate and complex models is a challenging task. ns-3 simulates realistically the network stack but the scale and complexity of the Internet topology is, from our point of view, limited by the IP forwarding operations.

This work proposes CIDRarchy, an IPv4 routing protocol for ns-3 that uses CIDR as the base to create an hierarchical Internet-like network topology that enables (1) IP forwarding with constant time complexity and automatic IPv4 address assignment, and (2) the implementation of an ns-3 helper to ease network topology creation. We implemented CIDRarchy, evaluated its performance, and obtained simulation time reduction over existing ns-3 routing protocols implementations that can reach over one order of magnitude.

CIDRarchy: CIDR-based ns-3 Routing Protocol for Large Scale Network Simulation

Modern tactical surveillance systems benefit from a network of distributed sensors that fuse multiplatform-multisensor data into a single integrated picture. Data fusion is complicated due to inconsistent dimensionality between sensors. For example, some radar systems provide range, bearing, and elevation measurements, while other systems provide two-dimensional measurements in range and bearing only. This paper presents a method for generating three dimensional track states and error covariance matrices from two dimensional tracks from two or more surveillance radars geographically separated in WGS-84 coordinates. Equations are developed for estimating the state and error covariance for the single sensor and multiplatform-multisensor cases. For surveillance radars with multiple tracks, track-to-track assignment is performed using the likelihood of the three dimensional track state for each candidate track-to-track association. Results of Monte Carlo simulations show that the new technique is a practical and efficient method that improves track accuracy, covariance consistency, and hence, the value of netting surveillance radars.

Multiplatform-multisensor tracking with surveillance radars

Given that large-scale network simulations are a significant part of active research [6], and that such simulations are known to be computationally and resource intensive, it is still of interest to find innovative ways to achieve faster execution times and to more efficiently provide data and results for the researcher.While utilization of parallel programming techniques and frameworks such as MPI, CUDA, and OpenCL are valid approaches and very useful in speeding up computations, few of these techniques seek to reduce repetitive, uninteresting, or non-changing segments of simulation runs which are unnecessarily repeated, for instance during initialization, and which are not of interest to the end result being studied.Recent advances in process checkpointing utilities, such as Distributed Multithreaded Checkpointing (DMTCP) [1], which support practical user-space checkpointing of a wide variety of distributed, multithreaded applications and support the use of important libraries such as MPI, enable innovative techniques for achieving computational savings and provide the potential for a more generic mechanism of moving simulations forward and backward in time efficiently.We present an approach and prototype module for ns-3 which provides an API for checkpointing running ns-3 applications at arbitrary times, restoring these applications to a running state, and for modifying parameters of the restored simulation before process continuation.

Kyle Harrigan

Papers

Multiplatform-multisensor tracking with surveillance radars

Simulation speedup of ns-3 using checkpoint and restore