Dae-Sung Jang

Integrated Simulator of Airborne Multi-function Radar Resource Manager and Environment Model

In this paper, an integrated radar resource manager for an airborne multi-function radar and a radar-environment simulator are presented. The radar-environment simulator includes target detection/measurement models, a nonlinear tracking filter for the airborne radar and an effective target generation algorithm. The structures and functions of modules in the radar resource manager are established and validated by the radar-environment simulator.

Heuristic pulse interleaving algorithms for multi-target tracking on pulse Doppler phased array radars

This paper presents several heuristic pulse interleaving algorithms for multi-target tracking on pulse Doppler phased array radars that can process multiple simultaneous received beams. A pulse interleaving problem is formalized as a mixed-integer linear program (MILP), explicitly taking into account the constraint that the pulses of different pulse repetition frequencies (PRF) can not be mutually interleaved to prevent pulse eclipsing. Several heuristic pulse interleaving algorithms are developed on the backbone of the same backward interleaving structure, while each algorithm features a PRF selection rule and a task selection rule. Numerical simulations validate effectiveness of the presented heuristic algorithms compared to the optimal solution of the MILP formulation.

Decentralized Message Passing Algorithm for Distributed Minimum Sensor Cover

This paper addresses a decentralized message passing approach for a sensor placement problem in a continuous plane, named minimum sensor cover problem: given pointwise tasks and omnidirectional cov...

Decentralized Message Passing for Minimum Sensor Cover Based on Belief Propagation

In this paper, decentralized message passing is addressed for a placement problem, named the minimum sensor cover problem: given point-wise tasks and sensors with omnidirectional coverage, find the minimum set of sensors that cover all the tasks. The minimum sensor cover problem can be converted to the geometric set cover by discretization of the solution space of the problem, and the geometric set cover is formulated as a maximum a posteriori (MAP) state assignment problem in a pairwise Markov random field model, which is a particular type of graphical models representing dependency relations between statistical variables. Belief propagation algorithm is a local iterative message passing algorithm devised to solve statistical inference problems in graphical models, but in a MAP assignment formulation of the set cover problem, solutions of belief propagation do not always result in convergence and the algorithm even produces infeasible solutions in largesized problems. A message passing algorithm based on belief propagation is presented to obtain stable and guaranteed feasible solutions by decentralized computations and numerical simulation validates the convergence, feasibility, and preferable solution quality of the presented algorithm against existing modifications of belief propagation and centralized greedy algorithm for the set cover.

Pass rate analysis of interception heuristic against border crossers along a linear border

In this paper, interception of border crossers along a border strip bounded by a border line and a military boundary line is considered. Performance measure of interception strategy of a guard is mission failure rate: pass rate of border crossers through the border strip. A defenceable area and a lateral defense length of sequential two-target interception are introduced to derive an expression of an upper bound of pass rate. Numerical simulations of border defense validate the upper bound.

Stochastic Radar Beam Scheduling Using Simulated Annealing

AESA radar is able to instantaneously and adaptively position and control the beam, and such adaptive beam pointing of AESA radar enables to remarkably improve the multi-mission capability, compared with mechanically scanned array radar. AESA radar brings a new challenges, radar resource management(RRM), which is a technique efficiently allocating finite resources, such as energy and time to each task in an optimal and intelligent way. Especially radar beam scheduling is the most critical component for the success of RRM. In this paper, we proposed stochastic radar beam scheduling algorithm using simulated annealing(SA), and evaluated the performance on the multi-function radar scenario. As a result, we showed that our proposed algorithm is superior to previous dispatching rule based scheduling algorithm from the viewpoint of beam processing latency and the number of scheduled beams, with real time capability.