A Bayesian Network for the Classification of Human Motion as Observed by Distributed Radar

In this article, a statistical model of human motion as observed by a network of radar sensors is presented where knowledge on the position and heading of the target provides information on the observation conditions of each sensor node. Sequences of motions are estimated from measurements of instantaneous Doppler frequency, which captures...

Approximately Optimal Resource Management for Multi-Function Radar: Algorithmic Solutions Using a Generic Framework

Recent advances in Multi-Function Radar (MFR) systems led to an increase in their degrees of freedom. As a result, modern MFR systems are capable of adjusting many parameters during runtime. An automatic adaptation of the radar system to changing situations, like weather conditions, interference, or target maneuvers, is often mentioned in the...

Approximately Optimal Radar Resource Allocation: Joint Radar Communication for Automotive Applications

Due to the rising number of wireless device users, it is expected that there will be a scarcity in the spectrum. The will especially true for the Automotive Spectrum between 77 and 81 GHz. In this thesis, we apply Sensor Management to the Joint Radar Communication scenario. We develop an algorithm that can allocate resources to both sensing and...

Radar Resource Management for Multi-Target Tracking using Model Predictive Control

With modern multi-function radars becoming more flexible, handling the limited amount of resources of these radars becomes increasingly important. In this thesis the radar resource management (RRM) problem in a multi-target tracking scenario is considered. Partially observable Markov decision processes (POMDPs) are used to describe each tracking...

Approximately Optimal Radar Resource Management for Multi-Sensor Multi-Target Tracking: A First Step Towards a Distributed Solution for Radar Resource Management in a Sensor Network

The Radar Resource Management (RRM) problem in a multi-sensor multi-target scenario is considered. The problem is defined as a constrained optimization problem in which the predicted error covariance is minimized subject to resource budget constraints. By applying Lagrangian Relaxation (LR) the problem is decoupled into multiple sub-optimization...

A Constrained POMDP Formulation and Algorithmic Solution for Radar Resource Management in Multi-Target Tracking

The radar resource management problem in a multitarget tracking scenario is considered. The problem is solved using a dynamic budget balancing algorithm. It models the different sensor tasks as partially observable Markov decision processes and solves them by applying a combination of Lagrangian relaxation and policy rollout. The algorithm has a...

Approximately Optimal Radar Resource Management for Multi-Sensor Multi-Target Tracking

Radar Resource Management in a multi-sensor multi-target scenario is considered. A dynamic resource balancing algorithm is proposed which optimizes target task parameters assuming an underlying partially observable Markov decision process (POMDP). By applying stochastic optimization methods, such as policy rollout, the POMDP is solved non...

Radar Resource Management for Multi-Target Tracking Using Model Predictive Control

The radar resource management problem in a multi-target tracking scenario is considered. Partially observable Markov decision processes (POMDPs) are used to describe each tracking task. Model predictive control is applied to solve the POMDPs in a non-myopic way. As a result, the computational complexity compared to stochastic optimization...

Optimal Balancing of Multi-Function Radar Budget for Multi-Target Tracking Using Lagrangian Relaxation

The radar resource management problem in a multitarget tracking scenario for multi-function radar is considered. To solve it, an optimal balancing of the sensor budget by applying Lagrangian relaxation and the subgradient method is proposed. In a time-invariant scenario it is shown that the proposed method will lead to balanced budgets based on...