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 problems. The problem is modeled according to a Partially Observable Markov Decision Process (POMDP). Using a stochastic optimization framework called policy rollout, the POMDP is solved non-myopically by looking ahead into the expected future. Two novel implementations, namely a centralized and distributed implementation are presented as viable approaches for solving this problem for a multi-sensor case. The centralized implementation, defined as the approximately optimal solution, utilizes a global policy per task. As such, the policy rollout for a single target needs to explore the actions of multiple sensors. The distributed implementation is considered as a practical alternative to improve on the computational complexity of the policy rollout of the centralized implementation. Now per sensor and per task a policy rollout is computed. To maintain a similar performance as the centralized implementation, at the beginning of each policy rollout the last known actions of the other sensors are shared. An additional third independent implementation is considered. The independent implementation uses no communication during the optimization process and is considered to be the implementation with the lowest performance with respect to the cost. All implementations have been applied to multiple two-dimensional simulated radar tracking scenarios. A comparison is made between the centralized, distributed and independent implementation based on the average cost and runtime. Results indicate that both the centralized and distributed implementation outperform the independent implementation with respect to the cost by a factor two. Subsequently, the distributed solution converges to similar results as the centralized implementation while requiring significantly less computational resources.

This document is the bachelor graduation thesis of BAP Group B2. Together with BAP Group
B1 the objective of this project was to create a control network and DC/DC converter implementation
capable of fast, analog power management with active, remote-control and -interfacing of
multiple converter’s energy flows. The system will be designed to function in the solar-powered
eBike charging station on the TU Delft campus.
This document specifically is concerned with the design, simulation and testing of the DC/DC
converter. The thesis presents a bi-directional flyback DC/DC converter design and prototype
with analog power management, universal control signals and high integration potential.
These aspects are accomplished by building upon the traditional flyback DC/DC converter design
with respect to circuit topology, power-flow control, connectivity and accessibility. The
difficult regulation and measurement of DC power flow has been reduced to a set of analog
signals interpretable by any microcontroller, easing the integration of the converter into larger
energy and control networks.
A working prototype is presented and the inner-workings of the electrical circuit are discussed
and evaluated in detail. The converter is tested beyond the limit of the design requirements and
the performance is documented. Provided are possible reasons for malfunctioning and guidelines
for future improvements.
Design