1,646 research outputs found
Mean Square Capacity of Power Constrained Fading Channels with Causal Encoders and Decoders
This paper is concerned with the mean square stabilization problem of
discrete-time LTI systems over a power constrained fading channel. Different
from existing research works, the channel considered in this paper suffers from
both fading and additive noises. We allow any form of causal channel
encoders/decoders, unlike linear encoders/decoders commonly studied in the
literature. Sufficient conditions and necessary conditions for the mean square
stabilizability are given in terms of channel parameters such as transmission
power and fading and additive noise statistics in relation to the unstable
eigenvalues of the open-loop system matrix. The corresponding mean square
capacity of the power constrained fading channel under causal encoders/decoders
is given. It is proved that this mean square capacity is smaller than the
corresponding Shannon channel capacity. In the end, numerical examples are
presented, which demonstrate that the causal encoders/decoders render less
restrictive stabilizability conditions than those under linear
encoders/decoders studied in the existing works.Comment: Accepted by the 54th IEEE Conference on Decision and Contro
Cooperative Pursuit with Multi-Pursuer and One Faster Free-moving Evader
This paper addresses a multi-pursuer single-evader pursuit-evasion game where
the free-moving evader moves faster than the pursuers. Most of the existing
works impose constraints on the faster evader such as limited moving area and
moving direction. When the faster evader is allowed to move freely without any
constraint, the main issues are how to form an encirclement to trap the evader
into the capture domain, how to balance between forming an encirclement and
approaching the faster evader, and what conditions make the capture possible.
In this paper, a distributed pursuit algorithm is proposed to enable pursuers
to form an encirclement and approach the faster evader. An algorithm that
balances between forming an encirclement and approaching the faster evader is
proposed. Moreover, sufficient capture conditions are derived based on the
initial spatial distribution and the speed ratios of the pursuers and the
evader. Simulation and experimental results on ground robots validate the
effectiveness and practicability of the proposed method
Graph Optimization Approach to Range-based Localization
In this paper, we propose a general graph optimization based framework for
localization, which can accommodate different types of measurements with
varying measurement time intervals. Special emphasis will be on range-based
localization. Range and trajectory smoothness constraints are constructed in a
position graph, then the robot trajectory over a sliding window is estimated by
a graph based optimization algorithm. Moreover, convergence analysis of the
algorithm is provided, and the effects of the number of iterations and window
size in the optimization on the localization accuracy are analyzed. Extensive
experiments on quadcopter under a variety of scenarios verify the effectiveness
of the proposed algorithm and demonstrate a much higher localization accuracy
than the existing range-based localization methods, especially in the altitude
direction
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