  We study a scenario where a group of agents, each with multiple heterogeneous
sensors are collecting measurements of a vehicle and the measurements are
transmitted over a communication channel to a centralized node for processing.
The communication channel presents an information-transfer bottleneck as the
sensors collect measurements at a much higher rate than what is feasible to
transmit over the communication channel. In order to minimize the estimation
error at the centralized node, only a carefully selected subset of measurements
should be transmitted. We propose to select measurements based on the Fisher
information matrix (FIM), as "minimizing" the inverse of the FIM is required to
achieve small estimation error.
  Selecting measurements based on the FIM leads to a combinatorial optimization
problem. However, when the criteria used to select measurements is both
monotone and submodular it allows the use of a greedy algorithm that is
guaranteed to be within $1-1/e\approx63\%$ of the optimum and has the critical
benefit of quadratic computational complexity. To illustrate this concept, we
derive the FIM criterion for different sensor types to which we apply FIM-based
measurement selection. The criteria considered include the time-of-arrival and
Doppler shift of passively received radio transmissions as well as detected
key-points in camera images.
