We propose an approximation framework for distributed target

localization in sensor networks. We represent the unknown

target positions on a location grid as a sparse vector,

whose support encodes the multiple target locations.

The location vector is linearly related to multiple sensor

measurements through a sensing matrix, which can be locally

estimated at each sensor. We show that we can successfully

determine multiple target locations by using linear

dimensionality-reducing projections of sensor measurements.

The overall communication bandwidth requirement

per sensor is logarithmic in the number of grid points and

linear in the number of targets, ameliorating the communication

requirements. Simulations results demonstrate the performance

of the proposed framework.