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Multitarget tracking methods in a sensor network often assume the knowledge of the locations of the sensor nodes. However, in reality sensor nodes are randomly deployed with no prior knowledge about their positions. We propose a method to track an unknown and variable number of targets in the presence of false detections with the positions of sensor nodes estimated jointly to avoid the need of extra localization hardware. Moreover, as low-power consumption is a requirement in sensor networks, a collaborative estimation scheme is presented. For each target in the field under observation there is only a small set of sensor nodes that are active while the others remain in an idle state. The proposed technique is based on a Rao-Blackwellized sequential Monte Carlo (SMC) method that takes advantage of the fact that the state space of the unknown variables is separable. Therefore the problem is divided in two parts. The first one generates samples to estimate the number of targets and solves the association uncertainty between measurements and targets; while the second one is a multiple target tracking problem that can be solved with a modified unscented Kalman filter (MUKF) for each sample. It is shown through simulations that it is possible to track the multiple targets and also get accurate estimates of the unknown locations of the sensor nodes.