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We propose a particle filter tracker to track multiple maneuvering targets using a batch of range measurements. The state update is formulated through a locally linear motion model and the observability of the state vector is proved using geometrical arguments. The data likelihood treats the range observations as an image using template models derived from the state update equation, and incorporates the possibility of missing data as well as spurious range observations. The particle filter handles multiple targets, using a partitioned state-vector approach. The filter proposal function uses a Gaussian approximation of the full-posterior to cope with target maneuvers for improved efficiency. By treating the range measurements as images and using smoothness constraints, the particle filter is able to avoid the data association problems. Computer simulations demonstrate the performance of the tracking algorithm.