Tracking of individual targets in a cluster presents difficult and computationally expensive problems that may be addressed using cluster tracking. This paper investigates the feasibility of tracking a single cluster using one or two space-based passive optical sensors. A functional model for closely spaced object (CSO) resolution was used to generate simulated measurements, and standard extended Kalman filter (EKF) techniques, along with gating and clustering logic, were used to estimate the state of the cluster centroid. An estimate was maintained of the two-dimensional extent of the cluster in each sensor's field-of-view. Results for a single-sensor filter run separately with two sets of measurements from two sensors, and for a centralized filter combining the same two sets of measurements, show that the effects of bias in CSO measurements cannot necessarily be overcome by the use of a second sensor. Results from the single-sensor filter over twenty Monte Carlo runs, all starting with the same initial state estimate (simulated handover error), are compared with results using the same sensor and measurements, but drawing the handover state errors from Gaussian distributions. The variance of the error in the second case is much larger throughout the entire track time, emphasizing the need for accurate handover data in a single angle-only sensor cluster tracking system.