Distributed algorithms in the sensors networks community usually require each sensor to have its own measurement. In practice, this constraint can not always be met. For example, in a camera network, all cameras might not observe a particular target as cameras are directional sensors and have a limited field-of-view (FOV). Moreover, different sensors might provide different quality measures related to different elements of the measurement vector depending on various factors as directionality, occlusion etc. This requires the designing of a new type of distributed algorithm that considers the quality and/or absence of measurements. In this paper, we present a distributed algorithm to compute the maximum likelihood estimate of the state of a target viewed by the network of cameras, taking into account the above-mentioned factors. We provide step-by-step derivation along with theoretical guarantee of optimality and convergence of the method. Experimental results are provided to show the performance of the proposed algorithm.