1. A target identification system comprising: a memory for storing data comprising: a data structure stored in the memory, the data structure including: a number of measurements of a target, each measurement from the number of measurements being observed at a predetermined time (z k ); a number of target types (T); each one of the number of measurements, each one the number of target type and each one of one or more hidden states, each hidden state (x k ) being characterized at the predetermined time, being correlated to one another; one or more processors operatively connected to the memory for storing data; the one or more processors being configured to: provide a first conditional probability distribution, a conditional probability of a target type given a number of measurements, defined inductively by p ( T | z 1 , 2 , … , k ) = p ( z k , T | z 1 , 2 , … , k - 1 ) ∑ T ^ p ( z k , T ^ | z 1 , 2 , … , k - 1 ) , and p ( z k , T | z 1 , 2 , … , k - 1 ) = p ( T | z 1 , 2 , … , k - 1 ) ∫ p ( z k | T , x k ) p ( x k | T , z 1 , 2 , … , k - 1 )  x k where p(z k,i |T,x k ) is a conditional probability of an ith measurement at a kth instance giving a target type T and hidden states at the kth instance, and p(T|z 1, 2,. .. k−1 ) is a conditional probability distribution f of a target type, T, given the plurality of measurements at the k−1 th time, z 1,. .. k−1 ; and obtain an estimate of the target type from the first conditional probability.