1. A target identification system, wherein target identification comprises flying object identification, the 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 ( T,z k |z 1,2. .. ,k−1 )/Σ T′ p ( T′,z k |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 ) dx 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 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; wherein p(x k |x k−1 ,T) is a multivariate Gaussian distribution and wherein p(x k |T, z k ) is another multivariate Gaussian distribution, where p(x k |x k−1 ,T) is a conditional probability distribution of hidden states at instance k given hidden states at instance k−1 and target type T; and wherein transition between one hidden state at one instance and the one hidden state at another instance is given by a predetermined dynamic model (referred to as ƒ k (x k−1 , T)) and wherein an expectation of p(x k |T, z 1,2,. .. k−1 ) is given by 
 μ k,k−1 x| =∫ƒ k ( x k−1 ) N ( x k ;μ k−1,k−1 x|T ,P k−1,k−1 xx|t ) dx k−1 and a covariance matrix is given inductively by 
 − P k,k−1 xx Q k +∫ƒ k ( x k−1 )ƒ k T ( x k−1 ) N ( x k ;μ k−1,k−1 x ,P k−1,k−1 xx|T ) dx k−1 −[μ k,k−1 x ] T μ k,k−1 x .