For the passive estimation of directions of arrivals (DOAs) of transmitting sources from an antenna array, high resolution estimators can be achieved from maximum likelihood or signal subspace based concepts, provided that certain model assumptions are satisfied. For the signal subspace class of methods, the sources are nearly always regarded as being stationary during the observation interval of the array data. If this is the case, and if all the other model assumptions are valid, then the DOA can be estimated to arbitrary accuracy in the presence of noise by observing the data over a sufficiently long time interval. When sources are in fact moving relative to the receiving array, errors are induced in signal subspace methods. These depend on the extent of the source motion. Hence there is a trade-off between decreasing noise errors and increasing motion errors as the observation time is increased, and some optimum observation period exists. In some cases, however, even the performance at the optimum observation interval may be unsatisfactory, and alternative approaches are desirable. Signal subspace concepts break down when sources are moving, and maximum likelihood methods for moving sources are computationally expensive. To overcome these problems three novel algorithms have been developed for the joint estimation of source position and velocity from a batch of array data for sources moving with constant angular velocity. These allow increased accuracy in the source position determination, and provide unbiased velocity estimates. The performance of the estimators is compared to the Cramer-Rao lower bound.
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