| Most of the transmit signal optimization methods aim to maximize Signal-to-Interference-plus-Noise Ratio (SINR) or minimize the ambiguity energy for non-zero values of delay and doppler. These optimization methods are rather indirect approaches, to achieve better estimation and detection capabilities of a radar. The current thesis states a direct information theoretic approach, derived from Fisher information theory, to achieve the end goal of radar - minimization of estimation error and maximization of detection accuracy. The transmit signal that generates higher new or additional information for a target subspace, rather than information itself, ultimately provides lowest estimation error.; To avoid computational complexity, the transmit signal for a MIMO radar is constructed by selection from available candidate solutions, in a sequential manner. The goodness (or badness) of a candidate solution is determined by a scalar value, resulting from new information matrix. The optimal transmit signal solution is compared with a pulse train signal (standard code), and a transmit signal selected by random guessing (guesswork code) from available candidate solutions. The transmit signal constructed by new information optimization shows superior results for various target scenarios, when compared with standard coded or guesswork coded transmit signal.; The new or additional information optimized transmit signal can be made data driven or adaptive leading to adaptive-transmit class radar. Transmit signal can be changed at real-time (or near real-time) to emphasize the least known objects, so that they can be correctly classified as target or clutter. |
