![]() ![]() We also used a perturbation error model of an interferometric radar altimeter (IRA) as the measurement noise covariance needed for an accurate CRLB analysis. This paper derives the Cramer-Rao Lower Bound (CRLB) on angular/Doppler frequency estimation and target localization using a bistatic multi-input multi-output (MIMO) radar system. (TOA) measurements is very important in military and civil applications such as Wireless Sensor Networks (WSNs), sonar, radar, security systems. Due to the difficulty in theoretically verifying that the model generated by the deep learning method was designed optimally, we performed a Cramér-Rao lower bound (CRLB) analysis to evaluate how close the proposed method was to the optimal design. An RNN is an artificial neural network that recognizes patterns from a time-series. The effectiveness of the developed technique is illustrated with several simulation examples. Based on the optimization of the CRLB, a practical technique for determining optimal receiver trajectories is developed. Based on this two-target CRLB, we propose several performance measures and develop a number of algo- rithms for designing the optimal antenna and pulse placement of colocated MIMO radar systems. ![]() An estimator that can achieve the CRLB under regularity conditions is the maximum likelihood (ML) estimator. Noise covariances and the measurement model of the PF were also trained. The Cramer-Rao lower bound (CRLB) for the radar location estimate is derived. The Cramer-Rao lower bound (CRLB) 18, 19 serves as a benchmark of the non-Bayesian estimator. For this study, a robust PF-based TRN was designed, which uses a recurrent neural network (RNN)-based deep learning method to function on flat and repetitive terrains. Even though TRN performs well in rough and unique terrains, its performance degrades on flat and repetitive terrain. The particle filter (PF)-based TRN has been widely used for unmanned aerial vehicles (UAVs) operating at a high altitude. Terrain-referenced navigation (TRN) is a technology that estimates the position of an aircraft by comparing the terrain elevation on a digital elevation model (DEM) and the altitude measured by an altimeter. ![]()
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