The superiority of multiple-input multiple-output (MIMO) radars over conventional radars has been recently shown in many aspects. These radars consist of many transmitters and receivers located far from each other. In this scenario, the MIMO radar is able to observe the targets from different directions. One of the advantages of these radars is exploitation of Doppler frequencies from different transmitter-target-receiver paths. The extracted Doppler frequencies can be used for estimation of target velocity Vector so that, the radar can be able to track the targets by use of its velocity Vector with reasonable accuracy. In this paper, two different processing systems are considered for MIMO radars. First one is the pulse Doppler system, and the second one is continuous wave (CW) system without range processing. The measurement of the velocity Vector of the target and its counterpart errors are taken into account. Also, the extended Kalman target Tracking by using its velocity Vector is considered. Besides, its performance is compared with those of MIMO target Tracking without using the velocity Vector and conventional radars. The simulation results show that the MIMO radars using velocity Vector have superior performance over other above-mentioned radars in fast maneuvering target Tracking. Since the range processing is ignored in CW MIMO radar systems, the complexity of this system is much lower than that of Pulse Doppler MIMO radar system, but has lower performance in Tracking fast maneuvering target.

Target Tracking is estimating the state of moving targets using noisy measurements obtained at a single observation point or node. Particle filters or sequential Monte Carlo methods use a set of weighted state samples, called particles, to approximate the posterior probability distribution in a Bayesian setup. During the past few years, Particle Filters have become very popular because of their ability to process observations represented by nonlinear state-space models where the noise of the model can be non-Gaussian. There are many Particle Filter methods, and almost all of them are based on three operations: particle propagation, weight computation, and resampling. One of the main limitations of the previously proposed schemes is that their implementation in a wireless sensor network demands prohibitive communication capability since they assume that all the sensor observations are available to every processing node in the weight update step. In this paper, we use a machine learning technique called support Vector machine to overcome this drawback and improve the energy consumption of sensors. Support Vector Machine (SVM) is a classifier which attempts to find a hyperplane that divides two classes with the largest margin. Given labeled training data, SVM outputs an optimal hyperplane which categorizes new examples. The training examples that are closest to the hyperplane are called support Vectors. Using our approach, we could compress sensor observations and only support Vectors will be communicated between neighbor sensors which lead to cost reduction in communication. We use LIBSVM library in our work and use MATLAB software to plot the results and compare the proposed protocol with CPF and DPF algorithms. Simulation results show significant reduction in the amount of data transmission over the network.

In high dynamic conditions, using Vector Tracking loops instead of scalar Tracking loops in GPS receivers is proved as an efficient method to compensate the performance. The Minimum Mean Squared Error detector as a multiuser detector is applied in the Vector Tracking loop for more reliability and efficiency. The Kalman filter does the two tasks of Tracking and extracting the navigation data after applying the multiuser detection on the correlator outputs. The covariance analysis is performed to study the effect of applying a multiuser detector along with a Vector Tracking loop against the conventional one. The covariance analysis is performed and the variance in the pseudorange-rate estimates produced by the two architectures of conventional Vector Tracking and the one with multiuser detector are used as the performance criteria. The steady state, state covariance of the Kalman filter of Vector Tracking loop is calculated. Comparing the psuedorange rate variances obtained from covariance analysis of each method shows improved performance of the new joint receiver of Vector and multiuser detector.

It is well-known that the stator Inter-Turn Short Circuit (ITSC) fault detection in Vector-controlled Induction Motor (IM) is a very difficult task so far. This is due to the various problems encountered in diagnostic processes resulting from the speed variation and its influence on electrical signals. As a solution, this paper proposes an approach based on the Vold-Kalman Filter Order Tracking (VKF-OT) technique. To this end, stator phase current, quadrature current and rotational speed were monitored at various speeds with and without fault (i. e. healthy and with the ITSC fault) in the Field oriented control (FOC) IM drive. The VKF-OT technique was applied to the monitored currents in order to track the related harmonic components fault, the results are compared to the classical Fourier analysis (FFT). The obtained results proved that the presented approach is more efficient than the FFT technique, especially when the IM is operating under non-stationary speed conditions.