JOURNAL OF RADAR
Year:2016 | Volume:3 | Issue:4 (SERIAL NO. 10)|Papers Count:6

This paper studies the use of Unscented Kalman Filters (UKF) to estimate nonlinear dynamics and, specifically, adaptive determination of scaling parameters in these filters. Due to lack of analytic solution and use of numerical methods instead, the computational load of these filters increases drastically. In this paper, a new method is proposed based on Interactive Multiple Models (IMM) which has lower computational burden than previous methods. The performance of the proposed filter is evaluated by simulating a tracking scenario. Its accuracy, computational load and stability are compared with the numerical adaptive UKF filter and constant parameter UKF filter. The simulation results show that the proposed filter performs better than the constant parameter UKF and very close to the numerical adaptive UKF.

Due to increasing growth of air traffic, it is very important to have a reliable air traffic control system. Usually secondary surveillance radar systems are employed for this task. In this paper, a new approach is presented to identify interrogators location of this system. The proposed method works passively and uses just a single receiver. This goal has been reached using angle and time difference of arrival information. How to determine the desired angle and calculate the time difference of arrival from the received data is described. knowing this information, a closed-form solution in polar coordinate is presented and its sensitivity is measured. Then a method is proposed based on maximum likelihood estimation of the target location. Afterward angle and time difference of arrival equations are linearized and then weighted least squares estimation is achieved. The suggested approach is evaluated on real and practical information and the results have finally been reported.

Multiple Input Multiple Output (MIMO) radars are the new generation of array radars that employ orthogonal waveforms as their probing signals. Despite several advantages, this strategy results in losing coherent processing gain. Many efforts have been made to overcome this weak point by modifying the correlation of transmit signals. Recently, a new phased MIMO radar has been proposed that partitions the transmitting array into a number of sub arrays, and allocates a waveform to each of them that is orthogonal to the waveforms transmitted by other sub arrays. In this radar, designing the weight vectors of each sub array provides a means of forming the desired beam pattern. This paper proposes a method for selecting weights based on conventional beam forming to maximize power around targets of interest and considers the results of this signal design on the receiving end by implementing four parameter estimation algorithms. Unlike the method used for MIMO radars, in this procedure both transmitted power from antennas and distribution of power among targets can be controlled and the desired beam pattern is obtained with less calculation and less orthogonal waveforms. The method is also extended to the planner array.

In ultra-wideband (UWB) multistatic microwave breast cancer imaging, the long imaging time is an important issue. In this paper, a novel imaging method for multistatic UWB radar based microwave breast cancer imaging using sparse sampling has been proposed that significantly reduces the amount of data and imaging time. In this method, instead of measuring multistatic radar signals conventionally, just a few signals, by randomly choosing the transmitter and the receiver, are sufficient for obtaining reliable images even at high noise levels. Using simulations done, it is shown that sparser images can be obtained compared to the delay-and-sum (DAS) beam forming method using only a few received signals and a few time samples.

In this paper, the detector of a moving target in MIMO radar with widely separated antenna in the presence of the a-stable clutter is proposed. Due to the complexity of the unknown parameter estimation in the presence of the target, the Rao test is applied. The a-stable model is a general model that can be applied to different environments with appropriate selection of parameters. The problem with this model is inexistence of a closed form for the probability density function of the distribution. Fortunately, there are models that approximate the distribution by a closed form expression. In the present paper, mixing of Gaussian and Cauchy model is used for expressing clutter. Finally, the performance of the proposed detector is compared with the optimal Gaussian and MiniMax detector by a graph of the probability of detection versus signal-to-clutter ratio. The simulation results, show that the performance of the proposed detector is better than the optimal Gaussian and MiniMax detector.