David Wilcox

On MIMO Radar Subarrayed Transmit Beamforming

Multiple-input-multiple-output (MIMO) radar schemes whereby the transmit array is partitioned into subarrays have recently been proposed in the literature to combine advantages of phased array and MIMO radar technology. In this work, we utilize this architecture to significantly simplify a transmit procedure in which the covariance matrix across the MIMO radar array is optimized to improve the Cramér-Rao bound (CRB) on target parameter estimation. The MIMO effective array for regular subarrayed transmit apertures is studied, and necessary conditions to obtain a filled effective aperture are presented, which is important for maintaining nonambiguous, low sidelobe beampatterns. The performance of the subarrayed transmit approach is evaluated in terms of the CRB on target parameter estimation, and the optimisation of the beamformer applied to the subarrays to minimize the CRB is considered. The subarrayed transmit scheme is found to have a CRB which is suboptimal to the full diversity transmission, as expected, but is solvable in a small fraction of the time using an iterative beamspace algorithm developed here.

A Study on MVDR Beamforming Applied to an ESPAR Antenna

The adaptive beamforming algorithm-minimum variance distortionless response (MVDR) has been studied based on the electronically steerable parasitic array radiator (ESPAR) antenna. The ESPAR antenna uses a single radio-frequency (RF) front end, and its beamforming is achieved by adjusting reactance loads of parasitic elements coupled to the central active element. In the proposed beamforming method, the MVDR beamformer optimizes weights applied to outputs of beams. The optimization problem is formulated as a second-order-cone programming (SOCP) problem including a Euclidean distance metric to approximate the optimal equivalent weight vector to a feasible solution. Then the ESPAR beampattern design strategy iterates between the SOCP problem and a simple projection of reactance loads. The simulations show that the proposed MVDR beamforming method based on an ESPAR antenna gives a beam steering at the desired direction and placing nulls at the interfering directions, and it converges fast. However, when the desired source is close to the interferer, the output signal-to-interference-plus-noise ratio (SINR) degrades and where we use the interference-plus-noise sample covariance matrix to improve the beamforming performance.

On the design of Blind Interference Alignment using ESPAR antenna

Recently, a class of “Blind Interference Alignment (BIA)” techniques has been proposed in [3] and [4], which allows interference alignment to be achieved without knowledge of channel coefficients at the transmitter. In [4], the key to realizing the BIA for multi-user (MU) multiple-output-single-input (MISO) broadcast channel (BC) is the use of a receive antenna capable of switching among multiple pre-determined antenna patterns. The Electronically Steerable Parasitic Array Radiator (ESPAR) antenna, which uses only a single radio frequency (RF) front-end, has been shown to allow multiple-output-multiple-input (MIMO) communication by transmitting and/ or receiving data over diverse beampatterns. As the ESPAR antenna is capable of switching antenna pattern among a set of orthogonal basis patterns, in this context, we design a MU-MISO BC where receivers are equipped with ESPAR antennas to create short-term beampattern fluctuations. We show that there are multiple potential channel values available for each user through antenna switching of an ESPAR antenna. Then, the designed system model is exploited for implementation of the BIA coding [4]. The performance of the proposed BIA scheme is evaluated via simulation.

Resolution of two point targets using sub-arrayed MIMO radar

Recent works on MIMO radar have shown benefits in optimising the signal covariance matrix for estimation of target parameters, over the performance of conventional radar and the orthogonal waveform approach used in much of the MIMO radar literature to date. The full diversity approach requires a large amount of flexibility at the transmitter and computation prior to transmission. In this work, we study the performance of a sub-arrayed transmit beamforming approach for MIMO Radar, which greatly reduces the requirements of the transmitter, for the important case of resolving two point targets.

Constrained adaptive Markov transition matrix based target tracking with IMMPF

Interacting multiple models particle filter (IMMPF) has been recently paid great attention for its eminent ability in solving nonlinear target tracking problem. Through improving some filter steps, such as sampling and re-sampling, particle filter can offer more estimation accuracy. This paper proposes a particle filter taking advantage of constrained adaptive Markov transition matrix based on post-probability. At the end of each filter iteration process, we study two methods to update Markov transition matrix for the next iteration process. One is with the ratio of likelihood function, and the other is with the compress ratio of estimation error. Furthermore, to avoid possible failure resulted from abnormal data during the iteration process; we set the upper bound to constrain Markov transition probability. Simulations show that constrained adaptive Markov transition matrix is beneficial to improve interacting multiple models particle filter results.

Beampattern optimisation for sub-arrayed MIMO radar for large arrays

The estimation of the direction of arrival (DOA) of signals in the far-field of an array has been an area of continued research. Recent advances in MIMO radar techniques have led to results whereby prior knowledge of the approximate target locations is used to generate arbitrary signals which are transmitted from an active array to improve DOA estimates. This would be a useful in a scenario such as target tracking. However, this neglects the complexities of the hardware required for larger arrays, and further, the amount of computation required on the transmit side alone. A sub-optimal method employing sub-arrays to reduce hardware requirements, and a low complexity algorithm to determine the optimal steering vectors for the arrays is presented and compared to the optimal full diversity transmit strategy.