A Robust Beamformer Based on Weighted Sparse Constraint

Applying a sparse constraint on the beam pattern has been suggested to suppress the sidelobe level of a minimum variance distortionless response (MVDR) beamformer. In this letter, we introduce a weighted sparse constraint in the beamformer design to provide a lower sidelobe level and deeper nulls for interference avoidance, as compared with a conventional MVDR beamformer. The proposed beamformer also shows improved robustness against the mismatch between the steering angle and the direction of arrival (DOA) of the desired signal, caused by imperfect estimation of DOA.Comment: 4 pages, 2 figure

Spatial Compressive Sensing for MIMO Radar

We study compressive sensing in the spatial domain to achieve target localization, specifically direction of arrival (DOA), using multiple-input multiple-output (MIMO) radar. A sparse localization framework is proposed for a MIMO array in which transmit and receive elements are placed at random. This allows for a dramatic reduction in the number of elements needed, while still attaining performance comparable to that of a filled (Nyquist) array. By leveraging properties of structured random matrices, we develop a bound on the coherence of the resulting measurement matrix, and obtain conditions under which the measurement matrix satisfies the so-called isotropy property. The coherence and isotropy concepts are used to establish uniform and non-uniform recovery guarantees within the proposed spatial compressive sensing framework. In particular, we show that non-uniform recovery is guaranteed if the product of the number of transmit and receive elements, MN (which is also the number of degrees of freedom), scales with K(log(G))^2, where K is the number of targets and G is proportional to the array aperture and determines the angle resolution. In contrast with a filled virtual MIMO array where the product MN scales linearly with G, the logarithmic dependence on G in the proposed framework supports the high-resolution provided by the virtual array aperture while using a small number of MIMO radar elements. In the numerical results we show that, in the proposed framework, compressive sensing recovery algorithms are capable of better performance than classical methods, such as beamforming and MUSIC.Comment: To appear in IEEE Transactions on Signal Processin

Effect of gain and phase errors on SKA1-low imaging quality from 50-600 MHz

Simulations of SKA1-low were performed to estimate the noise level in images produced by the telescope over a frequency range 50-600 MHz, which extends the 50-350 MHz range of the current baseline design. The root-mean-square (RMS) deviation between images produced by an ideal, error-free SKA1-low and those produced by SKA1-low with varying levels of uncorrelated gain and phase errors was simulated. The residual in-field and sidelobe noise levels were assessed. It was found that the RMS deviations decreased as the frequency increased. The residual sidelobe noise decreased by a factor of ~5 from 50 to 100 MHz, and continued to decrease at higher frequencies, attributable to wider strong sidelobes and brighter sources at lower frequencies. The thermal noise limit is found to range between ~10 - 0.3 $\mu$Jy and is reached after ~100-100 000 hrs integration, depending on observation frequency, with the shortest integration time required at ~100 MHz.Comment: 23 pages, 11 figures Typo correcte