Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userModular Sparse Conical Multi-beam Phased Array Design for Air Traffic Control Radar
64
0
0.0
(
0
)
Added by
Nicola Anselmi
Publication date
2021
fields
Electronic Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The design of a conical phased array antenna for air traffic control (ATC) radar systems is addressed in this work. The array, characterized by a fully digital beam-forming (DBF) architecture, is composed of equal vertical modules consisting of linear sparse arrays able to generate on receive multiple instantaneous beams pointing along different elevation directions. The synthesis problem is cast in the Compressive Sensing (CS) framework to achieve the best trade-off between the antenna complexity (i.e., minimum number of array elements and/or radio frequency components) and radiation performance (i.e., matching of a set of reference patterns). Towards this aim, the positions of the array elements and the set of complex element excitations of each beam are jointly defined through a customized CS-based optimization tool. Representative numerical results, concerned with ideal as well as real antenna models, are reported and discussed to validate the proposed design strategy and point out the features of the deigned modular sparse arrays also in comparison with those obtained from conventional arrays with uniformly spaced elements.
rate research
Read More
MIMO transmit arrays allow for flexible design of the transmit beampattern. However, the large number of elements required to achieve certain performance using uniform linear arrays (ULA) maybe be too costly. This motivated the need for thinned arrays by appropriately selecting a small number of elements so that the full array beampattern is preserved. In this paper, we propose Learn-to-Select (L2S), a novel machine learning model for selecting antennas from a dense ULA employing a combination of multiple Softmax layers constrained by an orthogonalization criterion. The proposed approach can be efficiently scaled for larger problems as it avoids the combinatorial explosion of the selection problem. It also offers a flexible array design framework as the selection problem can be easily formulated for any metric.
Sparse array design aided by emerging fast sensor switching technologies can lower the overall system overhead by reducing the number of expensive transceiver chains. In this paper, we examine the active sparse array design enabling the maximum signal to interference plus noise ratio (MaxSINR) beamforming at the MIMO radar receiver. The proposed approach entails an entwined design, i.e., jointly selecting the optimum transmit and receive sensor locations for accomplishing MaxSINR receive beamforming. Specifically, we consider a co-located multiple-input multiple-output (MIMO) radar platform with orthogonal transmitted waveforms, and examine antenna selections at the transmit and receive arrays. The optimum active sparse array transceiver design problem is formulated as successive convex approximation (SCA) alongside the two-dimensional group sparsity promoting regularization. Several examples are provided to demonstrate the effectiveness of the proposed approach in utilizing the given transmit/receive array aperture and degrees of freedom for achieving MaxSINR beamforming.
Cognitive multiple-input multiple-output (MIMO) radar is capable of adjusting system parameters adaptively by sensing and learning in complex dynamic environment. Beamforming performance of MIMO radar is guided by both beamforming weight coefficients and the transceiver configuration. We propose a cognitive-driven MIMO array design where both the beamforming weights and the transceiver configuration are adaptively and concurrently optimized under different environmental conditions. The perception-action cycle involves data collection of full virtual array, covariance reconstruction and joint design of the transmit and receive arrays by antenna selection.The optimal transceiver array design is realized by promoting two-dimensional group sparsity via iteratively minimizing reweighted mixed L21-norm, with constraints imposed on transceiver antenna spacing for proper transmit/receive isolation. Simulations are provided to demonstrate the perception-action capability of the proposed cognitive sparse MIMO array in achieving enhanced beamforming and anti-jamming in dynamic target and interference environment.
A novel single-sideband (SSB) time-modulated technique with 2-bit phase shifters is proposed. The timemodulated module is implemented by adding periodic phase modulation to 2-bit phase shifters, which is simpler without performance loss compared to existing SSB time-modulated method. During one modulation period, four phase states (0, {pi}/2, {pi}, 3{pi}/2) of 2-bit phase shifters are switched in sequence. After the modulation, the SSB time modulation is realized and the main power is distributed to the first harmonic component. The feasibility of the proposed method is verified by experiments. The undesired harmonics are efficiently suppressed. Meanwhile, 80{deg} beam scanning range are realized through the proposed module.
A novel multiple-input multiple-output (MIMO) dual-function radar communication (DFRC) system is proposed. The system transmits wideband, orthogonal frequency division multiplexing (OFDM) waveforms using a small subset of the available antennas in each channel use. The proposed system assigns most carriers to antennas in a shared fashion, thus efficiently exploiting the available communication bandwidth, and a small set of subcarriers to active antennas in an exclusive fashion (private subcarriers). A novel target estimation approach is proposed to overcome the coupling of target parameters introduced by subcarrier sharing. The obtained parameters are further refined via an iterative approach, which formulates a sparse signal recovery problem based on the data of the private subcarriers. The system is endowed with beamforming capability, via waveform precoding and antenna selection. The precoding and antenna selection matrices are optimally co-designed to meet a joint sensing-communication system performance. The sparsity of the transmit array is exploited at the communication receiver to recover the transmitted information. The use of shared subcarriers enables high communication rate, while the sparse transmit array maintains low system hardware cost. The sensing problem is formulated by taking into account frequency selective fading, and a method is proposed to estimate the channel coefficients during the sensing process. The functionality of the proposed system is demonstrated via simulations.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
