A single radio frequency (RF) channel digital beamforming (DBF) array antenna based on compressed sensing (CS) was proposed to reduce the hardware costs, power consumption, and the design complexity of large-scale DBF systems. For DBF antenna systems, the general way to obtain the signals received by each sensor is to connect each sensor to an independent RF receiver channel. When the array contains a large number of sensors, the multichannel signal sampling scheme makes the system power hungry and expensive. A time sequence phase weighting (TSPW) technology provides a solution to this problem. The TSPW antenna array can obtain the signals received by each sensor with only one RF receiver channel by sequentially sampling the specific single channel signals, which are produced by the TSPW array. However, the number of single channel samples scales linearly with the number of sensors. The sampling time will be so long as to be unacceptable when the array contains a large number of sensors. To overcome this problem, we introduced CS theory to the TSPW array. With the help of CS, the sampling frequency can be simultaneously reduced in both time and spatial domain, which correspond to the reduction of the number of both the samples and sensors. Theoretical analyses have been proposed to show the conditions that should be met for successful reconstruction. The simulation results from an X-band array with an aperture size of 80λ under the signal-to-noise ratio of 15 dB and the scenario of six targets showed that the proposed array could save above 63.1% of sensor numbers and above 84.4% in sampling time when compared with those of conventional TSPW arrays.

Due to the asymmetric geometry of flowpath and combustion organization, the heat flux distribution on RBCC's walls are extremely non-uniform. With a validated numerical model considering the changes of thermophysical properties and chemical components, the present study analyzed the effects of heat flux intensity, non-uniformity of heat flux distribution and inlet manifold on the mass flow distribution of parallel regenerative cooling channels. Results show that the intensity of heat flux enlarges the non-uniformity of parallel channels, however, the non-uniformity is reduced when the outlet temperature is above the completely pyrolysis value; and the non-uniformity heat flux distribution increases the mal-distribution of parallel channels dramatically, the difference of mass flow rate reaches to 33.2% when the heat flux difference is only 0.25 MW/m²; increasing the flow area of inlet manifold would improve the flow distribution of parallel channels with decrease of heat transfer efficiency and increases of pressure drop.

An array signal recovery algorithm based on sparse signal reconstruction theory is proposed for a single-RF-channel digital beamforming (DBF) array. A single-RF-channel antenna array is a low-cost antenna array in which signals are obtained from all antenna elements by only one microwave digital receiver. The spatially parallel array signals are converted into time-sequence signals, which are then sampled by the system. The proposed algorithm uses these time-sequence samples to recover the original parallel array signals by exploiting the second-order sparse structure of the array signals. Additionally, an optimization method based on the artificial bee colony (ABC) algorithm is proposed to improve the reconstruction performance. Using the proposed algorithm, the motion compensation problem for the single-RF-channel DBF array can be solved effectively, and the angle and Doppler information for the target can be simultaneously estimated. The effectiveness of the proposed algorithms is demonstrated by the results of numerical simulations.

In modern communication and radar applications, large-scale sensor arrays have increasingly been used to improve the performance of a system. However, the hardware cost and circuit power consumption scale linearly with the number of sensors, which makes the whole system expensive and power-hungry. This paper presents a low-cost nested multiple-input multiple-output (MIMO) array, which is capable of providing $O\left(2N^2\right)$ degrees of freedom (DOF) with $O\left(N\right)$ physical sensors. The sensor locations of the proposed array have closed-form expressions. Thus, the aperture size and number of DOF can be predicted as a function of the total number of sensors. Additionally, with the help of time-sequence-phase-weighting (TSPW) technology, only one receiver channel is required for sampling the signals received by all of the sensors, which is conducive to reducing the hardware cost and power consumption. Numerical simulation results demonstrate the effectiveness and superiority of the proposed array.