Array signal recovery algorithm for a single-RF-channel DBF array

Zhang, Duo; Wu, Wen; Da, Fang

doi:10.1186/s13634-016-0391-6

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…Over the last several years, many solutions have been developed to handle the challenge. One of the feasible solutions is the single receiver channel array based on the time-sequence-phase-weighting (TSPW) technology [ 11 , 12 ]. The single channel TSPW (SC-TSPW) array exchanges the sampling time for hardware cost savings.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Nested-MIMO Array for Large-Scale Wireless Sensor Applications

Zhang

Fang

et al. 2017

Sensors

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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(2N2) degrees of freedom (DOF) with O(N) 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.

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Section: Introductionmentioning

confidence: 99%

Low-Cost Nested-MIMO Array for Large-Scale Wireless Sensor Applications

Zhang

Fang

et al. 2017

Sensors

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Special issue: advanced techniques for radar signal processing

Orlando¹,

Chen²,

Aubry³

et al. 2017

EURASIP J. Adv. Signal Process.

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Recent advances in technology have led to the development of low-cost sensing devices capable of providing high performances in terms of both computational resources and measurement precision. More important, the reduced size of such devices have allowed to exploit them in diverse application fields (as, for instance, medical, military, manufacturing, transportation, and safety systems). However, in the context of military applications (e.g., radar and communication systems), the downside of this technology is that it is available for terrorist attacks aimed at denying targeting information and using radarguided missiles or small drones carrying dangerous (e.g., explosive or chemical) substances. Thus, it stems the need for innovative signal processing solutions to counteract these threats. Such techniques are applicable in ship and aircraft monitoring (for defense purposes), coastal surveillance, and, generally speaking, homeland security.This special issue focuses on radar signal processing techniques (target detection and tracking, interference estimation and suppression, adaptive beamforming, electronic warfare) that benefit from the mentioned advances to face the new challenging operating scenarios that naturally arise from nowadays technology advantages and disadvantages. More specifically, the emphasis is on (possibly distributed) radar systems equipped with arrays of sensors, which enable to capitalize the spatial diversity and power integration enabling significant improvements in performance.In general, radar systems perform three general functions, which are search, track, and imaging. The most important operation of a search radar is target detection. As a matter of fact, once the system declares the presence of a target, its resources are scheduled to estimate target parameters [1, 2] or for target tracking [3] which consists in the fine estimation of parameters as range, azimuth angle, elevation angle, and Doppler frequency offset.

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Realization of translation group in optical design with deep nerual network under eikonal-energy mapping

Hang¹,

Yuejiao²,

Chen³

et al. 2022

Acta Phys. Sin.

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Nonimaging optical design aiming at energy control has a wide range of applications in optoelectronics. A nonimaging optical system is composed of a light source, optical components, and a target screen, and can be described by an equation named light taming equation(LTE). Given the light source and prescribed target spot, the required freeform surfaces of the optical component can be obtained by solving the LTE. If the light source profile does not change, the optical surface has to make some suitable morphs when the target spot translates on the screen, and the group theory can describe these morph operators well.<br>The basic LTE was established for a normal nonimaging optical system, which is to design an optical element for redirecting the lights from the source so that a prescribed light distribution is generated on a given target. A translation light taming equation(T-LTE) was derived for the case of only spot translating on the target screen, and an optical translation group(OTG) was introduced for describing all of the morph operators of the optical surface caused by light spot translation. There are multiple solutions for the same T-LTE, but the uniqueness of the T-LTE solution is necessary for OTG. Fortunately, the eikonal-energy(KE) mapping method can guarantee the uniqueness of the T-LTE solution, where K is the optical path length. The supporting quadric method(SQM) is one of the KE mapping methods when the nonimaging optical system has only one optical surface to be resolved. The LTE with SQM was deduced, and the OTG can be discussed in K-space. A deep neural network(DNN) was introduced to fit the KE mapping and spot translating operators to obtain the required optical surface. Taking the uniform square spot as an example, the SQM generated the sample data of spot translation to train DNN. The optical simulation results show that the error between the light distribution generated by the DNN and the standard uniform square spot is small, all within the order of 10<sup>-3</sup>, which indicates that the DNN and KE mapping method successfully realized the function of the OTG. There is a guiding significance for intelligent nonimaging optical design.

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Array signal recovery algorithm for a single-RF-channel DBF array

Cited by 3 publications

References 41 publications

Low-Cost Nested-MIMO Array for Large-Scale Wireless Sensor Applications

Low-Cost Nested-MIMO Array for Large-Scale Wireless Sensor Applications

Special issue: advanced techniques for radar signal processing

Realization of translation group in optical design with deep nerual network under eikonal-energy mapping

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