Leveraging Software-Defined Radio Techniques in Multichannel Digital Weather Radar Receiver Design

Meier, John; Kelley, Redmond; Isom, Bradley; Yeary, Mark; Palmer, Robert D.

doi:10.1109/tim.2011.2178670

Cited by 30 publications

(8 citation statements)

References 23 publications

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“…IF signals produced by the downconversion units are then passed to 1 of 5 eight-channel digital receivers, also designed and built at OU and shown in Fig. 5b (Meier et al 2012). The sampling rate is 40 MS s 21 and utilizes the latest in software-defined radio technology to produce 14-bit in-phase and quadrature (IQ) data streams.…”

Section: April 2013 I S O M E T a Lmentioning

confidence: 99%

The Atmospheric Imaging Radar: Simultaneous Volumetric Observations Using a Phased Array Weather Radar

Isom

Palmer

Kelley

et al. 2013

Journal of Atmospheric and Oceanic Technology

Self Cite

102

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Mobile weather radars often utilize rapid-scan strategies when collecting observations of severe weather. Various techniques have been used to improve volume update times, including the use of agile and multibeam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field of view (FOV) of the radar, which is defined by a broad transmit beam. As a result, imaging radars provide update rates significantly exceeding those of existing mobile radars, including phased arrays. The Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU) is engaged in the design, construction, and testing of a mobile imaging weather radar system called the atmospheric imaging radar (AIR). Initial tests performed with the AIR demonstrate the benefits and versatility of utilizing beamforming techniques to achieve high spatial and temporal resolution. Specifically, point target analysis was performed using several digital beamforming techniques. Adaptive algorithms allow for improved resolution and clutter rejection when compared to traditional techniques. Additional experiments were conducted during two severe weather events in Oklahoma. Several digital beamforming methods were tested and analyzed, producing unique, simultaneous multibeam measurements using the AIR.

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Section: April 2013 I S O M E T a Lmentioning

confidence: 99%

The Atmospheric Imaging Radar: Simultaneous Volumetric Observations Using a Phased Array Weather Radar

Isom

Palmer

Kelley

et al. 2013

Journal of Atmospheric and Oceanic Technology

Self Cite

102

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“…The closed-form solution of (6) can constrain the interferences and keep simultaneous response of multiple expected signals. To obtain the optimal weight vector, we need to compute two matrix inversions from (6), i.e., one is the inverse operation of covariance matrix R xx , with size M × M, the other is the inverse operation of matrix C H R −1 xx C, with size D × D. For large arrays or adaptive phase array radar (APAR) [18], the number of array sensors is very large, so the computational complexity of covariance matrix inversion is very high. So how to decrease the computational burden of CF-LCMV is a serious problem.…”

Section: Problem Formulationmentioning

confidence: 99%

Robust Adaptive LCMV Beamformer Based On An Iterative Suboptimal Solution

Chu¹,

Li²,

Guo³

2015

RADIOENGINEERING

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Abstract. The main drawback of closed-form solution of linearly constrained minimum variance (CF-LCMV) beamformer is the dilemma of acquiring long observation time for stable covariance matrix estimates and short observation time to track dynamic behavior of targets, leading to poor performance including low signal-noise-ratio (SNR), low jammer-to-noise ratios (JNRs) and small number of snapshots. Additionally, CF-LCMV suffers from heavy computational burden which mainly comes from two matrix inverse operations for computing the optimal weight vector. In this paper, we derive a low-complexity Robust Adaptive LCMV beamformer based on an Iterative Suboptimal solution (RAIS-LCMV) using conjugate gradient (CG) optimization method. The steepest descent weight updated strategy is adopted to obtain a simple iteration process. The merit of our proposed method is threefold. Firstly, RAIS-LCMV beamformer can reduce the complexity of CF-LCMV remarkably. Secondly, RAIS-LCMV beamformer can adjust output adaptively based on measurement and its convergence speed is comparable. Finally, RAIS-LCMV algorithm has robust performance against low SNR, JNRs, and small number of snapshots. Simulation results demonstrate the superiority of our proposed algorithms. KeywordsRobust adaptive beamformer, LCMV, iterative suboptimal solution, computational analysis

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“…A high-level block diagram of the system components is given in Figure 2. More detailed descriptions of the individual subsystems can be found in [16], [17].…”

Section: The Airmentioning

confidence: 99%

The atmospheric imaging radar: System validation and observations of severe weather

Isom

Palmer

Kelley

et al. 2012

2012 IEEE Radar Conference

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Rapid updates are a highly desired feature in the field of mobile weather radars. Various techniques have been utilized to improve volume update times, including the use of agile and multi-beam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field-of-view (FOV) of the radar, which is defined by the broad transmit beam. As a result, imaging radars provide update rates exceeding those of existing mobile radars, including phased arrays. The Atmospheric Radar Research Center (ARRC) at the University of Oklahoma (OU) is currently engaged in evaluating a mobile X-band imaging radar system by through observations of severe weather events.

show abstract

Leveraging Software-Defined Radio Techniques in Multichannel Digital Weather Radar Receiver Design

Cited by 30 publications

References 23 publications

The Atmospheric Imaging Radar: Simultaneous Volumetric Observations Using a Phased Array Weather Radar

The Atmospheric Imaging Radar: Simultaneous Volumetric Observations Using a Phased Array Weather Radar

Robust Adaptive LCMV Beamformer Based On An Iterative Suboptimal Solution

The atmospheric imaging radar: System validation and observations of severe weather

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