Real-time multichannel airborne radar measurements

Little, Michael O.; Perry, W.P.

doi:10.1109/nrc.1997.588238

Cited by 18 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Flight experiments were performed with this Paragon machine installed onboard an airborne platform as part of the Real-Time Multi-Channel Airborne Radar Measurements (RTMCARM) program [3,14,15]. A sequence of CPI data sets collected by the radar were sent to these 25 nodes in a round robin manner where all three processors in each node worked on every single CPI in parallel and, therefore, no inter-node communication was needed.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of a Parallel Pipeline Computational Model for Space-Time Adaptive Processing

Liao¹,

Choudhary²,

Weiner

et al. 2005

J Supercomput

View full text Add to dashboard Cite

This paper presents further results on the design and implementation of various optimizations based on our earlier work of developing a parallel pipelined model for the computational intensive applications that have multiple processing tasks. Performance evaluation of this model was done by using a real-time airborne radar application that employs a Space-Time Adaptive Processing (STAP) algorithm. This paper focuses on the following four issues: (1) The tradeoffs between increasing the throughput and reducing the latency are examined in more detail when allocating processors among different processing tasks. (2) A multi-threaded design is incorporated into the pipeline model and implemented on a massively parallel computer with symmetric multi-processor nodes, which shows enhanced performance. (3) The disk I/O is incorporated into the parallel pipeline to study its effect on performance in which two I/O task designs have been implemented: embedding I/O in the pipeline or having a separate I/O task. By using a double buffering approach together with the asynchronous I/O, the overall pipeline performance scales well as the number of processors increases. (4) From the comparison of the two I/O implementations, it is discovered that the latency may be improved when merging multiple tasks into a single task. The effect of reorganizing the task structure of the pipeline is discussed in detail. All the performance results shown in this work demonstrate the linear scalability the parallel pipeline model can achieve using a production radar application. Although this paper focuses on the implementation of the parallel pipeline model and uses the results from a STAP application to support the claims of the discovered properties for this pipeline, this model is also applicable to many other types of applications with similar computational characteristics.

show abstract

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of a Parallel Pipeline Computational Model for Space-Time Adaptive Processing

Liao¹,

Choudhary²,

Weiner

et al. 2005

J Supercomput

View full text Add to dashboard Cite

show abstract

“…In this subsection, MCV−STAP and BAMCV−STAP are applied to the public available data set, the Multi−Channel Airborne Radar Measurements (MCARM) data set [61], to verify their attractiveness. Some indispensable parameters of the radar system are listed in Table 7.…”

Section: Measured Datamentioning

confidence: 99%

Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder

et al. 2022

View full text Add to dashboard Cite

Due to the shortage of independent and identically distributed (i.i.d.) training samples, space−time adaptive processing (STAP) often suffers remarkable performance degradation in the heterogeneous clutter environment. Sparse recovery (SR) techniques have been introduced into STAP for the benefit of the drastically reduced training requirement, but they are incompletely robust for involving the tricky selection of hyper−parameters or the undesirable point estimation for parameters. Given this issue, we incorporate the Multiple−measurement Complex−valued Variational relevance vector machines (MCV) to model the space−time echoes and provide a Gibbs−sampling−based method to estimate posterior distributions of parameters accurately. However, the Gibbs sampler require quantities of iterations, as unattractive as traditional Bayesian type SR−STAP algorithms when the real−time processing is desired. To address this problem, we further develop the Bayesian Autoencoding MCV for STAP (BAMCV−STAP), which builds the generative model according to MCV and approximates posterior distributions of parameters with an inference network pre−trained off−line, to realize fast reconstruction of measurements. Experimental results on simulated and measured data demonstrate that BAMCV−STAP can achieve suboptimal clutter suppression in terms of the output signal to interference plus noise ratio (SINR) loss, as well as the attractive real−time processing property in terms of the convergence rate and computational loads.

show abstract

“…At the end of the last century, by analyzing some measured datasets, such as the mountaintop dataset collected by Defense Advanced Research Projects Agency (DARPA) [13] and the multi-channel airborne radar measurement (MCARM) data collected by the Air Force Research Laboratory at Rome [14], researchers began to pay attention to another challenging problem, i.e., heterogeneous clutter environments, that limits the practical use of STAP [15]. Before this practice, it is usually assumed that the training samples employed to generate 2D weights are i.i.d with the cell under test (CUT).…”

Section: Introductionmentioning

confidence: 99%

Clutter Suppression for Wideband Radar STAP

Zhu

Shen

et al. 2022

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Space-time adaptive processing (STAP) has been known as a leading technique for airborne/spaceborne radar to detect ground slow-moving targets such as vehicles or tanks. Traditional STAP theory is based on the assumption of narrowband or "zero-bandwidth", where the decorrelation within the space-time snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid, due to the deteriorated decorrelation of the received signals within the space-time snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the twodimensional (2D) frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP methods is widened, resulting in a relative poor ability to detect slowmoving targets. In this paper, we focus on the clutter suppression for wideband radar STAP. A general signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the 2D spectrum of the ground clutter and quantitatively describe the 2D spreading of the ground clutter on the Doppler-spatial frequency plane. Finally, a 2D keystone transform algorithm, referred to as space-time keystone transform (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2D frequency domain caused by increasing bandwidths. Simulation results demonstrate that the ST-KT improves the performance of wideband STAP (W-STAP) methods in terms of the output signal-to-clutter plus noise ratio (SCNR) of moving targets.

show abstract

Real-time multichannel airborne radar measurements

Abstract: This paper describes a series of airborne radar experiments that were performed using an active aperture digital beamforming phased array radar designed to demonstrate real time space-time adaptive processing on an Intel high performance computer.

Cited by 18 publications

References 0 publications

Performance Evaluation of a Parallel Pipeline Computational Model for Space-Time Adaptive Processing

Performance Evaluation of a Parallel Pipeline Computational Model for Space-Time Adaptive Processing

Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder

Clutter Suppression for Wideband Radar STAP

Contact Info

Product

Resources

About