A MIMO radar benchmarking environment

Coogle, Richard A.; Glass, John D.; Smith, Larry; Miceli, Paul; Register, Andy H.; West, Philip D.; Blair, W.D.

doi:10.1109/aero.2011.5747423

Cited by 12 publications

(9 citation statements)

References 4 publications

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“…The benchmark is a radar simulation that includes various models for "real world" phenomena such as finite sensor resolution, false alarms, bistatic RCS, etc. For a high level description of the MIMO Benchmark, refer to [1]. Perhaps the most important aspect of the MIMO Benchmark is that it includes merged/extended measurements.…”

Section: Simulation/resultsmentioning

confidence: 99%

“…A MIMO radar is a coordinated series of networked radar installations that jointly illuminate a volume using orthogonal waveforms and jointly process the returns [1]. We assume that the radar system has N sensors that have the ability to transmit and receive.…”

Section: Mimo Radar Modelmentioning

confidence: 99%

“…The three targets will fly into formation with a 75 meter separation distance. The performance metrics of track completeness ratio and root mean square position error, which are defined in [1], are computed. Table 2 shows several parameters of interest for the PHD filter, and Table 3 shows our results.…”

Section: Default Tracking Solutionmentioning

confidence: 99%

“…The authors in [9] present a GM-PHD tracker that also includes a novel track management scheme. In this work, we present a similar algorithm as in [9] for the special case of a MIMO radar system with implementation in the ONR/GTRI MIMO Radar Benchmark [1]. The performance of our implementation of the GM-PHD filter will be compared to the performance of the default tracker in the Benchmark.…”

Section: Introductionmentioning

confidence: 95%

“…The added bistatic measurements are the result of MIMO radars transmitting orthogonal waveforms, which allow returns from multiple sensors to be differentiated [1]. If the time of each transmitted waveform is known, the received waveforms can be used to calculate a bistatic range.…”

Section: Introductionmentioning

confidence: 99%

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MIMO radar target tracking using the probability hypothesis density filter

Glass

Lanterman

2012

2012 IEEE Aerospace Conference

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Target tracking in a widely spread multiple input multiple output (MIMO) radar system requires joint processing of several measurements from multiple sensors. The probability hypothesis density (PHD) filter provides a promising framework to process these measurements, since it does not require any measurement-to-track associations. Furthermore, the PHD filter naturally handles a multi-target environment because of the lack of explicit data association. We implement a PHD filter in the GTRI/ONR MIMO Benchmark, and compare results against the Benchmark's default solution. We assume a linear Gaussian target model so that the posterior target intensity at any time step is a Gaussian mixture (GM). Under this assumption, the PHD filter has closed-form recursions and target state extraction is simplified. This paper focuses on our implementation of the GM-PHD filter in the MIMO Benchmark, along with practical issues such as track labeling and applying the filter for the case of multiple sensors.

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Section: Simulation/resultsmentioning

confidence: 99%

Section: Mimo Radar Modelmentioning

confidence: 99%

Section: Default Tracking Solutionmentioning

confidence: 99%