Multinomial pattern matching for high range resolution radar profiles

Koudelka, Melissa L.; Richards, John A.; Koch, Mark W.

doi:10.1117/12.720873

Cited by 9 publications

(4 citation statements)

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“…It is noted that the certain implementations assumeĈ = 0 and do not include it in the calculation. 1 …”

Section: Mpm Statistical Model and Trainingmentioning

confidence: 97%

“…The algorithms were original developed for synthetic aperture radar (SAR) ATR applications, and have since been utilized for classification of 1-D high range resolution (HRR) profiles as well as applied to other sensor modalities. 1,2 In addition to less sensitivity, these algorithms also have several other desirable traits. First, by effectively compressing the entire dynamic range of the SAR data into integer quantiles, less resources have to be utilized for the storage of signatures.…”

Section: Introductionmentioning

confidence: 98%

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Multinomial pattern matching revisited

Horvath

Rigling

2015

SPIE Proceedings

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Multinomial pattern matching (MPM) is an automatic target recognition algorithm developed for specifically radar data at Sandia National Laboratories. The algorithm is in a family of algorithms that first quantizes pixel value into N q bins based on pixel amplitude before training and classification. This quantization step reduces the sensitivity of algorithm performance to absolute intensity variation in the data, typical of radar data where signatures exhibit high variation for even small changes in aspect angle. Our previous work has focused on performance analysis of peaky template matching, a special case of MPM where binary quantization is used (N q = 2). Unfortunately references on these algorithms are generally difficult to locate and here we re-visit the MPM algorithm and illustrate the underlying statistical model and decision rules for two algorithm interpretations: the 1-of-K vector form and the scalar. MPM can also be used as a detector and specific attention is given to algorithm tuning where "peak pixels" are chosen based on their underlying empirical probabilities according to a reward minimization strategy aimed at reducing false alarms in the detection scenario and false positives in a classification capacity. The algorithms are demonstrated using Monte Carlo simulations on the AFRL civilian vehicle dataset for variety of choices of N q .

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“…It is noted that the certain implementations assumeĈ = 0 and do not include it in the calculation. 1 …”

Section: Mpm Statistical Model and Trainingmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 98%

Multinomial pattern matching revisited

Horvath

Rigling

2015

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…The approximation of the MPM test statistic in the general N q case is direct extension of the N q = 2 case and is accomplished by first calculating the mean and variance of the per-pixel penalties under the model given in (17). Beginning with the mean we have…”

Section: Mpm Performance Prediction Under Awgnmentioning

confidence: 99%

Performance prediction of multinomial pattern matching under ideal point response variations

Horvath

Rigling

2016

Algorithms for Synthetic Aperture Radar Imagery XXIII

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Typical ATR performance metrics are based on the results of empirical studies on truthed datasets where it is difficult to fully sample the space of expected variation yielding potentially false generalizations of empirical performance results to a rigorous performance assessment. This is especially difficult when many sources of variation can exist in the data, typically referred to as operating conditions. Here, we propose a general method to analytically predict the classification performance of the MPM algorithm when samples are assumed realizations of two separate MPM template parametrizations differing as a function of a single, conditionally independent operation condition. This performance prediction approach is then used to investigate the role the ideal point response has in the classification performance of synthetic aperture radar targets. The exact trade-off we study is coherently processing an aperture to yield a single higher resolution image versus non-coherently processing the aperture to yield multiple lower resolution looks of a scene. Experiments are conducted using SAR imagery from the Air Force Research Laboratories Civilian Vehicle dataset. An additional performance analysis presents an analytic approach to predict algorithm performance under additive white Gaussian noise for a general N q allowing the performance loss under IPR variations to be mapped to an equivalent loss in signal-to-noise ratio.

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“…In applications of automatic target recognition (ATR), 4,5 it has been found in the past that, similar to display, there is benefit to limiting sensitivity to high-intensity returns, which can vary widely between target image measurements, and sensitivity to variations at the low-end that can be dominated by scintillation, speckle, and other apparently random image phenomology effects. This led to use of histogram-equalized quantization, where quantization levels are spread uniformly through the population of intensity values.…”

Section: Introductionmentioning

confidence: 99%

SAR image quantization strategies for improved human/machine interpretability

Rigling,

Majumder,

Zelnio

2024

Algorithms for Synthetic Aperture Radar Imagery XXXI

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Synthetic aperture radar (SAR) images can have broad dynamic range depending on the responses of the content of the observed scenes. This can vary from intense specular responses from manmade objects to low/no-return areas due to shadowing or forward scatter off electromagnetically smooth surfaces. To display SAR images for human consumption or to input them modern recognition algorithms, one must usually adopt a finite precision display. This is typically no more than 8 bits of quantization, but in some instances, utilization of fewer bits may present operational advantages. This paper explores three distinct techniques for SAR image quantization and provides examples with measured imagery.

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Multinomial pattern matching for high range resolution radar profiles

Cited by 9 publications

References 0 publications

Multinomial pattern matching revisited

Multinomial pattern matching revisited

Performance prediction of multinomial pattern matching under ideal point response variations

SAR image quantization strategies for improved human/machine interpretability

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