A method for real-time detection of dust devils at a given location is based on identifying the abrupt, temporary decreases in atmospheric pressure that are characteristic of dust devils as they travel through that location. The method was conceived for use in a study of dust devils on the Martian surface, where bandwidth limitations encourage the transmission of only those blocks of data that are most likely to contain information about features of interest, such as dust devils. The method, which is a form of intelligent data compression, could readily be adapted to use for the same purpose in scientific investigation of dust devils on Earth.

A Third-Order Polynomial Fit with 3-standard-deviation limits was superimposed on a 10-minute sliding time window of Mars atmospheric-pressure readings. Two dust devils were detected as negative deviations of more than 3 standard deviations.
In this method, the readings of an atmospheric-pressure sensor are repeatedly digitized, recorded, and processed by an algorithm that looks for extreme deviations from a continually updated model of the current pressure environment. The question in formulating the algorithm is how to model current “normal” observations and what minimum magnitude deviation can be considered sufficiently anomalous as to indicate the presence of a dust devil. There is no single, simple answer to this question: any answer necessarily entails a compromise between false detections and misses.

For the original Mars application, the answer was sought through analysis of sliding time windows of digitized pressure readings. Windows of 5-, 10-, and 15-minute durations were considered. The windows were advanced in increments of 30 seconds. Increments of other sizes can also be used, but computational cost increases as the increment decreases and analysis is performed more frequently. Pressure models were defined using a polynomial fit to the data within the windows. For example, the figure depicts pressure readings from a 10-minute window wherein the model was defined by a third-degree polynomial fit to the readings and dust devils were identified as negative deviations larger than both 3 standard deviations (from the mean) and 0.05 mbar in magnitude. An algorithm embodying the detection scheme of this example was found to yield a miss rate of just 8 percent and a false-detection rate of 57 percent when evaluated on historical pressure-sensor data collected by the Mars Pathfinder lander. Since dust devils occur infrequently over the course of a mission, prioritizing observations that contain successful detections could greatly conserve bandwidth allocated to a given mission. This technique can be used on future Mars landers and rovers, such as Mars Phoenix and the Mars Science Laboratory.

This work was done by Kiri Wagstaff of Caltech for NASA’s Jet Propulsion Laboratory. NPO-44724

Topics:
Exteriors Mathematical models Physical Sciences Powertrains Pressure Simulation and modeling Transmissions Windows and windshields
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Real-Time Detection of Dust Devils From Pressure Readings

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This article first appeared in the March, 2009 issue of NASA Tech Briefs Magazine (Vol. 33 No. 3).

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Overview

The document titled "Real-Time Detection of Dust Devils from Pressure Readings" (NPO-44724) discusses a novel approach developed by NASA's Jet Propulsion Laboratory (JPL) for detecting dust devils on Mars using atmospheric pressure sensors. Traditionally, dust devils have been identified through images captured by landers or rovers, which require significant bandwidth for data transmission. However, the document highlights an alternative method that leverages pressure readings to detect these phenomena in real-time, thereby enhancing the scientific return of Mars missions.

Dust devils are characterized by a temporary and abrupt drop in atmospheric pressure, which can be detected by pressure sensors. The Mars Pathfinder lander, part of its meteorology package, was equipped with such a sensor, and during its 83-sol mission, 79 dust devils were identified through ground-based analysis. The primary objective of this research was to determine the feasibility of onboard analysis of pressure sensor data, allowing for immediate detection of dust devils without waiting for data to be sent back to Earth.

The solution involves adapting an existing method (Murphy and Nelli, 2002) to operate in an online setting. The researchers analyze a sliding window of pressure observations, fitting a polynomial to the data to identify extreme deviations from the mean. The window is advanced every 30 seconds, with the duration and type of polynomial fit being adjustable parameters. The study experimented with different window durations (5, 10, and 15 minutes) and polynomial degrees (1st, 2nd, and 3rd). The optimal results were achieved with a 10-minute window and a 3rd-degree polynomial fit, resulting in an 8% miss rate and a 57% false detection rate.

This approach represents a significant advancement in real-time analysis, as previous methods had only been applied to pressure data after it was transmitted to Earth. The findings suggest that onboard processing of pressure sensor data can effectively enhance the detection of dust devils, providing valuable insights into Martian atmospheric phenomena.

The document serves as a technical support package under NASA's Commercial Technology Program, aiming to disseminate aerospace-related developments with broader technological, scientific, or commercial applications. For further inquiries, contact information for JPL's Innovative Technology Assets Management is provided.