Signal Processing Approaches to the Detection and Localization of Gas Chemical Sources using Partially Selective Sensors

Abstract

Due to recent progress, higher-order chemical instrumentation provides large amounts of data which need automated processing in order to extract relevant information. In most cases, the raw signals or spectra are too complex for manual analysis. The ability to detect, identify and quantitate chemical substances in gas phase in field operations is required in a huge number of applications. Among them, I would like to highlight the need for chemical sensing on diverse humanitarian, safety and security applications. In these cases, it becomes extremely important to continuously monitor the environments where chemicals are spread in order to be ready to act when abnormal events are discovered.

In most critical scenarios the sample can not just be taken to the laboratory and analyzed, since an immediate answer is needed. In some other scenarios, the exploration of the area must be performed because the localization of the gas source or material of interest is unknown. This exploration can be performed using multiple mobile sensors in order to localize the chemical source or material.

Different sensing technologies have been successfully used to detect and identify different chemical substances (gases or volatile compounds). These compounds could be either toxic or hazardous, or they can be signatures of the materials to be detected, for instance, explosives or drugs.

Among these technologies, mobility based analyzers provide fast responses with high sensitivity.

However, IMS instruments are not exempt of problems. Typically, they provide moderate selectivity, appearing overlapped peaks in the spectra. Moreover, the presence of humidity makes peaks wider, thus worsening the resolving power and the resolution. Furthermore, the response of IMS is non-linear as substance concentration increases and more than one peak can appear in the spectra due to the same compound.

In the present thesis, these problems are addressed and applications using an Ion Mobility Spectrometer (IMS) and a Differential Mobility Analyzer (DMA) are shown. It is demonstrated that multivariate data analysis tools are more effective when dealing with these technologies.

For the first time, multivariate data analysis tools have been applied to a novel DMA. It is shown that DMA could be established as a good instrumentation for the detection of explosives and the detection and quantitation of VOCs.

Furthermore, Multivariate curve resolution Alternating Least Squares (MCR-ALS) is shown to be suitable to analyze IMS spectra qualitatively when interfering chemicals appear in the spectra and even when their behaviour is non-linear. Partial Least Squares (PLS) methods are demonstrated to work properly for the quantitative analysis of these signals; from this analysis the chemical concentrations of the target substances are obtained.

It is also demonstrated in this thesis that the quantitative measurements from these sensors can be integrated in a gas source localization algorithm in order to improve the localization of the source in those scenarios where it is required. It is shown that the new proposal works significantly better in cases where the source strength is weak. This is illustrated presenting results from simulations generated under realistic conditions.

Moreover, real-world data were obtained using a mobile robot mounting a photo ionization detector (PID). Experiments were carried out under forced ventilation and turbulences in indoors and outdoors environments. The results obtained validate the simulation results and confirm that the new localization algorithm can effectively operate in real environments.

Debido a los progresos recientes, la instrumentación química genera mayores volúmenes de datos los cuales requieren de un procesado automático con la finalidad de extraer la información relevante, ya que un análisis manual no suele ser viable debido a la elevada complejidad de los datos.

La habilidad de detectar, identificar y cuantificar sustancias químicas en fase gas en operaciones de campo es requerida en un gran número de aplicaciones. Entre ellas, aplicaciones humanitarias y de seguridad. En estos casos, la monitorización continua de los entornos es extremadamente importante, ya que se debe estar alerta de eventos anormales.

En los escenarios más críticos, debe realizarse una exploración del área porque la posición de la fuente de gas de interés es desconocida. Esta exploración puede realizarse usando múltiples robots.

Diferentes tecnologías de sensores se han aplicado con éxito a la detección e identificación de diferentes sustancias químicas (gases o compuestos volátiles). Estos compuestos pueden ser tóxicos, peligrosos, o precursores de explosivos o drogas.

De entre estas tecnologías, los analizadores basados en movilidad iónica (IMS) proporcionan rápidas respuestas con gran sensibilidad.

Sin embargo, estos instrumentos no están exentos de problemas. Típicamente, proporcionan una moderada selectividad, apareciendo picos solapados en los espectros. Además, la presencia de humedad provoca que los picos se ensanchen, así empeorando la resolución. Además, la respuesta de IMS es no lineal al incrementar la concentración y es posible que más de un pico debido al mismo compuesto aparezca en el espectro.

En la presente tesis se trata con estos problemas y se demuestra que las herramientas de análisis de datos multivariantes son más efectivas que las herramientas típicas univariantes al tratar con tecnologías de movilidad iónica (IMS y DMA), especialmente para el análisis cualitativo y cuantitativo de sus espectros.

Además, se demuestra que las medidas cuantitativas pueden integrarse de manera efectiva en un algoritmo de localización de fuentes químicas. Los resultados obtenidos (simulaciones realistas y datos reales) muestran que el algoritmo desarrollado durante la tesis puede funcionar especialmente bien en situaciones en las que la potencia de emisión de la fuente a detectar sea débil.