The basics of DSP for use in intelligent sensor applications: Part 3

Creed Huddleston

July 6, 2010

Creed Huddleston

A General Sensor Signal-processing Framework
Were now ready to set up a general sensor signal-processing framework for sensor applications. Like all good designs, the framework is deceptively simple; the key is to implement it reliably so that it performs all of its required tasks accurately, on time, every time. The framework is shown in Figure 2.19 below.

Figure 2.19. General Sensor Signal-processing Framework

((To view an expanded image, click here)

. The framework must be constructed as a hard real-time system; i.e., its response to system inputs and events must be deterministic (occur within a fixed time) and all processing for a given input or event must be finished before the next input or event occurs, at least for the critical processing sections.

Less critical sections, such as the communication protocol handler, are important, but they can occur in soft real-time; they must be capable of processing all inputs or events eventually, but they can queue up those inputs or events for processing at a time thats convenient for the application.

Signal Conditioning and Acquisition
The signal conditioning and acquisition section is responsible for performing any required conditioning of the analog input signal to limit the frequency spectrum to a band that can be successfully processed, to amplify the signal level to an appropriate range for digitization, and to digitize the resulting analog input signal.

The output of this section is a stream of sampled data that can then be processed numerically by the rest of the system.

Pre-analysis Filtering. Once the raw physical property signal has been sampled, its often necessary to apply application-specific filtering to the signal to remove unwanted noise or to somehow shape the signal into a more useful form.

The filtering is typically performed immediately after acquisition so that processing algorithms later in the signal chain are able to use relatively clean data, hopefully yielding better results.

Signal Linearization. Sometimes the parameter of interest does not vary linearly with the physical property being measured. A common example is a thermocouple signal, which has a complex polynomial relationship between its voltage and the corresponding temperature.

In such cases, the signal often needs to be linearized so that it can be dealt with more easily by the parameter analysis section. The specific linearization technique employed will vary by the type of property being measured.

Parameter Analysis. The parameter analysis is also highly application-specific. Although limited only by the designers imagination, some typical operations are parameter transformation (in which the measured signal is converted to the desired corresponding parameter value mathematically), frequency analysis, and limit comparison. Frequently, this is the most complex aspect of the sensor system and the area in which the most value can be added to the product.

Post-analysis Filtering. Once a parameter value has been computed, its not uncommon to filter those values to smooth the data for use by other components in the system. As with the pre-analysis filtering, the particular type of filter employed is application-specific.

Error Detection and Handling. While the parameter analysis section is generally where the most unique value is added to the sensor system, the error detection and handling section can make or break the viability of the system. The ability to detect and to recover from errors can separate a product from its competition, particularly in situations in which the penalty for failure can be catastrophic.

Simple error detection might include checking for the presence of the sensor element and verifying that extracted parameter values are in a reasonable range. More advanced error detection might include diagnostics to alert the user before an actual failure occurs.

Communication. The final element in the framework is the communication section. It is this section that reports all of the information gathered by an intelligent sensor and that allows the user to configure it for operation, so it is absolutely critical that this interface be robust and reliable.

A wide variety of communication interfaces are available, from RS-232 to Control Area Network (CAN) to Ethernet to wireless, though not all systems support all interfaces. The designer must select an interface that provides the easiest integration of the product with other elements of the system while staying within the cost and reliability constraints necessary for a particular application.

A Final Word
A thorough knowledge of DSP is invaluable to the development of robust sensor systems, and this treatment has been meant to instill an intuitive, not exhaustive, understanding.

Nevertheless, with this understanding it is possible to develop a general framework for the digital analysis and reporting of sensor information, one that will be useful in your subsequent work designing sensor systems for specific applications.

To read Part 1 in this series, go to Foundational DSP Concepts for Sensors
To read Part 2 in this series, go to Cleaning Up the Signal - Introducing Filters.

Creed Huddleston is President of Real-Time by Design, LLC, specializing in the design of intelligent sensors, located in Raleigh-Durham, North Carolina.

This series of articles is based on material from Intelligent Sensor Design by Creed Huddleston, used with permission from Newnes, a division of Elsevier. Copyright 2007. For more information about this title and other similar books, please visit www.elsevierdirect.com.