What are simple ways to improve sensor performance?

Although improving a sensor’s actual performance is not usually possible, with proper selection, system design, and integration, improving sensor performance at the system level is very possible. Tools available for sensor performance optimization include selection, placement, operation, and signal conditioning/processing.

It begins by selecting a sensor with the appropriate range, resolution, sensitivity, accuracy, response time, and other performance specifications. During the design process, minimize/manage noise and interference sources. Once in the field, regular calibration can be important.

Sensor performance pillars

The challenges related to optimizing sensor system operation can be complex, but they can be broken down into four primary considerations: selection, placement, operation, and data processing (Figure 1). Optimizing each of those performance pillars can be envisioned by answering a series of five questions: What is the objective? Why is the measurement important? Where is the system operating? When is the lifecycle stage of the design? How will the data be used?

Figure 1. Analytical framework for sensor system optimization. (Image: MDPI sensors)

Selecting the correct sensor is an important starting point. The device must be suited for the operating environment and be specified to deliver the required accuracy, resolution, range, and other performance criteria.

For example, linearity and drift are important considerations in many sensing applications. Some sensors, like thermistors, are highly nonlinear, and compensation can be provided in software. Other sensor technologies, like Hall effect sensors, have more linear outputs over the measurement range.

Sensor placement considerations include the optimal number and location of sensors to be deployed in a system. Sensors should be placed in an optimal location for accurate measurements, considering factors like accessibility, operating environment, and potential sources of interference.

Calibration for better performance

Once in the field, regular calibration is important for maintaining the accuracy and precision of the sensor output. Depending on the sensing technology, sensors must be protected from external interference from temperature, humidity, electromagnetic interference, vibration, and other factors.

Some industrial processes require detecting the proximity of a variety of materials. That can be accomplished using an inductive proximity sensor and applying the required correction factor, essentially recalibrating the sensor system based on the material properties. For example, if a sensor is rated for a 4 mm operating distance for detecting steel, and the new target is aluminum, a correction factor of 0.30 to 0.45 can be calculated (calibrated) to the operating distance of 1.2 to 1.8 mm (Table 1).

Table 1. Compensation factors for inductive proximity sensing. (Table: Balluff Automation)

Sensor operation considerations are broad. They include how sensors are powered, how the data is accessed, and maintenance needs like calibration. Regular maintenance and monitoring of sensors can ensure they are functioning correctly. This includes cleaning, repairing, or replacing sensors as needed. For some sensors and applications, like medical devices, regular calibration is mandatory, and the calibration chain must be traceable to an ultimate reference.

Filtering and fusing

Effective data processing is an important activity to maximize the quantity and quality of sensor data. There are several techniques for improving sensor performance, beginning with filtering to remove noise and improve the quality of the output signal. For example, MEMS tilt sensors can have accuracy better than ±0.15° at ambient temperature. However, acceleration can cause errors up to a few degrees. A simple way to stabilize the output is to add a low-pass filter (Figure 2).

Figure 2. How a low-pass filter can smooth tilt sensor data. (Image: TE Connectivity)

In more complex applications, sensor fusion can combine data from multiple sensor technologies to compensate for limitations in the individual sensors. Combining data from cameras, LiDAR, and radar enhances the accuracy and robustness of autonomous vehicle situational awareness by leveraging the unique strengths of each sensor.

In an inertial measurement unit (IMU), each sensor has its own limitations and potential errors. For example, gyroscopes can suffer from drift over time, while accelerometers are more affected by noise. Sensor fusion helps mitigate these errors by combining the strengths of each sensor and compensating for their weaknesses.

Summary

There are various methods available for improving sensor system performance. Improved performance can be realized through a thorough understanding of system requirements and sensor capabilities based on the four sensor system performance pillars.