Compressive Sensor Network Signal Source Localization
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Solution Overview
Problem
Conventional signal source localization techniques require all sensors in a network to operate at high sampling rates, leading to increased cost, complexity, and power consumption, which is inefficient and unsuitable for deployment in remote or harsh environments.
Innovation Solution
Implementing compressive sampling in a sensor network where only a subset of sensors operate at or above the Nyquist rate, while others generate compressive measurements at a lower sampling rate, allowing for accurate signal source localization without the need for high-rate sampling and transmission across all sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all sensors operate at or above the Nyquist rate, then signal source localization accuracy is maintained, but hardware cost, complexity and power consumption increase significantly
Solution Approach 1:
The sensor network is segmented into two groups: a reference sensor that operates at the full Nyquist rate and multiple compressive sensors that operate at reduced rates. This segmentation allows the system to maintain localization accuracy through the reference sensor while reducing overall system complexity and cost through the compressive sensors.
Solution Approach 2:
The sampling rate parameter is changed for different sensors in the network. The reference sensor maintains the Nyquist sampling rate to preserve signal integrity, while compressive sensors use lower sampling rates combined with compressive sampling techniques to reduce hardware requirements while still enabling accurate localization through coordinated processing.
2Measurement precision
If all sensors transmit samples at high sampling rates, then localization results are accurate, but power consumption and transmission resources are excessively consumed
Solution Approach 1:
The sensor network is segmented into a reference sensor that transmits at full rate and compressive sensors that transmit at reduced rates. This segmentation reduces the total power consumption and transmission resource usage while maintaining localization accuracy through the coordinated processing of compressed measurements alongside the reference sensor data.
Solution Approach 2:
The transmission sampling rate parameter is reduced for compressive sensors while maintaining the ability to achieve accurate localization results through compressive sampling reconstruction techniques, thereby significantly reducing power consumption and transmission bandwidth requirements.
3Device complexity
If compressive sampling is used at lower sampling rates, then hardware resources and power consumption are reduced, but signal source localization accuracy may be adversely impacted
Solution Approach 1:
A reference sensor operating at the full Nyquist rate serves as an intermediary that provides high-quality reference measurements. These reference measurements are used in coordinated processing with the compressive sensor measurements to reconstruct the signal and determine time differences of arrival, thereby enabling accurate localization despite the lower sampling rates of the compressive sensors.
Solution Approach 2:
The mechanical sampling system of compressive sensors operating at reduced rates is supplemented by the reference sensor system operating at full rate. This substitution approach replaces the need for all sensors to operate at high rates with a hybrid system where compressive sampling is combined with reference-based reconstruction, achieving both reduced hardware complexity and maintained accuracy.
Data Source
AI summary
In one aspect, a method for performing signal source localization is provided. The method comprises the steps of obtaining compressive measurements of an acoustic signal or other type of signal from respective ones of a plurality of sensors, processing the compressive measurements to determine time delays between arrivals of the signal at different ones of the sensors, and determining a location of a source of the signal based on differences between the time delays. The method may be implemented in a processing device that is configured to communicate with the plurality of sensors. In an illustrative embodiment, the compressive measurements are obtained from respective ones of only a designated subset of the sensors, and a non-compressive measurement is obtained from at least a given one of the sensors not in the designated subset, with the time delays between the arrivals of the signal at different ones of the sensors being determined based on the compressive measurements and the non-compressive measurement.