Aspect-Aware Target Detection and Localization by Wireless Sensor Networks.

Area of Science:

• Signal Processing
• Wireless Sensor Networks
• Target Detection and Localization

Background:

• Stealth targets with aspect-dependent reflections pose detection challenges.
• Wireless sensor networks (WSNs) are constrained by bandwidth and energy.
• Accurate target localization is crucial post-detection.

Purpose of the Study:

• To develop an effective generalized likelihood ratio test (GLRT) detector for stealth targets using WSNs.
• To simultaneously achieve target detection and location estimation under resource constraints.
• To investigate the impact of data quantization on detection and localization performance.

Main Methods:

• Development of a GLRT detector utilizing one-bit quantized sensor data.
• Proposal of an adaptive multi-bit quantization GLRT detector for enhanced precision.
• Derivation of the Cramer-Rao lower bound (CRLB) for target location estimation.

Main Results:

• The proposed GLRT detector with one-bit quantization outperforms existing methods like counting rules and scan statistics.
• Adaptive multi-bit quantization (specifically 2-bit) significantly improves detection and localization performance.
• The 2-bit quantization GLRT detector achieves substantially better results than the 1-bit version with minimal communication overhead increase.

Conclusions:

• Adaptive multi-bit quantization is a viable strategy for improving GLRT-based target detection and localization in WSNs.
• The proposed 2-bit quantization GLRT detector offers a favorable trade-off between performance gains and communication costs.
• This approach enhances the capabilities of WSNs for detecting and locating challenging stealth targets.