An optimized embedded target detection system using acoustic and seismic sensors

Abstract

Abstract-Detection of targets using low power embedded devices has important applications in border security and surveillance. In this paper, we build on recent algorithmic advances in sensor fusion, and present the design and implementation of a novel, multi-mode embedded signal processing system for detection of people and vehicles using acoustic and seismic sensors. Here, by "multi-mode", we mean that the system has available a complementary set of configurations that are optimized for different trade-offs. The multimode capability delivered by the proposed system is useful to supporting long lifetime (long term, energy-efficient "standby" operation), while also supporting optimized accuracy during critical time periods (e.g., when a potential threat is detected). In our target detection system, we apply a strategically-configured suite of single-and dual-modality signal processing techniques together with dataflow-based design optimization for energyefficient, real-time implementation. Through experiments using a Raspberry Pi platform, we demonstrate the capability of our target detection system to provide efficient operational tradeoffs among detection accuracy, energy efficiency, and processing speed.

Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks

“…Details about this sensor setup and experiments can be found in [17]. This dataset has been previously used for target detection in [18,19], where the authors focus on detection/classification using seismic and acoustic sensors only. Some data samples from the dataset can be seen in Fig.…”

Software synthesis from dataflow schedule graphs

“…For details on the algorithms and applications associated with this classification system, we refer the reader to [25,26]. The case study in this paper goes beyond the simulation and prototyping experiments reported in [25,26] in its use of DSGs and DIF-DSG as central parts of the design and implementation process.…”

“…For details on the algorithms and applications associated with this classification system, we refer the reader to [25,26]. The case study in this paper goes beyond the simulation and prototyping experiments reported in [25,26] in its use of DSGs and DIF-DSG as central parts of the design and implementation process. The experiments described in [25,26] are carried using hand-implemented dataflow graphs that do not employ DSG modeling nor the DIF-DSG software synthesis tool.…”

“…The case study in this paper goes beyond the simulation and prototyping experiments reported in [25,26] in its use of DSGs and DIF-DSG as central parts of the design and implementation process. The experiments described in [25,26] are carried using hand-implemented dataflow graphs that do not employ DSG modeling nor the DIF-DSG software synthesis tool. However, these earlier studies provide useful LIDE-C actor implementations, which we apply in this case study.…”

“…In the classifier application presented in [26], four different modes of operation are provided for people/vehicle/ noise classification. Among these modes, we employ in our experiments only the ALFFS (Accumulation of Local Feature-level Fusion Scores) mode.…”