Wideband ranging is essential for numerous emerging applications that rely on accurate location awareness. The quality of range information, which depends on network intrinsic properties and signal processing techniques, affects the localization accuracy. A popular class of ranging techniques is based on energy detection owing to its low complexity implementation. This paper establishes a tractable model for the range information as a function of wireless environment, signal features, and energy detection techniques. Such a model serves as a corner- stone for the design and analysis of wideband ranging systems. Based on the proposed model, we develop practical soft-decision and hard-decision algorithms. A case study for ranging and local- ization systems operating in a wireless environment is presented. Sample-level simulations validate our theoretical results
Sensor radar networks enable important new appli- cations based on accurate localization. They rely on the quality of range measurements, which serve as observations for inferring a target location. In harsh propagation environments (e.g., indoors), such observations can be nonrepresentative of the target due to noise, multipath, clutter, and non-line-of-sight conditions leading to target misdetection, false-alarm events, and inaccurate localization. These conditions can be mitigated by selecting and processing a subset of representative observations. We introduce blind techniques for the selection of representative observations gathered by sensor radars operating in harsh environments. A methodology for the design and analysis of sensor radar networks is developed, taking into account the aforementioned impairments and observation selection. Results are obtained for noncoherent ultra-wideband sensor radars in a typical indoor environment (with obstructions, multipath, and clutter) to enable a clear understanding of how observation selection improves the localization accuracy
Counting people and things (targets) in a monitored area, also known as crowd-counting, enables several applications in diverse scenarios including smart building, intelligent transportation, and public safety. In many scenarios, device-free systems relying on the signal backscattering from targets are preferred to device-based systems relying on the communication with the targets via dedicated or personal devices. However, the use of conventional radar techniques (e.g., for multi-target detection) requires to associate a different set of measured data with each detected target. Data association is a redundant operation for counting and results in high complexity even with few targets. The need of lower dimensionality and complexity calls for signal features to associate the measured signals directly with the number of targets. This paper proposes a mathematical framework for the design of device-free counting systems. First, a maximum a posteriori algorithm is developed for counting via wideband signal backscattering by relying on model order selection. Then, a method that relies on low-level features is proposed to lower the computational complexity. The proposed method is verified via sample-level simulations in realistic operating conditions and compared to current solutions.
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