# Localization with One-Bit Passive Radars in Narrowband Internet-of-Things using Multivariate Polynomial Optimization

arXiv: https://arxiv.org/abs/2007.15108 · HTML: https://mlsanity.com/paper/2007.15108/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [UNO: Unlimited Sampling Meets One-Bit Quantization](https://mlsanity.com/paper/2301.10155/) (2022)
- [Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communications](https://mlsanity.com/paper/2211.09253/) (2022)
- [Multi-Antenna Dual-Blind Deconvolution for Joint Radar-Communications via SoMAN Minimization](https://mlsanity.com/paper/2303.13609/) (2023)

## Abstract (arXiv)

Several Internet-of-Things (IoT) applications provide location-based services, wherein it is critical to obtain accurate position estimates by aggregating information from individual sensors. In the recently proposed narrowband IoT (NB-IoT) standard, which trades off bandwidth to gain wide coverage, the location estimation is compounded by the low sampling rate receivers and limited-capacity links. We address both of these NB-IoT drawbacks in the framework of passive sensing devices that receive signals from the target-of-interest. We consider the limiting case where each node receiver employs one-bit analog-to-digital-converters and propose a novel low-complexity nodal delay estimation method using constrained-weighted least squares minimization. To support the low-capacity links to the fusion center (FC), the range estimates obtained at individual sensors are then converted to one-bit data. At the FC, we propose target localization with the aggregated one-bit range vector using both optimal and sub-optimal techniques. The computationally expensive former approach is based on Lasserre's method for multivariate polynomial optimization while the latter employs our less complex iterative joint rtextitange-textittarget location textitestimation (ANTARES) algorithm. Our overall one-bit framework not only complements the low NB-IoT bandwidth but also supports the design goal of inexpensive NB-IoT location sensing. Numerical experiments demonstrate feasibility of the proposed one-bit approach with a 0.6% increase in the normalized localization error for the small set of 20-60 nodes over the full-precision case. When the number of nodes is sufficiently large (>80), the one-bit methods yield the same performance as the full precision.