MmWave 6D Radio Localization with a Snapshot Observation from a Single BS

Abstract

Accurate and ubiquitous localization is crucial for a variety of applications such as logistics, navigation, intelligent transport, monitoring, and control. Exploiting mmWave signals in 5G and Beyond 5G systems can provide accurate localization with limited infrastructure. We consider the single base station localization problem and extend it to 3D position and 3D orientation estimation of an unsynchronized multi-antenna user, using downlink MIMO-OFDM signals. Through a Fisher information analysis, we show that the problem is often identifiable, provided that there is at least one additional multipath component, even if the position of corresponding incidence point is a priori unknown. Subsequently, we pose a maximum likelihood (ML) estimation problem, to jointly estimate the 3D position and 3D orientation of the user as well as several nuisance parameters (the user clock offset and the positions of incidence points corresponding to the multipath). The ML problem is a highdimensional non-convex optimization problem over a product of Euclidean and Riemannian manifolds. To avoid complex exhaustive search procedures, we propose a geometric initial estimate of all parameters, which reduces the problem to a 1dimensional search over a finite interval. Numerical results show the efficiency of the proposed ad-hoc estimation, whose gap to the Cramér-Rao bound (CRB) is tightened using ML estimation.

Doppler-Enabled Single-Antenna Localization and Mapping Without Synchronization

“…With a high dimension of unknowns (size of state vector s), it is not practical to perform maximum likelihood estimation (MLE) and estimate all the unknowns at the same time (e.g., using gradient descent). Here, similar to the ad-hoc estimator in [19], we propose an algorithm that simplifies localization and mapping tasks into a 1D search problem.…”

Antenna Selection in Switch-Based MIMO Arrays via DOA Threshold Region Approximation

“…In order to solve the problem formulated in (5), two practical objective functions, the CRLB, and the PSL will be introduced in Section III-A before proceeding to the proposed TRA-based algorithms.…”

“…Note that δ = 1 indicates no PSL constraint and the optimization relies soley on the CRLB (this yields d δ=1 = [0, 0.5, 1, 9, 9.5, 10] for M = 6). In addition, a ULA setup (e.g., a linear array with d ula = [0, 1,2,3,4,5] for M = 6) and a best-scenario CRLB (e.g., the CRLB of d δ=1 for M = 6) are also used as benchmarks. We assume a coarse DOA estimate is available (e.g., from current connected subarray, or from IMUs and GPS), and the maximum DOA estimation error ∆u is chosen as 0.1 for generating û.…”

“…Compared with the time-of-arrival/timedifference-of-arrival (TOA/TDOA) where precise clock synchronization is needed, DOA is preferred in localization [4]. Furthermore, orientation estimation becomes possible with DOA information at the user side in high-frequency communication systems (e.g., mm-Waves and THz systems) [5]. Practical DOA estimation algorithms, such as MUSIC [6] and ESPRIT [7], are widely used to solve this problem.…”

Radio Localization and Sensing—Part II: State-of-the-Art and Challenges

“…By considering each propagation path in (1) as an object, generating a specific time-of-arrival (ToA), AoA, angle-of-departure (AoD), and Doppler, we can infer the 3D location of IPs x l , e.g., via the relation τ l = ( x tx − x inc,l + x rx − x inc,l )/c + B. Since each multipath component has three spatial degrees of freedom (i.e., x inc,l ∈ R 3 ) and a 5-dimensional measurement (ToA, AoA and AoD in azimuth and elevation, if we ignore Doppler), multipath directly contributes to improving localization performance [32]. In Table I, we provide an overview of minimal configurations needed to solve the 3D localization problem (and in the MIMO cases, the 3D orientation problem).…”