Positioning techniques using wireless communication signals have attracted considerable attention, especially in dense multipath environments where the Global Positioning System (GPS) is often unreliable[1, 2]. Potential applications include navigation, location-aware services, and asset tracking[3, 4]. A few positioning system using data communication systems, e.g., cellular network[5, 6], wireless local area network (WLAN)[7], digital television (DTV)[8], ultrawide bandwidth (UWB)[2, 9–11], and wireless sensor networks[12, 13] have been investigated in the literature. In these systems, time-of-arrival (TOA)-based approach is commonly preferred where the timing of the signal arriving from the direct propagation path is estimated and used for further location estimation[14, 15]. However, significant challenges arise in dense multipath environments, where the direct signal propagation path is often blocked by various obstacles such as buildings, pedestrians and slowly-moving vehicles, making the strength of the direct path significantly weaker than those of later paths (LPs) from scattering, reflection or refraction.

TOA estimation can be achieved by the classical correlation-based estimator, where the correlation is performed between a signal template^{a} and its received version, and the timing of the first detected peak of the correlation output is considered as the TOA[9, 16]. However, this method has limiting factors for positioning system using communication signals. The training sequence, designed for data communications, has limited duration in order to improve the transmission efficiency and therefore, the correlation peak of the first path is not distinct. Furthermore, due to the multipath propagation effect, the desired signal component of the first path is interfered by the superposition of the signal components of the LPs. In this case, the correlation output can exhibit adjacent peaks with similar or larger magnitude due to the large multipath interference, leading to ambiguity in the selection of the correct peak corresponding to the first path. Therefore, it is very difficult to reliably detect the weak direct path from the wireless signals consisting of strong multiple components and thus, large positioning error is inevitable when the direct path is erroneously identified.

To facilitate the TOA estimation with higher accuracy, super-resolution schemes that jointly estimate the amplitude and the relative delay of each path were proposed in[17–19]. Despite the extremely high computational complexity the methods entail, the results show that these estimators do not always lead to good TOA estimation. For real applications with complexity constraints, several simple techniques have been proposed to detect the first path. Energy detection-based method was proposed in[20–23]. However, the energy detector can perform poorly when the strength of the first path is not the strongest one. Meanwhile, threshold-based approach to detect the first path by comparing the correlation output with a particular threshold[20, 24–29] are gaining interests due to their potential for complete analog implementation, and therefore, particularly attractive to low cost battery-powered devices, e.g., mobile receivers. However, the threshold-based correlation estimator with the solutions to the aforementioned practical challenges in dense multipath environments remains large space undisclosed.

We introduced the idea of multipath interference cancelation in[30]; however, with a simple system model and performance evaluation under static channel. Furthermore, the theoretical performance and the practical constraints, e.g., complexity issues, have not been considered. In this article, we propose a direct path detection scheme using multipath interference cancelation for accurate TOA estimation in dense multipath environments. We consider an Orthogonal Frequency-Division Multiplexing (OFDM)-based communication system, which is widely adopted in various broadband wireless applications[31] and demonstrate the feasibility of the proposed direct path detection method in communication-based positioning systems. For data communication purpose, an iterative estimator for joint channel estimation and data detection with progressively improved accuracy is first proposed to achieve better performance of data communication. Based on the output of the iterative estimator, we propose to reconstruct and mitigate the multipath components from the received signals. Data-aided method is also used to construct an *enhanced preamble*. The new direct path detection is therefore based on the cross-correlation between the multipath interference-suppressed signal and the *enhanced preamble*. Considering the computational burden for the mobile receivers, the characteristics of the iterative estimator is studied by deriving a semi-analytical expression of the variance of the estimation error and the convergence conditions. An automatic stopping criterion is further developed to avoid the unnecessary computational complexity and allow a tradeoff between the performance degradation and computational burden. The performance of the direct path detection is analyzed by deriving the signal-to-noise-interference ratio (SINR)^{b}. Monte Carlo simulations are carried out to evaluate and verify the performance and effectiveness of different modules as well as the overall method.

The rest of the article is organized as follows. The model of the OFDM-based communication system is presented in Section ‘OFDM-based communication system with positioning capability’. The challenges of the classical correlation-based estimator for TOA estimation are also addressed in Section ‘OFDM-based communication system with positioning capability’. In Section ‘Proposed direct path detection scheme’, a new direct path detection scheme using the multipath interference cancelation and data-aided techniques are proposed. Performance analysis is presented in Section ‘Performance analysis of the proposed direct path detection method’ and demonstrated by Monte Carlo simulations in Section ‘Simulation results and discussions’. Finally, we conclude the article in Section ‘Conclusion’.

*Notations*: Throughout the article, if no special note is given, we denote vectors and matrices with letters in bold fonts and scalars with nonbold forms. For any variable *X*, we denote its corresponding estimated version by$\widehat{X}$ and the corresponding estimation error by$\mathrm{\Delta X}\triangleq X-\widehat{X}$. Superscript *T* denotes transpose and superscript *H* denotes the conjugate transpose. tr(·) denotes the trace of a matrix. **I** denotes the identity matrix. ∥·∥ denotes the Frobenius norm of a matrix.