Indoor localisation has become increasingly important for many services. When the environment is not crowded, i.e. most links between the target and anchors do not suffer from non‐line‐of sight (NLOS) offsets, the centralised time‐difference‐of‐arrival localisation can efficiently estimate the target position. However, such a method does not work in crowded environments unless a large number of anchors are installed, which is unrealistic for many applications. In this case, the objects that block the LOS links can serve as dynamic anchors (e.g. with round trip time‐of‐arrival localisation) to improve positioning accuracy. However, such a method tends to include too many unreliable dynamic anchors in estimation, which will significantly increase the computational complexity (e.g. to detect and discard such unreliable anchors in the NLOS mitigation stage). This study proposes a coarse NLOS detection algorithm based on discrete power levels to efficiently achieve the coarse NLOS mitigation, which automatically discards most unreliable dynamic anchors. The simulation results show that the proposed method can achieve very similar estimation performance as the optimal estimation method in the crowded environment, while analysis shows that it can significantly decrease the computational complexity.

Vehicular ad hoc networks represent a promising solution to vehicle navigation in parking lots. Many proposed architectures so far rely upon the roadside units (RSUs) around the parking lot for localization purposes. In this paper, we present a parked-vehicle assistance (PVA)-based position estimation scheme. The many PVA links we exploit significantly increase the number of communication links and range measurements used for localization. This way, we alleviate the constraint imposed by the usually limited number of RSUs. While the proposed method serves as a promising candidate to achieve accurate vehicle localization particularly in parking lots, the range measurements from the parked vehicles may suffer from severe non-line-of-sight (NLOS) conditions native to the parking lot topologies. In response, we propose a two-step algorithm to efficiently detect the NLOS links. This way, we can exploit the benefits offered by PVA, while keeping a reasonable computational load by efficiently differentiating the range measurements. Indeed, our simulation results demonstrate a significant increase in terms of the position accuracy compared with the RSU-based scheme.

Achieving accurate and effective modeling of the vehicle-to-vehicle (V2V) communication channel has proven to be a challenging task, particularly owing to the highly dynamic nature of vehicular environments. V2V channels generally may have contributions from the line-of-sight path, reflections from large stationary and moving objects such as bridges and other vehicles, and a diffuse base from large numbers of small stationary objects in the environment. We propose a new geometrical model for the diffuse component based on scattering objects distributed along the roadside, and use this model to predict the Doppler spectrum and angle-of-arrival distribution associated with this component for various V2V scenarios. In contrast with previous roadside scattering models that sum the contributions from large numbers of randomly-generated scattering objects, our model assumes a uniform linear distribution along the roadside. This permits a computationally efficient, closed-form model. Comparisons with on-road measurement data as well as the double ring model demonstrate the validity and effectiveness of the proposed model.

An experimental study of the Doppler coherence time properties of Vehicle-to-Vehicle (V2V) wireless channels at 5.9 GHz in both rural and highway environments is presented. Simultaneous RF and mobility measurements were conducted in environments near Pittsburgh, Pennsylvania. The average Doppler spread was observed to depend linearly on effective speed, defined as the square root of the sum of the squares of the ground speeds of the two vehicles. Sample spectrum is analyzed and comparisons with the double-ring models are presented. The coherence time was observed to vary inversely with effective speed, as expected. In addition, the coherence time was observed to decrease with vehicle separation out to about 100 m, followed by a relative peak at about 200 m. A possible interpretation of this peak in terms of a two-ray propagation model is presented. The observed Doppler spread should not be a problem for proposed V2V OFDM signal transmission formats, but the channel coherence time is much shorter than a typical packet suggesting that present equalization schemes may not be adequate.

Broadband sounding of the vehicle-to-vehicle channel is reported in suburban, rural, and highway environments. A direct sequence spread spectrum waveform based on zero correlation zone sequences was used with an instantaneous bandwidth of about 40 MHz. Cumulative distribution functions are presented for the maximum excess delay, RMS delay spread, and coherence bandwidth using a threshold of 15 dB below the peak. The highway environment showed the largest median RMS delay spread (about 110 ns), the largest median maximum excess delay (about 600 ns) and the smallest median 90% coherence bandwidth (about 900 kHz). In general, the distributions of these quantities for the suburban environment were narrower than those for the rural and highway environments. This is interpreted in terms of the more restricted range of distances to scattering objects in the suburban environment.

This study presents narrow-band measurements of the mobile vehicle-to-vehicle propagation channel at 5.9 GHz, under realistic suburban driving conditions in Pittsburgh, Pennsylvania. Our system includes differential Global Positioning System (DGPS) receivers, thereby enabling dynamic measurements of how large-scale path loss, Doppler spectrum, and coherence time depend on vehicle location and separation. A Nakagami distribution is used for describing the fading statistics. The speed-separation diagram is introduced as a new tool for analyzing and understanding the vehicle-to-vehicle propagation environment. We show that this diagram can be used to model and predict channel Doppler spread and coherence time using vehicle speed and separation.