One of the main challenges of radar-based localization applications in indoor environments is the presence of strong multipath. When the radar bandwidth is large enough, multipath components can be resolved in range but they result in unwanted ghost targets. We propose a novel multipath mitigation approach that exploits the fact that multipaths are highly dependent on the scene geometry. The multipath mitigation approach discards the ghost targets based on the fused information of multiple radars located at different positions in the scene. For such radar fusion, the output of the radar signal processing chain is translated into the world coordinate system that is common for all the radars. We propose a radar alignment approach to estimate the translation and rotation parameters from radar to world coordinate system and vice versa. Our multipath mitigation method is combined with an unscented Kalman filter to improve the localization accuracy. We demonstrate the effectiveness of our complete approach with a real experiment using two radars to detect and track a target in a room with severe multipath.
We investigate indoor human multi-target tracking in cartesian coordinates based on range, Doppler and Angle-of-Arrival measurements obtained with a four-antenna passive bistatic radar capturing 802.11ax Wi-Fi signals. A reference antenna selection method is described to perform angle processing correctly when dealing with target detection diversity among antennas. The tracking is performed by an Unscented Kalman Filter (UKF) to handle the non-linear relation between the measurement space and the state space. A Joint Probabilistic Data Association Filter is coupled to the UKF to handle the data association between tracks and measurements when dealing with multiple targets. Simulations are performed to determine the tracking parameters under heavy constraints and identify key scenarios. An experimental setup is built using Universal Software Radio Peripherals, featuring an over-the-air phase calibration for angle processing with an anchor antenna. It is used to validate the proposed single and multi-target tracking scheme.
Iterative localization is currently seen as an attractive solution to localize a transmitter in a cellular network. It has been shown that, by iterating between a range estimation step and a multi-lateration step, it is possible to refine the estimation in the first step, where only local information is used at iteration one. The iterative approach gets close to the performance of direct localization; nevertheless, it does not seem to converge to the direct localization performance for medium and low signal-to-noise-ratio values, due to the fact that it still suffers from loss of information due to projections and data representation. In this work, we propose to approximate the range log-likelihood at the base station with a Dirichlet kernel and to perform all the processing in a common xy-domain so that projections are no longer needed. We numerically show that our approach brings significant performance gains as compared to the time-of-arrival based iterative position estimation algorithm, getting really close to the performance of direct localization.
Geo-localization services are an important functionality in cellular networks. Besides, the use of Ultra Dense Networks and small cells, in current and future cellular networks, greatly increases the complexity of centralized localization approaches. Consequently, we propose a Self-Synchronization Positioning Estimation (SSPE) algorithm that estimates the transmitter position in a distributed fashion. The proposed SSPE algorithm reaches consensus for the posterior distribution of the transmitter position rather than on the final estimates. Such consensus ensures that the proposed SSPE algorithm converges to the centralized Direct Positioning Estimation (DPE) approach, which has the best performance of all localization approaches. We show that the proposed algorithm is related to the Iterative Positioning Estimation (IPE) algorithm, since both exploit the self-synchronization mechanism. As a result, the improvements and extensions for IPE, previously studied in other works, can be directly applied to the proposed SSPE algorithm. In addition, the proposed algorithm is able to localize the transmitter even when it is not time synchronized with the network as it is usually the case. The performance of the algorithms is numerically assessed through Monte-Carlo simulations by the mean distance error and mean range offset error. Finally, we not only show that our approach gets close to the DPE performance after a few iterations, but also that it converges for different logical network configurations.
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