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Register a new userA neurodynamic optimization approach to robust TDOA-based IoT localization using unreliable sensor data
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Added by
Wenxin Xiong
Publication date
2020
fields
Electronic Engineering
and research's language is
English
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This paper considers the problem of time-difference-of-arrival (TDOA) source localization using possibly unreliable data collected by the Internet of Things (IoT) sensors in the error-prone environments. The Welsch loss function is integrated into a hardware realizable projection-type neural network (PNN) model, in order to enhance the robustness of location estimator to the erroneous measurements. For statistical efficiency, the formulation here is derived upon the underlying time-of-arrival composition via joint estimation of the source position and onset time, instead of the TDOA counterpart generated in the postprocessing of sensor-collected timestamps. The local stability conditions and implementation complexity of the proposed PNN model are also analyzed in detail. Simulation investigations demonstrate that our neurodynamic TDOA localization solution is capable of outperforming several existing schemes in terms of localization accuracy and computational efficiency.
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This paper revisits the problem of locating a signal-emitting source from time-difference-of-arrival (TDOA) measurements under non-line-of-sight (NLOS) propagation. Many currently fashionable methods for NLOS mitigation in TDOA-based localization tend to solve their optimization problems by means of convex relaxation and, thus, are computationally inefficient. Besides, previous studies show that manipulating directly on the TDOA metric usually gives rise to intricate estimators. Aiming at bypassing these challenges, we turn to retrieve the underlying time-of-arrival framework by treating the unknown source onset time as an optimization variable and imposing certain inequality constraints on it, mitigate the NLOS errors through the $ell_1$-norm robustification, and finally apply a hardware realizable neurodynamic model based on the redefined augmented Lagrangian and projection theorem to solve the resultant nonconvex optimization problem with inequality constraints. It is validated through extensive simulations that the proposed scheme can strike a nice balance between localization accuracy, computational complexity, and prior knowledge requirement.
With the unprecedented demand for location-based services in indoor scenarios, wireless indoor localization has become essential for mobile users. While GPS is not available at indoor spaces, WiFi RSS fingerprinting has become popular with its ubiquitous accessibility. However, it is challenging to achieve robust and efficient indoor localization with two major challenges. First, the localization accuracy can be degraded by the random signal fluctuations, which would influence conventional localization algorithms that simply learn handcrafted features from raw fingerprint data. Second, mobile users are sensitive to the localization delay, but conventional indoor localization algorithms are computation-intensive and time-consuming. In this paper, we propose EdgeLoc, an edge-IoT framework for efficient and robust indoor localization using capsule networks. We develop a deep learning model with the CapsNet to efficiently extract hierarchical information from WiFi fingerprint data, thereby significantly improving the localization accuracy. Moreover, we implement an edge-computing prototype system to achieve a nearly real-time localization process, by enabling mobile users with the deep-learning model that has been well-trained by the edge server. We conduct a real-world field experimental study with over 33,600 data points and an extensive synthetic experiment with the open dataset, and the experimental results validate the effectiveness of EdgeLoc. The best trade-off of the EdgeLoc system achieves 98.5% localization accuracy within an average positioning time of only 2.31 ms in the field experiment.
Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), and Received Signal Strength (RSS) based. Since the Cram{e}r-Rao lower bounds (CRLB) of these methods depend on the sensor geometry explicitly, sensor placement becomes a crucial issue in source localization applications. In this paper, we consider finding the optimal sensor placements for the TOA, TDOA and RSS based localization scenarios. We first unify the three localization models by a generalized problem formulation based on the CRLB-related metric. Then a unified optimization framework for optimal sensor placement (UTMOST) is developed through the combination of the alternating direction method of multipliers (ADMM) and majorization-minimization (MM) techniques. Unlike the majority of the state-of-the-art works, the proposed UTMOST neither approximates the design criterion nor considers only uncorrelated noise in the measurements. It can readily adapt to to different design criteria (i.e. A, D and E-optimality) with slight modifications within the framework and yield the optimal sensor placements correspondingly. Extensive numerical experiments are performed to exhibit the efficacy and flexibility of the proposed framework.
We consider a network of agents that locate themselves in an environment through sensor measurements and aim to transmit a message signal to a base station via collaborative beamforming. The agents sensor measurements result in localization errors, which degrade the quality of service at the base station due to unknown phase offsets that arise in the agents communication channels. Assuming that each agents localization error follows a Gaussian distribution, we study the problem of forming a reliable communication link between the agents and the base station despite the localization errors. In particular, we formulate a discrete optimization problem to choose only a subset of agents to transmit the message signal so that the variance of the signal-to-noise ratio (SNR) received by the base station is minimized while the expected SNR exceeds a desired threshold. When the variances of the localization errors are below a certain threshold characterized in terms of the carrier frequency, we show that greedy algorithms can be used to globally minimize the variance of the received SNR. On the other hand, when some agents have localization errors with large variances, we show that the variance of the received SNR can be locally minimized by exploiting the supermodularity of the mean and variance of the received SNR. In numerical simulations, we demonstrate that the proposed algorithms have the potential to synthesize beamformers orders of magnitude faster than convex optimization-based approaches while achieving comparable performances using less number of agents.
Stochastic model predictive control (SMPC) has been a promising solution to complex control problems under uncertain disturbances. However, traditional SMPC approaches either require exact knowledge of probabilistic distributions, or rely on massive scenarios that are generated to represent uncertainties. In this paper, a novel scenario-based SMPC approach is proposed by actively learning a data-driven uncertainty set from available data with machine learning techniques. A systematical procedure is then proposed to further calibrate the uncertainty set, which gives appropriate probabilistic guarantee. The resulting data-driven uncertainty set is more compact than traditional norm-based sets, and can help reducing conservatism of control actions. Meanwhile, the proposed method requires less data samples than traditional scenario-based SMPC approaches, thereby enhancing the practicability of SMPC. Finally the optimal control problem is cast as a single-stage robust optimization problem, which can be solved efficiently by deriving the robust counterpart problem. The feasibility and stability issue is also discussed in detail. The efficacy of the proposed approach is demonstrated through a two-mass-spring system and a building energy control problem under uncertain disturbances.
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