Non-cooperative Localization Research Articles

Energy-efficient anchor activation protocol for non-cooperative localization of Industrial Internet of Things (IIoT)

The Industrial Internet of Things (IIoT) necessitates low-latency and ultra-reliable communication to support various applications. Besides, the data generated in IIoT must be geo-tagged to take location-aware timely actions. Therefore, this paper proposes an accurate and energy-efficient localization scheme for localizing the devices in IIoT. We introduce a novel anchor activation protocol for the non-cooperative localization of IIoT devices. The proposed method is sequential, distributed, and error adaptive, that first performs distributed scheduling followed by adaptive selection. This distributed localization scheme is well-suited for IIoT due to the non-cooperative nature of the devices; however, at the same time, it is more challenging because no centralized node can disseminate the anchors’ network information. Therefore, the anchors rely only on the local information from other anchors in their vicinity. The distributed approach can significantly benefit such networks because the centralized node can be a system’s bottleneck. Besides the distributed nature, the decision process of anchor selection for localization considers the transmission energy of the anchors to make it energy efficient. Our results show the effectiveness of the proposed method both in terms of localization accuracy and energy efficiency.

Joint Node Selection and Power Allocation for Cooperative Positioning Based on Bidding Auction in VANET

Due to buildings blocking GPS and Wi-Fi signals, traditional techniques can’t offer the user’s required positioning accuracy in resource-constrained underground parking, but the cooperation of agent nodes can provide the exact localization information to improve the positioning accuracy. However, some well-localized agents may not be willing to sacrifice additional power to improve the others’ positioning accuracy. To encourage cooperation among nodes and allocate transmission power reasonably, this paper proposes a bidding-auction-based cooperative localization (BACL) algorithm to improve the positioning accuracy of agent nodes by joint node selection incentive and power allocation strategy. Firstly, the contribution of channel parameters and prior localization information of agent nodes for positioning accuracy are quantified and an incentive mechanism of cooperative localization from an economic perspective is proposed. Secondly, a virtual currency incentive rule is developed to compensate agent nodes of cooperative localization reasonably due to the consumption of energy for transmitting their location information. Finally, the simulation results have shown that the proposed BACL algorithm has excellent performance in terms of localization accuracy in resource-constrained scenarios. Compared with the full-power cooperative localization (FPCL) and non-cooperative localization (NCL) algorithms, the proposed BACL algorithm improved the positioning accuracy by 10% and 65%, respectively. Meanwhile, compared with the FPCL algorithm, the proposed algorithm reduced resource consumption by 50%.

Sparse Direct Position Determination Based on TDOA Information in Correlation-Domain

The sparse direct position determination (DPD) method requires reconstructing the emitter position with prior knowledge. However, in non-cooperative localization scenarios, it is difficult to reconstruct the transmitted signal with the unknown signal form and propagation model. In this paper, a sparse DPD method based on time-difference-of-arrival (TDOA) information in correlation-domain is proposed. Different from the traditional sparse DPD method, the received signal is converted into correlation-domain, and the proposed dictionary matrix is generated by the quantized delay difference, which solves the pseudo-positioning problem. Compared to the conventional multi-signal classification (MUSIC) method, multi-frequency fusion (MFF) method, and two-step positioning algorithm, the proposed algorithm achieves higher positioning accuracy. The feasibility of the algorithm has been verified by both simulation and real-world measured tests.

Non-Cooperative Passive Direct Localization Based on Waveform Estimation

This paper considers a non-cooperative passive localization system wherein widely distributed receivers are used to localize a transmitter radiating a periodical pulse pair signal. Two possible pulse modulation models, noncoherent and coherent pulses, are fully considered for practical application, and are effectively unified as a general model for the algorithm design. To achieve highly accurate and robust localization performance, an enhanced direct position determination (DPD) algorithm based on waveform estimation (WE) is devised to jointly estimate the transmitter position and the waveform profile. The optimal objective function based on a least square (LS) principle is first derived to directly determine the position of the transmitter. Due to the complete lack of knowledge on the transmitted signal, the processing center calculates the objective function at each searched grid of interest by using estimated pulses instead of the real ones, while extraction of pulse samples and estimation of waveform are executed. Theoretical derivation gives the solution to estimate the non-parameterized waveform with a structure of maximum Rayleigh quotient. Additionally, simulation results verify the effectiveness of the proposed algorithm for many common waveform types in the cases of transmitting noncoherent and coherent pulses, and also show the excellent advantage over the classical DPD algorithm at low signal-to-noise ratio (SNR).

Incentive Mechanism for Cooperative Localization in Wireless Networks

Cooperative localization has emerged as an appealing technology since it can improve the localization performance without any infrastructure change compared with non-cooperative localization. However, some well-localized agents may not be willing to sacrifice additional power to improve the others' localization accuracy. This paper proposes an incentive mechanism from an economic perspective for cooperative localization, whereby a pricing scheme is designed to lead each agent to the optimal state. A game-theoretic algorithm is proposed where each player (agent) can obtain the optimal budget strategy to minimize its individual utility. To make profits in the game, the relationship between the agent's network condition and its budget strategy is derived. Furthermore, a fairness-aware price allocation rule (PAR) is developed to distribute the budget among the reference agents proportional to each node's contribution. Analytical and numerical results show that agents with better network conditions are more likely to join the cooperation under the proposed incentive mechanism, leading to an improved localization performance.

Interference Sources Localization and Communication Relationship Inference With Cognitive Radio IoT Networks

With the widespread application of Internet of things (IoT), the interference problem becomes more and more serious, which results in not only the poor network performance but also the increased energy consumption of IoT nodes. Therefore, in this paper, we investigate the problem of how to locate the interference sources and infer their communication relationships in the cognitive radio IoT (CR-IoT) deployment scenario. First, we utilize MDS-MAP(P) algorithm with dynamic power control to realize cooperative self-localization of the CR-IoT nodes, which is more energy-efficient than all the IoT nodes equipped with global positioning system (GPS) receivers. Then, we propose a non-cooperative localization method to determine the inference sources with the angle of arrivals (AoAs) measured by the CR-IoT nodes. Finally, the communication relationship between interference sources can be inferred based on the association rule of signals. The network simulation results validate that the proposed methods can locate the interference sources accurately with low energy consumption and correctly infer their communication relationships, which is helpful for the interrupted CR-IoT nodes to take a specific opportunistic transmission policy to reduce their energy consumption.

Noncooperative Localization and Tracking Through the Factorization Method

The localization and tracking of human targets are cast as linear inverse obstacle problems and solved by means of the factorization method. The proposed approach is validated against indoor monitoring and multifrequency sensing, where Green’s function pertaining to the involved realistic scenarios has been determined through full-wave simulations. The results, which include the analysis of the impact on final performance of the number of employed transceivers as well as preliminary 2-D processing of realistic data simulated in 3-D geometry, show good robustness to noise and model errors.

A Robust, Non-Cooperative Localization Algorithm in the Presence of Outlier Measurements in Ocean Sensor Networks.

As an important means of multidimensional observation on the sea, ocean sensor networks (OSNs) could meet the needs of comprehensive information observations in large-scale and multifactor marine environments. In what concerns OSNs, accurate location information is the basis of the data sets. However, because of the multipath effect—signal shadowing by waves and unintentional or malicious attacks—outlier measurements occur frequently and inevitably, which directly degrades the localization accuracy. Therefore, increasing localization accuracy in the presence of outlier measurements is a critical issue that needs to be urgently tackled in OSNs. In this case, this paper proposed a robust, non-cooperative localization algorithm (RNLA) using received signal strength indication (RSSI) in the presence of outlier measurements in OSNs. We firstly formulated the localization problem using a log-normal shadowing model integrated with a first order Taylor series. Nevertheless, the problem was infeasible to solve, especially in the presence of outlier measurements. Hence, we then converted the localization problem into the optimization problem using squared range and weighted least square (WLS), albeit in a nonconvex form. For the sake of an accurate solution, the problem was then transformed into a generalized trust region subproblem (GTRS) combined with robust functions. Although GTRS was still a nonconvex framework, the solution could be acquired by a bisection approach. To ensure global convergence, a block prox-linear (BPL) method was incorporated with the bisection approach. In addition, we conducted the Cramer–Rao low bound (CRLB) to evaluate RNLA. Simulations were carried out over variable parameters. Numerical results showed that RNLA outperformed the other algorithms under outlier measurements, notwithstanding that the time for RNLA computation was a little bit more than others in some conditions.

GICS, a new concept to enhance in-flight safety and security of commercial aviation

This paper presents a new concept of aircraft tracking based on a LEO constellation of small satellites, each of them carrying receiving and transmitting devices in order to check the integrity of the ADS-B (Automatic Dependent Surveillance - Broadcast) positioning information and proceed with a non-cooperative independent localization of the aircraft, if a flight anomaly comes out. To do so, on side of the conventional ADS-B processing, authors propose to add a multilateration process and two active radar modes, able to perform a finer and reliable localization of the aircraft, as well as a first information on the nature of the flight anomaly. The paper deals with a preliminary system analysis, although underpinned by some illustrations of existing sensor technologies, already or nearly operational in space. The concept has been patented [1].

Cooperative Detection-Assisted Localization in Wireless Networks in the Presence of Ranging Outliers

Location-aware wireless networks can provide precise location information in harsh environments, however, which is only possible when all nodes are well-functioning. In this paper, we propose algorithms and analyze the performance limits for both non-cooperative and cooperative localization networks in the presence of ranging outliers. Especially, we show that the localization performance can be boosted by using the cooperative outlier detection scheme. An algorithm based on expectation-maximization is proposed for non-cooperative localization networks, while a variational message passing-based algorithm is proposed for the cooperative counterparts. Performance limits are investigated using Cramer–Rao lower bound. Further inspection on the performance limits confirms the performance gain from the cooperative detection scheme. Stochastic geometric analysis is also carried out to account for the stochastic nature of wireless networks, as well as to provide simpler expressions and additional insights. Simulation results corroborate the analytical results, and show that both of the proposed algorithms are capable of attaining the corresponding performance limits at a significantly reduced computational cost compared with existing algorithms.

3-D Target Localization in Wireless Sensor Networks Using RSS and AoA Measurements

This paper addresses target localization problems in both noncooperative and cooperative 3-D wireless sensor networks (WSNs), for both cases of known and unknown sensor transmit power, i.e., $P_{T}$ . We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength and angle-of-arrival information, respectively. Based on range and angle measurement models, we derive a novel nonconvex estimator based on the least squares criterion. The derived nonconvex estimator tightly approximates the maximum-likelihood estimator for small noise. We then show that the developed estimator can be transformed into a generalized trust region subproblem framework, by following the squared range approach, for noncooperative WSNs. For cooperative WSNs, we show that the estimator can be transformed into a convex problem by applying appropriate semidefinite programming relaxation techniques. Moreover, we show that the generalization of the proposed estimators for known $P_{T}$ is straightforward to the case where $P_{T}$ is not known. Our simulation results show that the new estimators have excellent performance and are robust to not knowing $P_{T}$ . The new estimators for noncooperative localization significantly outperform the existing estimators, and our estimators for cooperative localization show exceptional performance in all considered settings.

Joint Allocation of Spectral and Power Resources for Non-Cooperative Wireless Localization Networks

Network localization is a key feature in many wireless services and applications. In typical range-based non-cooperative localization techniques, agents try to perform position estimation through ranging with respect to anchors with known positions. Based on the definition of squared positional error bound, the localization accuracy can be determined by the transmit power, carrier frequency, and signal bandwidth. This paper analyzes the joint power and spectrum allocation (JPSA) optimization problems in resource restricted wireless localization systems. We first formulate both optimal interference-free and interference affected JPSA problems. We then formulate the robust counterparts of the problems in the presence of uncertainty of the agents’ positions. Since all JPSA problems are non-convex, we show that they can be modeled and solved as geometric programming (GP) by proper approximations. Numeric results validate our analysis and show that the developed algorithms are able to find solutions close to the global optimum in the investigated cases. We can find the optimal/robust resource deployment in non-cooperative wireless localization networks based on the proposed frameworks.

Analysis of Target Localization With Ideal Binary Detectors via Likelihood Function Smoothing

This letter deals with noncooperative localization of a single target using censored binary observations acquired by spatially distributed sensors. An ideal, noise-free setting is considered whereby each sensor can perfectly detect if the target is in its close proximity or not. Only those detecting sensors communicate their decisions and locations to a fusion center (FC), which subsequently forms the desired location estimator based on censored observations. Because a maximum-likelihood estimator (MLE) does not exist in this setting, current approaches have relied on heuristic, centrality-based geometric estimators such as the center of a minimum enclosing circle (CMEC). A smooth surrogate to the likelihood function is proposed here, whose maximizer is shown to approach the CMEC asymptotically as the likelihood approximation error vanishes. This provides rigorous analytical justification as to why the CMEC estimator outperforms other heuristics for this problem, as empirically observed in prior studies. Since the Cramer-Rao Bound does not exist either, an upshot of the results in this letter is that the CMEC performance can be adopted as a benchmark in this ideal setting and also for comparison with other more pragmatic binary localization methods in the presence of uncertainty.

Accurate Refinement for Cooperative Localization Using Received Signal Strength Measurements

A refinement approach is proposed to improve the localization accuracy for non-cooperative or cooperative localization. The proposed localization design is divided into two steps: localization primitive and refinement. The localization primitive introduces an extra variable to obtain the preliminary estimates. Then the non-cooperative and cooperative refinement is conducted to improve the positioning accuracy by utilizing the source-anchor and source-source RSS measurements. The simulations demonstrate the validity of the localization refinement approaches and test the impacts of signal strength noises on positioning error. The results also show that the performance of designed refinement algorithms can attain the approximate position Cramer-rao Low Bound (CRLB) at sufficiently small noises.

Propagation Modeling for Radio Frequency Tomography in Wireless Networks

Non-cooperative localization is an important class of applications with the goal of detecting and tracking objects without their explicit participation. Recognizing that these objects will attenuate any radio signals that pass through them, recent works have proposed using the received signal strength (RSS) measurements from networks of portable wireless devices to detect, localize, and track objects through radio frequency (RF) tomography. Because the effects of these objects on radio signals can be relatively small, accurate radio propagation models are essential to the accuracy and reliability of these methods. Despite their critical importance, only limited work has been done to develop and test such models. In this paper, we describe the RF tomography problem in detail, comparing both existing and novel shadowing models analytically. We design an RF tomography testbed and describe the techniques used to conduct field test measurements. We present the experimental results from our RF tomography testbed comparing several different shadowing models. Further, we present the results of applying the different shadowing models to RSS measurements obtained in a field test and evaluate how well each model approximates the radio propagation experienced during our test.

Introduction to the Special Issue on Non-Cooperative Localization Networks

Introduction to the Special Issue on Non-Cooperative Localization Networks