Two-step Localization Research Articles

Coherent summation of multiple short-time signals for direct positioning of a wideband source based on delay and Doppler

We consider identifying the source position directly from the received source signals. This direct position determination (DPD) approach has been shown to be superior in terms of better estimation accuracy and improved robustness to low signal-to-noise ratios (SNRs) to the conventional two-step localization technique, where signal measurements are extracted first and the source position is then estimated from them. The localization of a wideband source such as a communication transmitter or a radar whose signal should be considered deterministic is investigated in this paper. Both passive and active localization scenarios, which correspond to the source signal waveform being unknown and being known respectively, are studied. In both cases, the source signal received at each receiver is partitioned into multiple non-overlapping short-time signal segments for the DPD task. This paper proposes the use of coherent summation that takes into account the coherency among the short-time signals received at the same receiver. The study begins with deriving the Cramér–Rao lower bounds (CRLBs) of the source position under coherent summation-based and non-coherent summation-based DPDs. Interestingly, we show analytically that with coherent summation, the localization accuracy of the DPD improves as the time interval between two short-time signals increases. This paper also develops approximate maximum likelihood (ML) estimators for DPDs with coherent and non-coherent summations. The CRLB results and the performance of the proposed source position estimators are illustrated via simulations.

Source localization using a moving receiver and noisy TOA measurements

This paper considers using a moving receiver to locate a stationary source that transmits periodically with an unknown period. The receiver records the time of arrivals (TOAs) of the intercepted signals for source localization. A new two-step localization algorithm is developed. Its first step ignores the signal propagation delay and identifies the signal period using existing period estimation techniques. Sufficient conditions for correctly relating the TOA measurements are established. The second step of the new algorithm jointly estimates the source position and the signal period using an iterative maximum likelihood estimator. Simulation results demonstrate the effectiveness of the newly proposed technique.

An improved localization algorithm with wireless heartbeat monitoring system for patient safety in psychiatric wards

Psychiatric patients often require continuous monitoring to keep them out of dangerous situations. Accordingly, hospitals hire additional staff to monitor patients' vital signs, maintain patient safety, and ensure that patients do not leave the hospital without notice. However, ward staff have difficulty knowing whenever a psychiatric patient is stepping into potential danger zones or encountering any safety threat. This paper reports the development of a wireless monitoring system to improve patient safety in psychiatric wards and reduce avoidable risks. The proposed system can ease the workload of nurses, help locate patients, and monitor patients' heartbeats. A two-step clustering localization algorithm is proposed for use in tracking patients' locations. This study marks for the first time that heartbeat detection using a ZigBee-based platform with localization function has been proposed. A proof-of-concept system is developed to understand the current hardware challenges and to enable functional analysis of the proposed ZigBee-based patient localization system. The error distance of the proposed localization algorithm is approximately 1m. Its location accuracy is 90% with the error distance of up to 3m. The proposed system is expected to improve patient safety significantly in psychiatric wards at low cost.

A Two-Step Secure Localization for Wireless Sensor Networks

Accuratelylocatingunknownnodesisacriticalissueinthestudyofwirelesssensornetworks(WSNs). Many localization approaches have been proposed based on anchor nodes, which are assumed to know their locations by manual placement or additional equipments such as global positioning system. However, none of these approaches can work properly under the adversarial scenario. In this paper, we propose a novel scheme called two-step secure localization (TSSL) stand against many typical malicious attacks, e.g. wormhole attack and location spoofing attack. TSSL detects malicious nodes step by step. First, anchor nodes collaborate with each other to identify suspicious nodes by checkingtheircoordinates,identitiesandtimeofsendinginformation.Then,byusingamodifiedmesh generationscheme,maliciousnodesareisolatedandtheWSNisdividedintoareaswithdifferenttrust grades. Finally, a novel localization algorithm based on the arrival time difference of localization information is adopted to calculate locations of unknown nodes. Simulation results show that the TSSL detects malicious nodes effectively and the localization algorithm accomplishes localization with high localization accuracy.

ZigBee Node Locating Technology Based on Genetic Algorithm

Location technology as Wireless Sensor Network’s support technology for most applications has been widely researched, ZigBee is one of representative technology of WSN. On the basis of the common two-step localization algorithm, this paper proposes a locating method that optimize the measurement data using genetic algorithm before locating calculation, then using multilateral measurement least-square method for calculating position. Experimental results show that the modified algorithm is significantly reduced the position error, effectively improve the position accuracy.

Localization Algorithms for Wireless Sensor Retrieval

In wireless sensor networks (WSNs), localization has many important applications, among which wireless sensor retrieval bears special importance for cost saving, data analysis and security purposes. Localization for sensor retrieval is especially challenging due to the fact that the number and locations of these sensors are both unknown. In this paper, we propose two probabilistic localization algorithms that iteratively identify the locations of multiple wireless sensors in WSNs, one of which calculates location information offline, and the other online. In both algorithms, we implement a two-step localization process — the first step is called Grid-LEGMM (grid location estimation based on the Gaussian mixture model), a coarse-grain location search using grids by choosing the proper number and locations of the wireless sensors that maximize a likelihood estimation, and the second step is called EM-LEGMM (expectation maximization based on the Gaussian mixture model), which uses the EM-method to refine the results of Grid-LEGMM. An additional step in the online localization algorithm is a credit-based filtering mechanism that removes spurious sensor locations. The performance of both offline and online localization algorithms are analyzed using the Cramer–Rao lower bound (CRLB), and evaluated using simulations and real testbed experiments.