scholarly journals An improved geometric algorithm for indoor localization

2018 ◽  
Vol 14 (3) ◽  
pp. 155014771876737 ◽  
Author(s):  
Junhua Yang ◽  
Yong Li ◽  
Wei Cheng
Keyword(s):  
Maximum Likelihood ◽  
Iteration Method ◽  
Indoor Environment ◽  
Maximum Likelihood Method ◽  
Indoor Localization ◽  
Signal Strength ◽  
Receive Signal ◽  
Receive Signal Strength ◽  
Likelihood Method ◽  
Receive Signal Strength Indicator

Indoor localization system using receive signal strength indicator from wireless access point has attracted lots of attention recently. Geometric method is one of the most widely used spatial graph algorithms to locate object in an indoor environment, but it does not achieve good results when it is applied to a limited amount of valid data, especially when using the trilateration method. On the other hand, localization based on fingerprint can achieve high accuracy but need to pay heavy manual labor for fingerprint database establishment. In this article, we propose a bilateral greed iteration localization method based on greedy algorithm in order to use all of the effective anchor points. Comparing to trilateration, fingerprint, and maximum-likelihood method, the bilateral greed iteration method improves the localization accuracy and reduces complexity of localization process. The method proposed, coupled with measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms trilateration and maximum-likelihood receive signal strength indicator–based indoor location methods without using any radio map information nor a complicated algorithm. Extensive experiment results in a Wi-Fi coverage office environment indicate that the proposed bilateral greed iteration method reduces the localization error, 63.55%, 9.93%, and 47.85%, compared to trilateration, fingerprint, and maximum-likelihood method, respectively.

Application of the maximum‐likelihood method (MLM) for sonic velocity logging

Geophysics ◽  
1986 ◽  
Vol 51 (3) ◽  
pp. 780-787 ◽  
Author(s):  
Kai Hsu ◽  
Arthur B. Baggeroer
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Laboratory Data ◽  
Signal Strength ◽  
Likelihood Method ◽  
Data Sets ◽  
Normalization Procedure ◽  
Computational Requirement ◽  
Full Waveforms ◽  
Schlumberger Array

Modern digital sonic tools can record full waveforms using an array of receivers. The recorded waveforms are extremely complicated because wave components overlap in time. Conventional beamforming approaches, such as semblance processing, while robust, sometimes do not resolve the interfering wave components propagating at similar speeds, such as multiple compressional arrivals due to the formation alteration surrounding the borehole. Here the maximum‐likelihood method (MLM), a high‐resolution array processing algorithm, is modified and applied to process borehole array sonic data. Extensive modifications of the original MLM algorithm were necessary because of the transient character of the sonic data and its effect upon the spectral covariance matrix. We applied MLM to several array sonic data sets, including laboratory data, synthetic waveforms, and field data taken by a Schlumberger array sonic tool. MLM’s slowness resolution is demonstrated in resolving a secondary compressional arrival from the primary compressional arrival in an altered formation, and the formation compressional arrival in the presence of a stronger casing arrival in an unbonded cased hole. In comparison with the semblance processing results, the MLM results clearly show a better slowness resolution. However, in the case of a weak formation arrival, the semblance processing tends to enhance and resolve the weak arrival by the semblance normalization procedure, while the MLM, designed to estimate the signal strength, does not. The heavy computational requirement (mainly, many matrix inversions) in the MLM makes it much slower than semblance processing, which may prohibit implementation of the MLM algorithm in a real‐time environment.

scholarly journals MAXIMUM LIKELIHOOD ESTIMATION OF THE NUMBER OF NUCLEOTIDE SUBSTITUTIONS FROM RESTRICTION SITES DATA

Genetics ◽  
1983 ◽  
Vol 105 (1) ◽  
pp. 207-217
Author(s):  
Masatoshi Nei ◽  
Fumio Tajima
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Iteration Method ◽  
Maximum Likelihood Method ◽  
Likelihood Estimation ◽  
Restriction Enzymes ◽  
Likelihood Method ◽  
Nucleotide Substitutions ◽  
Simple Method ◽  
Restriction Sites

ABSTRACT A simple method of the maximum likelihood estimation of the number of nucleotide substitutions is presented for the case where restriction sites data from many different restriction enzymes are available. An iteration method, based on nucleotide counting, is also developed. This method is simpler than the maximum likelihood method but gives the same estimate. A formula for computing the variance of a maximum likelihood estimate is also presented.

Design of Automation Calibration for RF Unit of Wireless Transceiver System

2013 ◽  
Vol 441 ◽  
pp. 955-958
Author(s):  
Bin Wen Huang ◽  
Yuan Jiao
Keyword(s):  
Radio Frequency ◽  
System Design ◽  
Signal Strength ◽  
Receive Signal ◽  
The Other ◽  
Receive Signal Strength ◽  
Receive Signal Strength Indicator ◽  
Productivity Effect ◽  
Wireless Transceiver

Calibration of radio frequency (RF) unit is a must during researching and manufacturing of wireless transceiver system. Automation calibration plays an important role in promoting productivity effect. This paper focuses on the design of automation calibration of RF unit in wireless transceiver system. Design of automation calibration is separated by two parts, one is transmission (Tx) power calibration, the other is receive signal strength indicator (RSSI) calibration.

scholarly journals OPTIMALISASI JARINGAN NIRKABEL DENGAN METODE RSSI DI AIKOM TERNATE

2021 ◽  
Vol 9 (1) ◽  
pp. 108-115
Author(s):  
Laroma Larumbia ◽  
Susanti H Hasan ◽  
Seh Turuy
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Blind Spot ◽  
Data Rate ◽  
Receive Signal ◽  
Receive Signal Strength ◽  
Receive Signal Strength Indicator ◽  
Wireless Monitoring

Penelitian ini bertujuan untuk optimalisasi jaringan nirkabel dari titik buta atau blind spot di lingkungan kampus AIKOM Ternate. Area-area titik buta ini membuat pengguna jaringan nirkabel (dosen, staf, dan mahasiswa) tidak nyaman dikarenakan harus mendekat ke sumber jaringan (access point (AP)) terdekat agar dapat mengakses jaringan internet ataupun intranet. Dengan optimalisasi jaringan nirkabel ini, area jangkauan jaringan nirkabel disesuikan dengan kebutuhan agar tidak ada lagi titik buta. Penggunaan aplikasi Wireless Monitoring (Wirelessmon) untuk mendeteksi area jangkauan dari setiap AP yang dipasang, yang diukur adalah receive signal strength indicator (RSSI), termasuk penentuan penggunaan kanal dari setiap AP agar tidak tumpang tindih atau overlapping dalam penggunaan kanal pada setiap AP dan data rate yang mengalami peningkatan dan penurunan menyesuaikan dengan kualitas sinyal yang diterima. Hasil penelitian ini menunjukkan setelah dilakukannya optimalisasi dengan memasang AP pada lima titik. Optimalisasi jaringan nirkabel pada kampus AIKOM Ternate berhasil dengan RSSI -61dBm sampai dengan -44dBm, RSSI termasuk dalam kategori sangat bagus (very good). Disarankan pengukuran RSSI menggunakan software lebih dari satu sehingga dilakukan perbandingan, melakukan perbandingan RSSI yang diterima dengan throughput yang dihasilkan pada perangkat yang berbeda, dan pengelola jaringan dapat menggunakan hasil penelitian ini akan tetapi penggunaan perangkat dengan spesifikasi yang berbeda dengan yang digunakan dalam penelitian ini maka disarankan untuk melakukan pengambilan data ulang agar hasilnya maksimal.

scholarly journals RECEIVE SIGNAL STRENGTH PREDICTION IN THE GSM BAND USING WAVELET DECOMPOSITION

2016 ◽  
Vol 36 (1) ◽  
pp. 172-177
Author(s):  
A Danladi ◽  
NW Silika ◽  
PG Vasira
Keyword(s):  
Wavelet Decomposition ◽  
Indoor Environment ◽  
Signal Strength ◽  
Rayleigh Distribution ◽  
Receive Signal ◽  
Receive Signal Strength ◽  
One Dimensional ◽  
Slow Fading ◽  
Radio Wavelength ◽  
Good Signal

In this work, GSM receive signal strength was monitored in an indoor environment. Samples of  GSM receive signal strength was measured on a Mobile Equipment (ME). One-dimensional multilevel wavelet decomposition technique was used to predict the fading phenomenon of the GSM receive signal strength measured. Wavelet prediction revealed that the GSM receive signal strength is attenuated due to the slow fading phenomenon, which fades about 3 times faster than the radio wavelength. The prediction is further validated using probability density functions in terms of Gaussian and Rayleigh distributions. It is observed that, significant part of the signal strength measured is dominated by good signal (- 101 dBm to – 74 dBm) with an average of – 88.8842 dBm and the signal strength followed more of Gaussian than Rayleigh distribution. This confirmed the wavelet prediction.  http://dx.doi.org/10.4314/njt.v36i1.22

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