In Wi-Fi fingerprint positioning, what we should most care about is the distance relationship between the user and the reference points (RP). However, most of the existing weighted k-nearest neighbor (WKNN) algorithms use the Euclidean distance of received signal strengths (RSS) as distance measure for fingerprint matching, and the RSS Euclidean distance is not consistent with the position distance. To address this issue, this paper analyzes the relationship between RSS similarity and position distance, propose a novel WKNN based on signal similarity and spatial position. Firstly, we obtain the weighted Euclidean distance (WED) by balancing the size between the RSS difference and the signal propagation distance difference according to the attenuation law of the spatial signal. Then, we obtain the approximate position distance (APD) by making full use of the position distances and WEDs between RPs. Finally, the nearest RPs can be selected more accurately based on the APDs between the user and different RPs, and the position of user can be estimated by the proposed WKNN based on the APD (APD-WKNN) algorithm. In order to fully evaluate the proposed algorithm, we use three fingerprint databases for comparison experiments with eight fingerprint positioning algorithms. The results show that the proposed algorithm can significantly improve the positioning accuracy of WKNN algorithm. INDEX TERMS Fingerprint positioning, weighted k-nearest neighbor, RSS similarity, position distance. I. INTRODUCTION With the rapid development of Location-based Services (LBS), many positioning technologies and signal processing methods [1]-[5] have been proposed. Due to the obstruction of the building, the usability of indoor navigation satellite signals is poor. This makes the Global Navigation Satellite System (GNSS) unable to guarantee satisfactory positioning performance in the indoor environment [6]. Therefore, various indoor positioning technologies have been proposed, among which the Wi-Fi fingerprint positioning is widely used because it can achieve positioning using only existing network facilities. The basic idea of Wi-Fi fingerprint positioning is to use the received signal strength (RSS) of Wi-Fi signal to The associate editor coordinating the review of this manuscript and approving it for publication was Liangtian Wan .

A low-power wideband common-gate (CG) low-noise amplifier (LNA) presented. The CG LNA uses double g m enhancement to provide input matching under low-power consumption. Feed-forward noise cancellation (FFNC) is employed in the LNA to suppress the noise from the CG transistor. The LNA is designed and fabricated in TSMC 130-nm CMOS technology. This LNA can achieve a maximum gain of 14 dB with a 3 dB bandwidth from 350 to 950 MHz. The LNA consumes 0.5 mA current under a 0.8-V supply. The average noise figure of the LNA is 4.0 dB. The core area of the LNA is 0.06 mm 2 .

This paper presents a method for extracting system nonlinearities and time-localized transient response to impulsive loading by processing stationary/transient responses using the Hilbert—Huang transform (HHT) and a sliding-window fitting (SWF) technique. Time-dependent dynamic characteristics of nonlinear systems are derived using perturbation analysis. The SWF is introduced mainly to show the mathematical implications of HHT and the differences between HHT and the discrete Fourier transform. Similar to the wavelet transform the SWF uses windowed predetermined regular harmonics and function orthogonality to extract local harmonic components. It simultaneously decomposes a signal into just a few regular/distorted harmonics, and the obtained time-varying amplitudes and frequencies of the harmonics can reveal system nonlinearities. On the other hand the HHT uses the apparent time scales revealed by the signal's local maxima and minima and cubic splines of the extrema to sequentially sift components of different time scales, starting from high-frequency to low-frequency ones. Because HHT does not use predetermined basis functions and function orthogonality for component extraction, components are extracted without distortion and hence their time-varying amplitudes and frequencies can be accurately computed using the Hilbert transform to reveal system characteristics and nonlinearities. Moreover, because the first component extracted from HHT contains all discontinuities of the original signal, its time-varying frequency and amplitude are excellent indicators for pinpointing the time instants of impulsive loads. However, the discontinuity-induced Gibbs' phenomenon makes HHT analysis inaccurate around the two data ends. On the other hand, the SWF analysis suffers less from Gibbs' phenomenon at the two data ends, but it cannot extract accurate time-varying frequencies and amplitudes because the use of predetermined basis functions and function orthogonality in the sliding-window fitting process distorts the extracted components. Numerical and experimental results show that the proposed method with the use of HHT can provide accurate extraction of intrawave amplitude and phase modulations, distorted harmonic response under a single-frequency harmonic excitation, softening and hardening effects, different orders of nonlinearity, interwave amplitude and phase modulations, multiple-mode vibrations caused by internal/ external resonances, and time instants of impact loading on a structure. These are key phenomena for performing dynamics-based system identification and damage detection.

A Global Satellite Navigation System (GNSS) cannot provide normal location services in an indoor environment because the signals are blocked by buildings. The Beidou satellite navigation system (BDS)/GPS indoor array pseudolite system is proposed to overcome the problems of indoor positioning with conventional pseudolite, such as time synchronization, ambiguity resolution and base stations. At the same time, an algorithm for Doppler differential positioning is proposed to improve the indoor positioning accuracy and the positioning coverage of the system, which uses the Doppler difference equation and Known Point Initialization (KPI) to determinate the velocity and position of the receiver. Experiments were conducted to verify the proposed system under different conditions; the average positioning error of the Doppler differential positioning algorithm was 7.86 mm in the kinematic test and 2.9 mm in the static test. The results show that BDS/GPS indoor array pseudolite system has the potential to make indoor positioning achieve sub-centimeter precision. Finally, the positioning error of the proposed algorithm is also analyzed, and the data tests show that the dilution of precision (DOP) and cycle- slips have a significant impact on the indoor positioning accuracy; a cycle-slip of a half-wavelength can cause positioning errors of tens of millimeters. Therefore, the Doppler-aided cycle-slip detection method (DACS) is proposed to detect cycle-slips of one cycle or greater than one, and the carrier phase double difference cycle-slip detection method (CPDD) is used to detect cycle slips of a half-wavelength.