Joint Angle and Frequency Estimation Using One-Bit Measurements

Li, Zeyang; Shi, Junpeng; Wang, Xinhai; Wen, Fangqing

doi:10.3390/s19245422

Cited by 10 publications

(10 citation statements)

References 38 publications

(42 reference statements)

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“…8 and Table 10. The 50%, 75% and mean error of the APD-WKNN are 1.58 m, 3.06 m and 2.32 m, which are smaller than 1 The positioning error on Database2 is shown in Fig. 9 and Table 11.…”

Section: Positioning Performance Comparison With Machine Learning-mentioning

confidence: 81%

A Novel Weighted KNN Algorithm Based on RSS Similarity and Position Distance for Wi-Fi Fingerprint Positioning

et al. 2020

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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 .

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“…8 and Table 10. The 50%, 75% and mean error of the APD-WKNN are 1.58 m, 3.06 m and 2.32 m, which are smaller than 1 The positioning error on Database2 is shown in Fig. 9 and Table 11.…”

Section: Positioning Performance Comparison With Machine Learning-mentioning

confidence: 81%

A Novel Weighted KNN Algorithm Based on RSS Similarity and Position Distance for Wi-Fi Fingerprint Positioning

et al. 2020

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“…Compared with the unit, ASPeCT, GRASS and Yan 2015, the SCST performs best in eliminating the side peak performance, phase discrimination curve, anti-multipath performance and code tracking accuracy. This provides a prerequisite for the integration of the new generation of navigation systems with other systems, such as DOA [1]- [3], [42]- [44] and 3D imaging [45] combined with GNSS and radar.…”

Section: Discussionmentioning

confidence: 99%

“…sign(sin(2π f s )t), sine modulation sign(cos(2π f s )t), cosine modulation (3) Subcarriers can also be expressed in mathematical formulas as:…”

Section: Sc(t) =mentioning

confidence: 99%

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Unambiguous Tracking Technique Based on Shape Code for BOC Signals

Fang

Gan

2020

IEEE Access

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The multiple peaks characteristic of binary offset carrier (BOC) autocorrelation function (ACF) makes ambiguity easy to be generated. There are some methods to eliminate ambiguity, but for higher-order BOC Signal, it will sacrifice signal energy or cannot maintain narrow correlation. This paper studies these problems. According to the idea of GRASS algorithm shape code, this paper proposes an unambiguous tracking algorithm, Sub Cross-correlation Shift Technique (SCST), which is suitable for BOC(m,n) signals.The key to this algorithm is to generate a new cross-correlation function between the BOC signal and the PRN code based on the shape code. The new cross-correlation function is linearly combined with the autocorrelation function of the BOC signal to remove sub-peak interference and achieve high-accuracy tracking. The phase discrimination function is given, and the effectiveness of the tracking algorithm is analyzed theoretically. The disadvantage of this method is that it needs multilevel storage, which will bring extra resource consumption to the receiver. For comparison, Unit Correlation, ASPeCT, GRASS, and the algorithm proposed by Yan are proposed. Experiments show that SCST can completely remove the side peak, and the phase discriminator output has only two main peaks, which successfully eliminates the false lock point. The multipath error envelope has only one and the smallest area. In terms of code tracking accuracy, for a BOC(10,5) signal with a received signal-to-noise ratio (SNR) of −28 dB, SCST is less than 45.8%, 67.5%, and 12.2% of the Unit Correlation, GRASS, and Yan proposed algorithms. BOC, tracking, PRN code, unambiguous. INDEX TERMS

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“…After dispreading, the signal power is 23-30 dB higher than the noise power. Therefore, the traditional Direction of Arrival (DOA) estimation algorithm [27][28][29] can be used to measure the direction of arrival of the spoofing. In this paper, we use the multiple signal classification (MUSIC) algorithm [30] as an example to introduce the process of estimating the direction of arrival of spoofing.…”

Section: Suppressing Spoofing Based On Direction Of Arrival (Doa)mentioning

confidence: 99%

Adaptive Spoofing Suppression Algorithm for GNSS Based on Multiple Antennas Array

Fan

Gan

et al. 2020

Sensors

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The signals of navigation satellites are easily affected by spoofing interference, causing the wrong position, speed or Universal Time Coordinate of the receiver to be calculated. Traditional detection and suppression algorithms are used only to eliminate the spoofing signals, which may lead to an insufficient number of satellites for positioning. An adaptive spoofing suppression algorithm (ASSA) based on a multiple antenna array is proposed in this study. The ASSA can use the cross-correlation gain of multiple antenna array to adaptively generate nulling and realize the simultaneous suppression of multiple spoofing signals. Moreover, ASSA does not need to capture and track spoofing separately, thus reducing the complexity of implementation and calculation. Experiments were conducted to verify the proposed system under different conditions, and the results show that ASSA can suppress multiple spoofings with little impact on positioning performance. Under the condition of spoofing, ASSAs were (2.22 m, 2.41 m, 4.43 m) in the static test and (2.27 m, 2.43 m, 4.64 m) in the kinematic test, which are good positioning performances for both. In addition, the ASSA is applied before capturing signals, which is beneficial to identifying and eliminating spoofing earlier and faster.

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Joint Angle and Frequency Estimation Using One-Bit Measurements

Cited by 10 publications

References 38 publications

A Novel Weighted KNN Algorithm Based on RSS Similarity and Position Distance for Wi-Fi Fingerprint Positioning

A Novel Weighted KNN Algorithm Based on RSS Similarity and Position Distance for Wi-Fi Fingerprint Positioning

Unambiguous Tracking Technique Based on Shape Code for BOC Signals

Adaptive Spoofing Suppression Algorithm for GNSS Based on Multiple Antennas Array

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