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Authors = Buyun Jia ORCID = 0000-0002-8619-6037

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Open AccessArticle A Novel Carrier Loop Algorithm Based on Maximum Likelihood Estimation (MLE) and Kalman Filter (KF) for Weak TC-OFDM Signals
Sensors 2018, 18(7), 2256; https://doi.org/10.3390/s18072256
Received: 16 May 2018 / Revised: 25 June 2018 / Accepted: 9 July 2018 / Published: 13 July 2018
Viewed by 298 | PDF Full-text (4984 KB) | HTML Full-text | XML Full-text
Abstract
Digital broadcasting signals represent a promising positioning signal for indoors applications. A novel positioning technology named Time & Code Division-Orthogonal Frequency Division Multiplexing (TC-OFDM) is mainly discussed in this paper, which is based on China mobile multimedia broadcasting (CMMB). Signal strength is an
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  • 923| 538| 559| 39| 642| 436| 861| 27| 511| 225| [...] Read more.
    Digital broadcasting signals represent a promising positioning signal for indoors applications. A novel positioning technology named Time & Code Division-Orthogonal Frequency Division Multiplexing (TC-OFDM) is mainly discussed in this paper, which is based on China mobile multimedia broadcasting (CMMB). Signal strength is an important factor that affects the carrier loop performance of the TC-OFDM receiver. In the case of weak TC-OFDM signals, the current carrier loop algorithm has large residual carrier errors, which limit the tracking sensitivity of the existing carrier loop in complex indoor environments. This paper proposes a novel carrier loop algorithm based on Maximum Likelihood Estimation (MLE) and Kalman Filter (KF) to solve the above problem. The discriminator of the current carrier loop is replaced by the MLE discriminator function in the proposed algorithm. The Levenberg-Marquardt (LM) algorithm is utilized to obtain the MLE cost function consisting of signal amplitude, residual carrier frequency and carrier phase, and the MLE discriminator function is derived from the corresponding MLE cost function. The KF is used to smooth the MLE discriminator function results, which takes the carrier phase estimation, the angular frequency estimation and the angular frequency rate as the state vector. Theoretical analysis and simulation results show that the proposed algorithm can improve the tracking sensitivity of the TC-OFDM receiver by taking full advantage of the characteristics of the carrier loop parameters. Compared with the current carrier loop algorithms, the tracking sensitivity is effectively improved by 2–4 dB, and the better performance of the proposed algorithm is verified in the real environment. Full article
    (This article belongs to the Special Issue Selected Papers from UPINLBS 2018)
    Figures

    Figure 1

    Figure 1
    <p>TC-OFDM signal Frame Structure.</p>
    Full article ">Figure 2
    <p>Conventional carrier loop structure.</p>
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    <p>The principle of MLE.</p>
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    <p>LM algorithm flow chart.</p>
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    <p>The proposed carrier loop structure based on MLE and KF.</p>
    Full article ">Figure 6
    <p>The Relationship between Loss of Lock Probability and SNR.</p>
    Full article ">Figure 7
    <p>The RMS Frequency Tracking Error with SNR under different sample observations.</p>
    Full article ">Figure 8
    <p>The residual carrier and phase convergence curve estimated by LM algorithm.</p>
    Full article ">Figure 9
    <p>Frequency error comparison results by MLE and MLE&amp;KF.</p>
    Full article ">Figure 10
    <p>Comparison results of frequency estimation errors by three algorithms under different SNR.</p>
    Full article ">Figure 11
    <p>Comparison of the tracking probabilities between the three algorithms.</p>
    Full article ">Figure 12
    <p>Each component of the modified base stations.</p>
    Full article ">Figure 13
    <p>The TC-OFDM receiver. (<bold>a</bold>) is the internal and external structure of the TC-OFDM receiver; and (<bold>b</bold>) is the communication between the positioning receiver and the mobile phone.</p>
    Full article ">Figure 14
    <p>Actual test diagram of the tracking sensitivity between the three algorithms.</p>
    Full article ">Figure 15
    <p>The base station distribution of the test environment on the campus.</p>
    Full article ">Figure 16
    <p>The RMSE positioning accuracy error in horizontal direction.</p>
    Full article ">
    Open AccessArticle A Pseudorange Measurement Scheme Based on Snapshot for Base Station Positioning Receivers
    Sensors 2017, 17(12), 2783; https://doi.org/10.3390/s17122783
    Received: 15 October 2017 / Revised: 27 November 2017 / Accepted: 28 November 2017 / Published: 1 December 2017
    Viewed by 892 | PDF Full-text (7838 KB) | HTML Full-text | XML Full-text
    Abstract
    Digital multimedia broadcasting signal is promised to be a wireless positioning signal. This paper mainly studies a multimedia broadcasting technology, named China mobile multimedia broadcasting (CMMB), in the context of positioning. Theoretical and practical analysis on the CMMB signal suggests that the existing
    [...] Read more.
    Digital multimedia broadcasting signal is promised to be a wireless positioning signal. This paper mainly studies a multimedia broadcasting technology, named China mobile multimedia broadcasting (CMMB), in the context of positioning. Theoretical and practical analysis on the CMMB signal suggests that the existing CMMB signal does not have the meter positioning capability. So, the CMMB system has been modified to achieve meter positioning capability by multiplexing the CMMB signal and pseudo codes in the same frequency band. The time difference of arrival (TDOA) estimation method is used in base station positioning receivers. Due to the influence of a complex fading channel and the limited bandwidth of receivers, the regular tracking method based on pseudo code ranging is difficult to provide continuous and accurate TDOA estimations. A pseudorange measurement scheme based on snapshot is proposed to solve the problem. This algorithm extracts the TDOA estimation from the stored signal fragments, and utilizes the Taylor expansion of the autocorrelation function to improve the TDOA estimation accuracy. Monte Carlo simulations and real data tests show that the proposed algorithm can significantly reduce the TDOA estimation error for base station positioning receivers, and then the modified CMMB system achieves meter positioning accuracy. Full article
    Figures

    Figure 1

    Figure 1
    <p>Terrestrial single frequency network coverage of the China mobile multimedia broadcasting (CMMB) system.</p>
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    <p>Flowchart of the signal generation.</p>
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    <p>Structure of the fusion signal.</p>
    Full article ">Figure 4
    <p>Characteristics of the autocorrelation function: (<bold>a</bold>) is autocorrelation function with infinite bandwidth; and, (<bold>b</bold>) is power spectral density.</p>
    Full article ">Figure 5
    <p>The proportion of the signal component through the filter in the total signal under different bandwidths.</p>
    Full article ">Figure 6
    <p>The autocorrelation function under different bandwidths.</p>
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    <p>Flowchart of the proposed algorithm.</p>
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    <p>Flowchart of the proposed algorithm.</p>
    Full article ">Figure 9
    <p>The Taylor approximation error when <inline-formula> <mml:math display="block" id="mm74"> <mml:semantics> <mml:mrow> <mml:mi>β</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1.6</mml:mn> </mml:mrow> </mml:semantics> </mml:math> </inline-formula>.</p>
    Full article ">Figure 10
    <p>The 24-order Taylor approximation error when <inline-formula> <mml:math display="block" id="mm75"> <mml:semantics> <mml:mrow> <mml:mi>β</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1.6</mml:mn> </mml:mrow> </mml:semantics> </mml:math> </inline-formula>.</p>
    Full article ">Figure 11
    <p>The correlation of 24-order Taylor and the theoretical correlation.</p>
    Full article ">Figure 12
    <p>The spectrum of received signals: (<bold>a</bold>) is before filtering and (<bold>b</bold>) is after filtering.</p>
    Full article ">Figure 13
    <p>The time difference of arrival (TDOA) estimation errors under different setting code phase differences: (<bold>a</bold>) is SNR = 0 dB and (<bold>b</bold>) is SNR = ?15 dB.</p>
    Full article ">Figure 14
    <p>The TDOA estimation errors under different signal-to-noise ratio (SNR).</p>
    Full article ">Figure 15
    <p>Each equipment of the modified base station.</p>
    Full article ">Figure 16
    <p>The positioning receiver: (<bold>a</bold>) is the internal and external structure of the receiver, and (<bold>b</bold>) shows that the receiver uploads the related data to the mobile phone through Bluetooth to display.</p>
    Full article ">Figure 17
    <p>Test environment.</p>
    Full article ">Figure 18
    <p>Fading channel distribution.</p>
    Full article ">Figure 19
    <p>The TDOAs for the No. 1 point of the 3rd floor: (<bold>a</bold>) is the fluctuations with time and (<bold>b</bold>) is the corresponding boxplot.</p>
    Full article ">Figure 20
    <p>The positioning accuracy of the entire system: (<bold>a</bold>) is horizontal accuracy and (<bold>b</bold>) is vertical accuracy.</p>
    Full article ">
    Open AccessArticle An Acquisition Scheme Based on a Matched Filter for Novel Communication and Navigation Fusion Signals
    Sensors 2017, 17(8), 1766; https://doi.org/10.3390/s17081766
    Received: 20 June 2017 / Revised: 21 July 2017 / Accepted: 28 July 2017 / Published: 2 August 2017
    Cited by 3 | Viewed by 1118 | PDF Full-text (4440 KB) | HTML Full-text | XML Full-text
    Abstract
    In order to enhance the positioning capability of terrestrial networks, a novel communication and navigation fusion signal is proposed. The novel signal multiplexes the communication and navigation signal in the same frequency band, and the navigation system is superimposed on the original communication
    [...] Read more.
    In order to enhance the positioning capability of terrestrial networks, a novel communication and navigation fusion signal is proposed. The novel signal multiplexes the communication and navigation signal in the same frequency band, and the navigation system is superimposed on the original communication system. However, the application of pseudorandom noise (PRN) sequences in the navigation system is limited by the communication clock period. Taking the application of PRN sequences limited by the clock period as objects, the present study analyzes truncated PRN (TPRN) sequences. PRN sequences with a TPRN sequence as the navigation signal can overcome the communication system clock period limitation. Then, a matched filter algorithm with double detection (MFADD) is proposed to acquire the novel signal. The matched filter method is applied to the proposed algorithm to determine the start code phase of TPRN. Monte Carlo simulations and real data tests demonstrate the effectiveness of the proposed algorithm for the designed signal. Full article
    Figures

    Figure 1

    Figure 1
    <p>Flowchart of communication and navigation fusion signal generation.</p>
    Full article ">Figure 2
    <p>Structure of the communication and navigation fusion signal.</p>
    Full article ">Figure 3
    <p>Flowchart of cyclic correlation method.</p>
    Full article ">Figure 4
    <p>The TPRN leads to the nature of the correlation attenuation.</p>
    Full article ">Figure 5
    <p>Zero-padding leads to the autocorrelation attenuation.</p>
    Full article ">Figure 6
    <p>Correlation using the cyclic correlation method.</p>
    Full article ">Figure 7
    <p>Flowchart of the matched filter method.</p>
    Full article ">Figure 8
    <p>Correlation using the matched filter method: (<bold>a</bold>) autocorrelation results and (<bold>b</bold>) cross-correlation results.</p>
    Full article ">Figure 9
    <p>A condition of MFADD.</p>
    Full article ">Figure 10
    <p>Flowchart of the MFADD.</p>
    Full article ">Figure 11
    <p>Contrast of Correct Rate of Complete Gold Code and Truncated Gold Code.</p>
    Full article ">Figure 12a
    <p>Normalized non-coherent integration of 31 non-coherent integration results of different incoming signal phase: (<bold>a</bold>–<bold>c</bold>) three significant autocorrelation peaks and (<bold>d</bold>,<bold>e</bold>) two significant autocorrelation peaks.</p>
    Full article ">Figure 12b
    <p>Normalized non-coherent integration of 31 non-coherent integration results of different incoming signal phase: (<bold>a</bold>–<bold>c</bold>) three significant autocorrelation peaks and (<bold>d</bold>,<bold>e</bold>) two significant autocorrelation peaks.</p>
    Full article ">Figure 13
    <p>Comparison of detection probability.</p>
    Full article ">Figure 14
    <p>The equipment of MDBBS.</p>
    Full article ">Figure 15
    <p>The positioning receiver: (<bold>a</bold>) appearance and (<bold>b</bold>) internal structure.</p>
    Full article ">Figure 16
    <p>The equipment of MDBBS.</p>
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    <p>The positioning accuracy of MDBBS.</p>
    Full article ">Figure 18
    <p>The positioning accuracy of MDBBS: (<bold>a</bold>) horizontal positioning results and (<bold>b</bold>) vertical positioning results.</p>
    Full article ">

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