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

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Open AccessArticle A Fine Frequency Estimation Algorithm Based on Fast Orthogonal Search (FOS) for Base Station Positioning Receivers
Electronics 2018, 7(12), 376; https://doi.org/10.3390/electronics7120376
Received: 15 September 2018 / Revised: 13 November 2018 / Accepted: 21 November 2018 / Published: 3 December 2018
Viewed by 286 | PDF Full-text (3847 KB) | HTML Full-text | XML Full-text
Abstract
Base station signals have been widely studied as a promising navigation and positioning signal. The time and code division-orthogonal frequency division multiplexing (TC-OFDM) signal is a novel communication and navigation fusion signal that can simultaneously implement communication and positioning services. The TC-OFDM signal [...] Read more.
Base station signals have been widely studied as a promising navigation and positioning signal. The time and code division-orthogonal frequency division multiplexing (TC-OFDM) signal is a novel communication and navigation fusion signal that can simultaneously implement communication and positioning services. The TC-OFDM signal multiplexes the pseudorandom noise (PRN) code, called positioning code, and the Chinese mobile multimedia broadcasting (CMMB) signal in the same frequency band. For positioning in the TC-OFDM receiver, it is necessary to acquire and track the PRN code phase and the carrier frequency. The tracking performance is directly influenced by the accuracy of the signal acquisition, especially the acquired carrier frequency accuracy. This paper focuses on the fine frequency acquisition of TC-OFDM receivers and proposes a novel fine frequency estimation algorithm, which uses a non-linear modelling method, called fast orthogonal search (FOS), to improve the frequency acquisition accuracy of TC-OFDM receivers. With this algorithm, the PRN code is first stripped off in coarse code phase. Then, the candidate functions at each of the interest frequencies are generated, which consist of pairs of sine and cosine terms. Finally, the FOS algorithm is used to detect the carrier frequency. Simulation and experimental results show that, compared with the current carrier frequency estimation algorithms, the proposed algorithm effectively improves carrier frequency estimation accuracy and then reduces the time to the first fix. Full article
Figures

Figure 1

Figure 1
<p>Structure of the fusion signal.</p>
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<p>Coarse acquisition techniques used inside the receivers.</p>
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<p>Structure of the fusion signal.</p>
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<p>The autocorrelation function of short codes: (<bold>a</bold>) under different bandwidths, (<bold>b</bold>) under different SNR.</p>
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<p>The flowchart of the proposed algorithm.</p>
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<p>Scheme of determining <italic>f<sub>m</sub></italic>.</p>
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<p>Campus experimental environment and base station distribution.</p>
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<p>Comparison of root mean square of estimated frequency deviation for five algorithm at SNR = ?25 dB.</p>
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<p>Comparison of root mean square of estimated frequency deviation for five algorithm at SNR = ?30 dB.</p>
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<p>Comparison of converging process of FLL for five algorithms at SNR = ?25 dB and <inline-formula><mml:math display="block" id="mm112"><mml:semantics><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> = 767 Hz.</p>
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<p>Comparison of converging process of FLL for five algorithm at SNR = ?30 dB and <inline-formula><mml:math display="block" id="mm113"><mml:semantics><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> = 567 Hz.</p>
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<p>Comparison of converging process of FLL for five algorithm at SNR = ?25dB and <inline-formula><mml:math display="block" id="mm115"><mml:semantics><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> = 567 Hz.</p>
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<p>Comparison of converging process of FLL to PLL for five algorithms at SNR = ?25 dB and <inline-formula><mml:math display="block" id="mm116"><mml:semantics><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>d</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula> = 767 Hz.</p>
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<p>All equipment of the MDBSS.</p>
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<p>The TC-OFDM receiver: (<bold>a</bold>) the internal and external structure of the receiver and (<bold>b</bold>) positioning data are transmitted to the mobile phone via Bluetooth.</p>
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<p>Comparison of the TTFF of three algorithms of the experimental tests.</p>
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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
Cited by 1 | Viewed by 595 | 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 [...] 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>
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<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>
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<p>The Relationship between Loss of Lock Probability and SNR.</p>
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<p>The RMS Frequency Tracking Error with SNR under different sample observations.</p>
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<p>The residual carrier and phase convergence curve estimated by LM algorithm.</p>
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<p>Frequency error comparison results by MLE and MLE&amp;KF.</p>
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<p>Comparison results of frequency estimation errors by three algorithms under different SNR.</p>
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<p>Comparison of the tracking probabilities between the three algorithms.</p>
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<p>Each component of the modified base stations.</p>
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<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>
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<p>Actual test diagram of the tracking sensitivity between the three algorithms.</p>
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<p>The base station distribution of the test environment on the campus.</p>
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<p>The RMSE positioning accuracy error in horizontal direction.</p>
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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
Cited by 3 | Viewed by 1234 | 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>
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<p>Characteristics of the autocorrelation function: (<bold>a</bold>) is autocorrelation function with infinite bandwidth; and, (<bold>b</bold>) is power spectral density.</p>
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<p>The proportion of the signal component through the filter in the total signal under different bandwidths.</p>
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<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>
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<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>
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<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>
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<p>The correlation of 24-order Taylor and the theoretical correlation.</p>
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<p>The spectrum of received signals: (<bold>a</bold>) is before filtering and (<bold>b</bold>) is after filtering.</p>
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<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>
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<p>The TDOA estimation errors under different signal-to-noise ratio (SNR).</p>
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<p>Each equipment of the modified base station.</p>
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<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>
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<p>Test environment.</p>
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<p>Fading channel distribution.</p>
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<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>
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<p>The positioning accuracy of the entire system: (<bold>a</bold>) is horizontal accuracy and (<bold>b</bold>) is vertical accuracy.</p>
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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 6 | Viewed by 1488 | 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>
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<p>Structure of the communication and navigation fusion signal.</p>
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<p>Flowchart of cyclic correlation method.</p>
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<p>The TPRN leads to the nature of the correlation attenuation.</p>
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<p>Zero-padding leads to the autocorrelation attenuation.</p>
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<p>Correlation using the cyclic correlation method.</p>
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<p>Flowchart of the matched filter method.</p>
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<p>Correlation using the matched filter method: (<bold>a</bold>) autocorrelation results and (<bold>b</bold>) cross-correlation results.</p>
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<p>A condition of MFADD.</p>
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<p>Flowchart of the MFADD.</p>
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<p>Contrast of Correct Rate of Complete Gold Code and Truncated Gold Code.</p>
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<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>
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<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>
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<p>Comparison of detection probability.</p>
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<p>The equipment of MDBBS.</p>
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<p>The positioning receiver: (<bold>a</bold>) appearance and (<bold>b</bold>) internal structure.</p>
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<p>The equipment of MDBBS.</p>
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<p>The positioning accuracy of MDBBS.</p>
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<p>The positioning accuracy of MDBBS: (<bold>a</bold>) horizontal positioning results and (<bold>b</bold>) vertical positioning results.</p>
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