Search

Simultaneously charging multiple devices is a unique advantage of wireless power transfer (WPT). However, it is challenging to analyze and optimize the performance of a multireceiver (multi-Rx) WPT system due to the intricate coupling among coils. In this article, a method for adaptive decoupling between Rxs is proposed for electronic device charging, which can achieve independent regulation of each output in a multi-Rx WPT system. A simple feedback control avoids tedious system structure and position parameters’ measurement and calculation. With a fixed driving frequency, the number of Rx is not limited. It is analyzed that the quadrature phenomenon of the transmitter (Tx) and Rx currents can be used as a sign that the cross-coupling is fully compensated. A phase synchronization channel is established that shares power transfer coils. An microprogrammed control unit-(MCU) free feedback loop is constructed in Rx to control the variable switched capacitor and finally realize adaptive cross-coupling compensation. A two-load experimental prototype was built to verify the theoretical analysis. Compared with the system without adaptive decoupling between Rxs, the measured system efficiency and output power of the proposed system were improved by 69% and 14.3 times, respectively.

To increase the measurement rate and reduce the ranging error of real-time in-situ laser ranging in full-waveform light detection and ranging (LiDAR), a back propagation (BP) neural network-based online ranging method (BPO) implemented on FPGA is proposed. In BPO, a three-layer BP neural network model is trained and implemented on a Virtex-6 FPGA to estimate the parameters of laser pulses for ranging in real time. The training dataset is generated using the multi-pulse coherent average method combined with the B-spline interpolation method. In the implementation of BPO, the parallel-pipeline architecture is employed to accelerate the computational process. Simulations proved that BPO decreased the time consumption of a single measurement to $2.95~\mu \text{s}$ . The measurement rate was increased to 339 kHz by BPO, which was approximately as 1.2 times as the measurement rate of Gauss-Newton-based online ranging method (GNO). Experiments were conducted by an FPGA-based in-situ ranging system to evaluate the ranging error of BPO. The results revealed that the mean and standard deviation of ranging errors of BPO were 0.8 mm and 1.4 cm for a laser pulse with an SNR of 36.4 dB, respectively. For a laser pulse with an SNR of 21.8 dB, the mean and standard deviation of ranging errors of BPO were 1.7 cm and 5.5 cm, which decreased 0.8 cm and 1.7 cm from those of GNO, implying that the proposed method is capable of mapping the trailing laser pulse and reducing the ranging error caused by the trailing distortion.

The regulated output characteristic is crucial for a wireless power transfer (WPT) system. Most studies focus on the single-load system or the system with multiple evenly distributed loads; however, the loads might distribute in space at different distances from the power supply in practice. In this article, a WPT hybrid repeater system is proposed to power multiple loads over a long distance. The output mode of each load in the system can be switched separately to realize the load-independent constant-current (CC) or constant-voltage (CV) output. Based on four compensation network combinations of a two-load WPT repeater system with CC/CV output, the structure of the $N$ -load WPT hybrid repeater system with load-independent CC/CV output is derived. With the ZPA input characteristic, fixed operating frequency, and high system efficiency, the proposed system can be easily used in various medium- or low-power applications. The effects of system parameters on output characteristics, power transfer capability, and system efficiency are investigated. A three-load experimental prototype was built to verify the theoretical analysis. Measurement results were in good agreement with the analysis, and the maximum measured efficiency reached 83%.

To increase the measurement rate of real-time in-situ laser ranging, an online Gauss-Newton-based parallel-pipeline method (GNPPo) is proposed to extract waveform parameters. The proposed method includes waveform pre-processing for initial estimation, variable substitution for fast computation, and Gauss-Newton method for optimization. In waveform pre-processing, parallel-pipeline architecture was used to obtain initial values of waveform parameters as input for Gauss-Newton method. In the variable substitution, the initial values were transformed to reduce the number of divisions and accelerate Jacobi matrix calculation. To obtain the optimized waveform parameters, adjoint matrix method was used to calculate the iteration vector in Gauss-Newton method. The optimized waveform parameters required inverse transformation by variable substitution for accurately calculating the distance. The proposed method employed parallel-pipeline architecture, implemented on Kintex-7 FPGA. Simulations first proved that the proposed method decreased time cost and increased the occupied computational resource. Extensive experiments were conducted using a lab-built full-waveform ranging system. In comparison with the online Gauss-Newton-based pipeline method (GNPo) and the Gauss-Newton-based post-processing method (GNp), experimental results revealed that the proposed method realizes a range measurement rate of 285.7 kHz, approximately 2.5 times and 49 times as fast as GNPo and GNp, respectively. The mean range error and range standard deviation of GNPPo are 0.6 cm and 1.7 cm, at a distance of 21 m with an SNR of 36.6 dB, roughly the same scale of GNp, and a millimeter-scale increase of GNPo. The proposed method will be used for LiDAR application on autonomous vehicles with real-time centimeter accuracy.

In this paper, an online echo waveform fitting-based method is proposed for the real-time in situ laser ranging. In the method, an embedded FPGA is employed to realize the fast single-pulse fitting, and the parameters of the echo waveform of an object under detection are, thus, extracted in an online manner. The parameters of the echo waveform are utilized to estimate the time interval between the emitted pulse and the echo. The distance of the object is then calculated from the time interval. The simulations were first done to analyze and obtain the computational resource consumption and the time cost of a single measurement. Then, an FPGA- and ADC-based ranging system was designed, and an experiment was carried out to verify the proposed method. The results show that a single measurement of the distance can be done within $8.7~\mu \text{s}$ , which is fast enough for online ranging. At a distance of 70 m with an SNR of 18.4 dB, the mean range error and the range standard deviation are 4.9 and 10.7 cm, respectively. In addition, the proposed method was also compared with the TDC-based in situ method and the Gauss–Newton algorithm-based off-line method.

A co-path full-waveform light detection and ranging (LiDAR) system for detection of multiple objects along laser path is designed in this paper. The co-path full-waveform LiDAR system consists of optical and electrical units. The optical unit implements emission of laser pulses and reception of laser echoes, while the electrical unit controls the emission of laser pulses and acquires the waveforms of both emitted laser pulses and reflected echoes. Then, the acquired echoes are denoised through wavelet decomposition and fitted using Gaussian functions to obtain distances between the objects and the co-path full-waveform LiDAR system. To analyze the performance of the co-path full-waveform LiDAR system, a peak signal-to-noise ratio (PSNR) model of the received laser echoes is established and verified by using a diffuse reflectance reference target (DRRT) with 30% reflectivity at distances from 30 to 105 m. Meanwhile, the ranging uncertainty of the co-path full-waveform LiDAR system for the DRRT at distances from 30 to 105 m was analyzed. Experimental results show that the PSNRs calculated from the received laser echoes are 4 dB lower than the expected ones calculated by the established PSNR model, implying that the established PSNR model is of acceptable accuracy and the system is of better detection performance. The ranging uncertainties of the system for the DRRT at 30 and 105 m are 10 and 32 mm, respectively. In addition, experiments were carried out to verify the ranging performance of the co-path full-waveform LiDAR system for multiple along-path objects. Results show that (1) the system can decrease the influence of waveform overlap between the pulses reflected by two adjacent objects and improve the ranging accuracy; (2) the system can detect multiple along-path objects from the laser echoes when the distance between each two adjacent objects is larger than 1.5 times the full width at half maximum (FWHM) of each returned laser pulse; (3) the system can be applied to surveillance of the group of Unmanned Aerial Vehicles (UAVs).

To improve the positional accuracy of point-clouds, a self-calibration model of a terrestrial laser scanner (TLS) was used to calibrate mounting angle errors as systematic errors. The model was based on the TLS scanning mechanism, and parameters of the model were determined by using measured TLS point-clouds. To automatically solve parameters of the self-calibration model, a background target-based autonomous method was proposed to find the feature points used as input for the model. The autonomous self-calibration method presented here involved three steps: determination of initial feature points using a keypoint quality algorithm with no prior knowledge, filtering the initial feature points to find coarse feature points using a $K$ -means algorithm, optimization of coarse feature points to find fine feature points based on measurement precision. In comparison with the auxiliary target-based method, experimental results showed that the background target-based autonomous method is a valid self-calibration method for TLS, with comparable measurement precision and a simplified self-calibration procedure.

Time of flight (TOF) based light detection and ranging (LiDAR) is a technology for calculating distance between start/stop signals of time of flight. In lab-built LiDAR, two ranging systems for measuring flying time between start/stop signals include time-to-digital converter (TDC) that counts time between trigger signals and analog-to-digital converter (ADC) that processes the sampled start/stop pulses waveform for time estimation. We study the influence of waveform characteristics on range accuracy and precision of two kinds of ranging system. Comparing waveform based ranging (WR) with analog discrete return system based ranging (AR), a peak detection method (WR-PK) shows the best ranging performance because of less execution time, high ranging accuracy, and stable precision. Based on a novel statistic mathematical method maximal information coefficient (MIC), WR-PK precision has a high linear relationship with the received pulse width standard deviation. Thus keeping the received pulse width of measuring a constant distance as stable as possible can improve ranging precision.

In this paper, a polarized active imaging technology was utilized for measuring the moisture content of the holly leaves. Polarization images of the holly leaves encoded with the polarization degree were acquired by the polarized active imaging LiDAR. The polarization degree of the holly leaves was used as a variable to generate a mapping model to reflect the moisture content. The polarization degrees were calculated from polarization images matched by the method of Speeded Up Robust Feature (SURF) algorithm and affine transformation. Images were denoised by using the multiple iterative median filter and the Lee filter. The actual moisture contents of the holly leaves were obtained by the method of moisture drying processing. The predictive accuracy and stabilities were utilized to establish an optimal mapping model by the method of statistical fitting. Experiments were carried out to validate the mapping model between the moisture content and the polarization degree of the holly leaves. The results show that the polarization degrees of the holly leaves are significantly positive-correlated with the moisture content rising from 45% to 75%. The relationship between the moisture content and the polarization degree of the holly leaves was presented with exponential model.