Frequency-Modulated Signal Measurement Using Closed-Loop Methodology.

Frequency-modulated (FM) signals are widely used in sensing, measurement, and signal detection due to their strong anti-interference and easy transmission characteristics. Although the high-precision measurement methods for static signals are quite complete, the high-precision measurement methods for dynamic FM signals still need to be studied, and the measurement accuracy in the high-sampling system still has room for improvement. Traditionally, the equal-precision measurement method is widely applied in most scenarios. However, its accuracy is limited by the quantization error of ±1 word and the sampling gate time, making it difficult to improve the frequency measurement accuracy while ensuring a high sampling rate at the same time. In this paper, a high-precision feedback frequency measurement system with the capability to eliminate the quantization error of ±1 word is proposed. The proposed system consists of two stages, the rough measurement stage based on the equal-precision measurement method and the precise measurement stage based on the negative feedback tracking architecture using the phase-frequency detector (PFD) and direct digital synthesizer (DDS). The effectiveness and feasibility of the system are verified by both simulation and experiment. At the sampling rate of 2 kHz, the frequency measurement accuracy is improved by more than 30 dB.

A Source Localization Method Using Complex Variational Mode Decomposition.

Source localization with a passive sensors array is a common topic in various areas. Among the popular source localization algorithms, the compressive sensing (CS)-based method has recently drawn considerable interest because it is a high-resolution method, robust with coherent sources and few snapshots, and applicable for mixed near-field and far-field source localization. However, the CS-based methods rely on the dense grid to ensure the required estimation precision, which is time-consuming and impractical. This paper applies the complex variational mode decomposition (CVMD) to source localization. Specifically, the signal model of the source localization problem is similar to the time-domain frequency-modulated signal model. Motivated by this, we extend CVMD, initially designed for nonstationary time-domain signal analysis, to array signal processing. The decomposition results of the array measurements can correspond to the potential sources at different locations. Then, the sources' direction and range can be estimated by model fitting with the decomposed subsignals. The simulation results show that the proposed CVMD-based method can locate the pure far-field, pure near-field, mixed far-field, and near-field sources. Notably, it can yield high-resolution localization for the coherent sources with one single snapshot with low computing time.

A New Self-Calibration and Compensation Method for Installation Errors of Uniaxial Rotation Module Inertial Navigation System.

Calibration and compensation techniques are essential to improve the accuracy of the strap-down inertial navigation system. Especially for the new uniaxial rotation module inertial navigation system (URMINS), replacing faulty uniaxial rotation modules introduces installation errors between modules and reduces navigation accuracy. Therefore, it is necessary to calibrate these systems effectively and compensate for the installation error between modules. This paper proposes a new self-calibration and compensation method for installation errors without additional information and equipment. Using the attitude, velocity, and position differences between the two sets of navigation information output from URMINS as measurements, a Kalman filter is constructed and the installation error is estimated. After URMINS is compensated for the installation error, the average of the demodulated redundant information is taken to calculate the carrier's navigation information. The simulation results show that the proposed method can effectively assess the installation error between modules with an estimation accuracy better than 5". Experimental results for static navigation show that the accuracy of heading angle and positioning can be improved by 73.12% and 81.19% after the URMINS has compensated for the estimated installation errors. Simulation and experimental results further validate the effectiveness of the proposed self-calibration and compensation method.

A Modified Complex Variational Mode Decomposition Method for Analyzing Nonstationary Signals with the Low-Frequency Trend.

Complex variational mode decomposition (CVMD) has been proposed to extend the original variational mode decomposition (VMD) algorithm to analyze complex-valued data. Conventionally, CVMD divides complex-valued data into positive and negative frequency components using bandpass filters, which leads to difficulties in decomposing signals with the low-frequency trend. Moreover, both decomposition number parameters of positive and negative frequency components are required as prior knowledge in CVMD, which is difficult to satisfy in practice. This paper proposes a modified complex variational mode decomposition (MCVMD) method. First, the complex-valued data are upsampled through zero padding in the frequency domain. Second, the negative frequency component of upsampled data are shifted to be positive. Properties of analytical signals are used to get the real-valued data for standard variational mode decomposition and the complex-valued decomposition results after frequency shifting back. Compared with the conventional method, the MCVMD method gives a better decomposition of the low-frequency signal and requires less prior knowledge about the decomposition number. The equivalent filter bank structure is illustrated to analyze the behavior of MCVMD, and the MCVMD bi-directional Hilbert spectrum is provided to give the time-frequency representation. The effectiveness of the proposed algorithm is verified by both synthetic and real-world complex-valued signals.

Cold Starting Temperature Drift Modeling and Compensation of Micro-Accelerometer Based on High-Order Fourier Transform.

The traditional temperature modeling method is based on the full heating of the accelerometer to achieve thermal balance, which is not suitable for the cold start-up phase of the micro-accelerometer. For decreasing the complex temperature drift of the cold start-up phase, a new temperature compensation method based on a high-order Fourier transform combined model is proposed. The system structure and repeatability test of the micro digital quartz flexible accelerometer are provided at first. Additionally, we analyzed where the complex temperature drift of the cold start-up phase comes from based on the system structure and repeatability test. Secondly, a high-order temperature compensation model combined with K-means clustering and the symbiotic organisms search (SOS) algorithm is established with repeatability test data as training data. To verify the proposed temperature compensation model, a test platform was built to transmit the measured values before and after compensation with the proposed Fourier-related model and the other time-related model, which is also a model aiming at temperature compensation in the cold start-up phase. The experimental results indicate that the proposed method achieves better compensation accuracy compared with the traditional temperature compensation methods and the time-related compensation model. Furthermore, the compensation for the cold start-up phase has no effect on the original accuracy over the whole temperature range. The stability of the accelerometer can be significantly improved to about 30 µg in the start-up phase of different temperatures after compensation.

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming.

Utilizing the difference in phase and power spectrum between signals and noise, the estimation of direction of arrival (DOA) can be transferred to a spatial sample classification problem. The power ratio, namely signal-to-noise ratio (SNR), is highly required in most high-resolution beamforming methods so that high resolution and robustness are incompatible in a noisy background. Therefore, this paper proposes a Subspaces Deconvolution Vector (SDV) beamforming method to improve the robustness of a high-resolution DOA estimation. In a noisy environment, to handle the difficulty in separating signals from noise, we intend to initial beamforming value presets by incoherent eigenvalue in the frequency domain. The high resolution in the frequency domain guarantees the stability of the beamforming. By combining the robustness of conventional beamforming, the proposed method makes use of the subspace deconvolution vector to build a high-resolution beamforming process. The SDV method is aimed to obtain unitary frequency matrixes more stably and improve the accuracy of signal subspaces. The results of simulations and experiments show that when the input SNR is less than -27 dB, signals of decomposition differ unremarkably in the subspace while the SDV method can still obtain clear angles. In a marine background, this method works well in separating the noise and recruiting the characteristics of the signal into the DOA for subsequent processing.

Integrated tilt angle sensing for large displacement scanning MEMS mirrors.

Fourier transform spectrometers (FTS) based on piston-scanning MEMS mirrors have clear advantages of small size and low cost. However, the performance of this type of MEMS FTS is seriously limited by the difficulty of precisely controlling the tilt angle of the MEMS mirror plate during its piston scanning. This paper reports an integrated tilt angle sensing method, which is achieved via a mixed signal integrated optoelectronic position sensor (iOE-PS) that is bonded directly on the back of an electrothermally-actuated MEMS mirror. The iOE-PS integrates a laser diode, a band-gap reference, a quadrant photo-detector (QPDs), and the QPDs' readout circuits all on a single chip. The iOE-PS has been fabricated in a 180 nm CMOS process. Experimental results show that the iOE-PS has a linear response when the MEMS mirror plate moves vertically between 1.31 mm to 1.50 mm over the iOE-PS chip; the tilt angle can be measured up to at least 5° with a resolution of 0.0067°. The iOE-PS can greatly reduce the size and complexity of MEMS mirrors-enabled systems with integrated closed-loop control capability.

Integrated Optoelectronic Position Sensor for Scanning Micromirrors.

Scanning micromirrors have been used in a wide range of areas, but many of them do not have position sensing built in, which significantly limits their application space. This paper reports an integrated optoelectronic position sensor (iOE-PS) that can measure the linear displacement and tilting angle of electrothermal MEMS (Micro-electromechanical Systems) scanning mirrors. The iOE-PS integrates a laser diode and its driving circuits, a quadrant photo-detector (QPD) and its readout circuits, and a band-gap reference all on a single chip, and it has been fabricated in a standard 0.5 µm CMOS (Complementary Metal Oxide Semiconductor) process. The footprint of the iOE-PS chip is 5 mm × 5 mm. Each quadrant of the QPD has a photosensitive area of 500 µm × 500 µm and the spacing between adjacent quadrants is 500 µm. The iOE-PS chip is simply packaged underneath of an electrothermally-actuated MEMS mirror. Experimental results show that the iOE-PS has a linear response when the MEMS mirror plate moves vertically between 2.0 mm and 3.0 mm over the iOE-PS chip or scans from -5 to +5°. Such MEMS scanning mirrors integrated with the iOE-PS can greatly reduce the complexity and cost of the MEMS mirrors-enabled modules and systems.