The bias drift of a micro-electro-mechanical systems (MEMS) accelerometer suffers from the 1/f noise and the temperature effect. For massive applications, the bias drift urgently needs to be improved. Conventional methods often cannot address the 1/f noise and temperature effect in one architecture. In this paper, a combined approach on closed-loop architecture modification is proposed to minimize the bias drift. The modulated feedback approach is used to isolate the 1/f noise that exists in the conventional direct feedback approach. Then a common mode signal is created and added into the closed loop on the basis of modulated feedback architecture, to compensate for the temperature drift. With the combined approach, the bias instability is improved to less than 13 μg, and the drift of the Allan variance result is reduced to 17 μg at 100 s of the integration time. The temperature coefficient is reduced from 4.68 to 0.1 mg/°C. The combined approach could be useful for many other closed-loop accelerometers.

As the limited communication spectrum can not meet the demand of the exponential growth of intelligent connected devices in the internet of things(IoT) and typical smart city applications, in this paper, we propose a tractable spectrum sensing method based on Rao detection over non-Gaussian noise, such as generalized Gaussian noise(GGN), Gaussian mixture noise(GMN) and symmetric alpha-stable distribution ( $\text{S}\alpha \text{S}$ ) noise, multi-path fading channels environment to alleviate the issue of spectrum scarcity. In this method, there are unknown parameters in the multi-path fading channels. When the probability density function (P.D.F.) of non-Gaussian noise has a closed-form expression, the spectrum sensing method based on Rao detection is used. Otherwise the P.D.F. for $\text{S}\alpha \text{S}$ noise is estimated firstly by using non-parametric kernel estimation method, which addresses the issue that $\text{S}\alpha \text{S}$ noise has no closed-form P.D.F. expression, and then the performance of spectrum sensing is derived based on the theory of Rao detection in multi-path fading channels over typical smart city applications. Simulation results show that the accuracy of estimated P.D.F. for $\text{S}\alpha \text{S}$ noise and the performance of spectrum sensing under different $\alpha $ values over indoor, outdoor, and vehicle fading channels environment.

At present, there are many technical methods in the field of target detection, but the detection methods are greatly affected by direct-path signals and need technical support such as extracting pure direct-path signals, so they cannot be used under the condition of without-direct-path signals. In this paper, a distributed target detection method is studied under a without-direct-path signals system. In the case of without-direct-path signals, target detection is achieved by the generalized likelihood ratio test (GLRT). At the same time, the input echo signals in target detection need time synchronization, so the impact of time delay should be eliminated. However, the traditional time delay estimation method is realized through coherent processing between echoes, which requires a high signal-to-noise ratio (SNR). Therefore, a time delay estimation method is proposed in this paper. Finally, the experimental results show that the accuracy of target detection by GLRT is improved, and the signal-to-noise ratio is also improved under the condition of without-direct-path signals. Moreover, the accuracy of delay detection is improved.

In this paper, a novel DFT-based pilot-symbol-aided channel estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems is presented. By using transform domain cluster and discriminant analysis, this algorithm can adaptively remove the noise in the channel impulse response (CIR). Then as the noise is removed, the accuracy of channel estimation can be improved dramatically. With a number of simulation experiments, the efficacy of the proposed channel estimation algorithm is validated by the numerical results.

Variable-area capacitor with tuning capability is used to control the drive mode of MEMS gyroscopes for constant-frequency oscillation. The proposed control approach tunes the drive axis natural frequency to a preselected frequency and regulates the amplitude of the drive axis vibration. The algorithm mainly implemented using a field-programmable-gate-array (FPGA) is tested on a MEMS gyroscope operating at atmospheric pressure which is of our own manufacture. First measurement results of the novel digital prototype show the amplitude variance of the detected signal of drive axis is 26ppm in an hour while the phase variance is 45ppm.