This paper proposes a new multiple input multiple output receiver based on the Kalman filtering algorithm. The Kalman filtering algorithm is based on the Gaussian assumption of the input signal. However, the assumptio...
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ISBN:
(纸本)9781467309899
This paper proposes a new multiple input multiple output receiver based on the Kalman filtering algorithm. The Kalman filtering algorithm is based on the Gaussian assumption of the input signal. However, the assumption is not appropriate for the digital communication system which has non-Gaussian input signal. The proposed receiver overcomes the problem by using multiple Kalman filters and its output is obtained using the weighted sum of the outputs of the Kalman filters by the Gaussian sum approximation method to make the data signal approximately Gaussian. Simulation results show that the bit error rate (BER) performance of the proposed receiver is better than the previous Kalman-based receivers and its BER performance is close to the maximum likelihood (ML) receiver with lower computational complexity than the ML receiver.
Fall has become the second major accident injury death *** these death related to fall, most people are more than 65-year-old. So the fall has become one of the most harmful factors to the elderly, and the elderly'...
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Fall has become the second major accident injury death *** these death related to fall, most people are more than 65-year-old. So the fall has become one of the most harmful factors to the elderly, and the elderly's self-care ability is poor, a falling accident can result in the elderly long-term pain and even paralysis. Therefore, in the face of increasingly prominent global aging problem, developing a portable, accurate judgment, real-time data acquisition system for body fall detecting is of great economic and social value. The paper designed a fall data acquisition and analysis system based on low-cost MEMS inertial sensors and multi-node Wi-Fi networks. The system adopts STM32 F407 as primary controller, Single chip SOC chip ESP8266(Espressif inc.) as Wi-Fi RF module, low power consumption chip MPU9150(InvenSense inc.) as motion capture inertial sensor. Firstly, we wear the designed sensor nodes at each joints on the body segments, acquire real-time accurate body motion data by MPU9150, including acceleration, velocity and attitude angle, and develop digital fusion filtering algorithm that output real-time accurate body motion data in the process of movement. Then, this system uses the star network mode. We design the special low power consumption and high throughput communication protocol. The real-time accurate body motion data from various parts of the body are sent to gathering node through ESP8266 Wi-Fi RF node, while gathering node sends personalize commands to each sensor node. Finally, we design the data analysis software platform on PC, which can real-time display, playback, visualization analysis measure, save and read each node motion data waveform. Using the system, in the experiments of simulating human body fall and daily life behavior, after observing and analysing the real-time accurate body motion data wave-form each part from wireless sensor nodes, we find that the system is reliable,stable and high transport precision on wireless real-ti
The detection probability of the radar of the vehicle target tracking system is often less than 1 during driving on urban roads, and the measurement data loss problem may occur. In this paper, the stability of the veh...
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The detection probability of the radar of the vehicle target tracking system is often less than 1 during driving on urban roads, and the measurement data loss problem may occur. In this paper, the stability of the vehicle target tracking system is studied and the sufficient conditions are given for the stability of the mean-square exponent under incomplete measurement conditions. A suboptimal estimation algorithm for vehicle target tracking motion parameters under incomplete measurement conditions is given when the detection probability is known. The simulation results show that the proposed filtering algorithm is effective.
The adaptive estimation procedure of the model reference adaptive system is modified and applied to counting process models. Maximum likelihood estimates constitute a subclass of the adaptive estimators considered. Th...
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The fixed maximum acceleration and maneuvering frequency of current statistical model leads to the divergence of filtering algorithm. In this study, a new model which employs innovation dominated subjection function t...
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ISBN:
(纸本)9781467391054
The fixed maximum acceleration and maneuvering frequency of current statistical model leads to the divergence of filtering algorithm. In this study, a new model which employs innovation dominated subjection function to adaptively adjust maximum acceleration and maneuvering frequency is proposed based on current statistical model. Although the new model has a better performance, a fluctuant phenomenon appears. As far as this problem is concerned, a new filter algorithm which is based on amendatory and adaptively fading kalman filtering is proposed. The results of simulation indicate the effectiveness and coherent of the new model and the new algorithm, and their well performance in maneuvering target tracking.
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