Digital demodulators for analog signals: comparative analysis and simulation

The paper examines digital demodulators for two commonly used techniques of modulating analog signals: amplitude modulation (AM) and frequency modulation (FM). The described demodulators can be used to perform the radio monitoring of narrowband signal ranges including FM broadcasting stations as well as license-free CB, LPD, PMR bands. The demodulators considered in this work are intended for programmable devices with limited memory and computing resources, for example, for STM32F407 microcontrollers and similar ones. The paper presents the analysis and simulation of demodulators for AM signals, FM signals with low modulation indices and for FM signals without restriction on the modulation indices. In addition, the authors demonstrate how to demodulate the phase-modulation signal using a quadrature demodulator. The number of operations that are available for demodulation is limited by IF multiplication and filtering. The simulation of the analyzed demodulation algorithms was carried out in the Scilab environment which is a free analogue of the Matlab environment. To explain the principle of operation of demodulators, block diagrams and graphs of signals in time and frequency domains are shown.

A Tracking Method in FM Broadcast-based Passive Radar Systems

Passive radars are popular because without the expensive, high-power-rated RF components, they are much cheaper than the active ones, nevertheless, they are much harder to detect from their electromagnetic emission. Passive radars produce so-called RV matrices in an intermediate signal processing step. Although accurate RV matrices are found in DVBT-based passive radars, the characteristics of the FM signals are not always suitable for this purpose. In those situations, further signal processing causes false alarms and unreliable plots, misleads the tracker, and consumes power for processing unnecessarily, which matters in portable setups. Passive radars also come with the advantage of a possible MIMO setup, when multiple signal sources (broadcast services for example) are reflected by multiple targets to the receiver unit. One common case is the stealth aircraft’s which form is designed to reflect the radar signal away from the active radar, but it could also reflect the signals of the available broadcast channels. Only one of these reflected signals could reveal the position of the target.

Effect of Modulated Signals on Vibrational Resonance in a Harmonically Trapped Potential System

We consider a harmonically trapped potential system driven by modulated signals with two widely different frequencies ω and Ω, where Ω >> ω. The forms of modulated signals are amplitude modulated (AM) and frequency-modulated (FM) signals. An amplitude-modulated external signal is consisting of a low-frequency (ω) component and two high-frequencies (Ω + ω) and (Ω − ω) whereas the frequency modulated signal consisting of the frequency components such as f sinωt cos(g cosΩt) and f sin(g cosΩt) cosωt. Depending upon the values of the parameters in the potential function, an odd number of potential wells of different depths can be generated. We numerically investigate the effect of these modulated signals on vibrational resonance (VR) in single-well, three-well, five-well and seven-well potentials. Different from traditional VR theory in the present paper, the enhancement of VR is made by the amplitudes of the AM and FM signals. We show the enhanced response amplitude (Q) at the low-frequency ω, showing the greater number of resonance peaks and non-decay response amplitude on the response amplitude curve due to the modulated signals in all the potential wells. Furthermore, the response amplitude of the system driven by the AM signal exhibits hysteresis and a jump phenomenon. Such behavior of Q is not observed in the system driven by the FM signal.

Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology

Fetal movement (FM) is an essential physiological parameter to determine the health status of the fetus. To address the problems of harrowing FM signal extraction and the low recognition rate of traditional machine learning classifiers in FM signal detection, this paper develops a passive FM signal detection system based on intelligent sensing technology. FM signals are obtained from the abdomen of the pregnant woman by using accelerometers. The FM signals are extracted and identified according to the clinical nature of the features hidden in the amplitude and waveform of the FM signals that fluctuate in duration. The system consists of four main stages: (i) FM signal preprocessing, (ii) maternal artifact signal preidentification, (iii) FM signal identification, and (iv) FM classification. Firstly, Kalman filtering is used to reconstruct the FM signal in a continuous low-amplitude noise background. Secondly, the maternal artifact signal is identified using an amplitude threshold algorithm. Then, an innovative dictionary learning algorithm is used to construct a dictionary of FM features, and orthogonal matching pursuit and adaptive filtering algorithms are used to identify the FM signals, respectively. Finally, mask fusion classification is performed based on the multiaxis recognition results. Experiments are conducted to evaluate the performance of the proposed FM detection system using publicly available and self-built accelerated FM datasets. The classification results showed that the orthogonal matching pursuit algorithm was more effective than the adaptive filtering algorithm in identifying FM signals, with a positive prediction value of 89.74%. The proposed FM detection system has great potential and promise for wearable FM health monitoring.

RANSAC algorithm for instantaneous frequency estimation and reconstruction of frequency-modulated undersampled signals

Frequency modulated (FM) signals sampled below the Nyquist rate or with missing samples (nowadays part of wider compressive sensing (CS) framework) are considered. Recently proposed matching pursuit and greedy techniques are inefficient for signals with several phase parameters since they require a search over multidimensional space. An alternative is proposed here based on the random samples consensus algorithm (RANSAC) applied to the instantaneous frequency (IF) estimates obtained from the time-frequency (TF) representation of recordings (undersampled or signal with missing samples). The O’Shea refinement strategy is employed to refine results. The proposed technique is tested against third- and fifth-order polynomial phase signals (PPS) and also for signals corrupted by noise.

A Frequency Modulation Fingerprint-Based Positioning Algorithm for Indoor Mobile Localization of Photoelectric Modules

Optoelectronic modules have a wide range of applications in the field of wireless communication. However, the function of mobile localization has not been realized in optoelectronic modules. In this paper, an indoor positioning algorithm, which was based on frequency modulation (FM) signals, was realized in optoelectronic modules. Firstly, FM monitoring receiver DB4004 was used to collect FM signals; Secondly, FM signals were preprocessed and analyzed to build a FM dataset. Finally, weighted centroid k-nearest neighbors (WC-KNN) precise positioning algorithm was proposed to obtain the position information of the photoelectric module. Experimental results showed that the median location error of the WC-KNN algorithm can reach 0.8 m and additional hardware equipment was not required. The research results provided the feasibility for the practical application of equipment based on optoelectronic devices in various fields.