Multi-tier dynamic sampling weak RF signal estimation theory

A method for implementing weak radio frequency (RF) signal amplification with high-gain based on injection-locked dual optoelectronic oscillator (OEO) is proposed and experimentally demonstrated. By keeping the gain of cascade dual OEOs (CDOEOs) just a little below threshold, the proposed system will have a great advantage in amplifying weak RF signals, which is equal to the potential oscillation mode. The experimental results show that the sensitivity of the system can reach as low as -100 dBm, and the maximum gain is 46 dB. The phase noise performance of the weak RF signal after amplification is also investigated.

In this paper, a new method is proposed to identify the frequency and amplitude of weak signals by using a non-smooth system. The variable scale limit system of smooth and discontinuous (SD) oscillator without considering the phase is adopted as the identification system. By using the non-smooth stochastic subharmonic-like Melnikov method, an analytical expression of chaotic threshold under Gaussian white noise is given. Based on the phase diagram and Poincaré section diagram, the influence of noise intensity on the recognition system is studied. According to the non-smooth variable scale-convex-peak frequency identification method, the frequency of the signal to be detected can be accurately identified. Using the numerical simulation, the amplitude of the signal to be measured is identified according to the amplitude bifurcation diagram of the reference signal. The optimal value range of the parameters of the identification system is determined. Through an example of early fault of wheelset bearing of high-speed train, the frequency and amplitude of the early weak fault signal can be identified and the fault location can be determined, which verifies the effectiveness of the above method. The results show that the non-smooth system can identify the frequency and amplitude of the weak signal submerged in strong noise, and it has a wider application and higher accuracy than the continuous system.

Instantaneous frequency estimation for wheelset bearings weak fault signals using second-order synchrosqueezing S-transform with optimally weighted sliding window

Strong interference will affect direction of arrival (DOA) estimation of weak desired signal and even cause DOA estimation failure. This paper investigates the weak signal DOA estimation for an antenna array under strong interference signals, and proposed a novel DOA estimation method for strong interference source suppression and weighted l1-norm sparse representation. A parallel adaptive beamforming algorithm based on power inversion is used to suppress strong interference and form new array data. To reduce spurious peaks in the spectrum under strong interference, a weighted matrix is determined by the optimized subspace algorithm for the subspace projection. Then, the DOA estimation, which is calculated by weighted l1-norm sparse representation, is formed by the weighted matrix and new array data. In this paper, the superiority of the proposed algorithm is illustrated by an example of unmanned aerial vehicle (UAV) video signal DOA estimation under strong interference signals. The simulated results of an orthogonal frequency division multiplexing signal indicate that the proposed algorithm shows merits of fewer snapshots, a sharper main lobe, a lower average noise spectrum value, higher DOA estimation accuracy and success rate. For validation, an outdoor experiment was conducted which demonstrated that the proposed algorithm is superior to other algorithms and can be used for DOA estimation of UAV video signals under strong WiFi interference. Both the simulations and experiments verify that the proposed algorithm can effectively suppress strong interference and achieve better DOA estimation performance for weak signals.

The strategy for any organization can be set from the outside in, according to the school of positioning; according to this, the supply of products and / or services has to adapt to changes that occur in the environment. According to the movement schools of thought, a strategy can be set from the inside out in which case the organization influences the surroundings with its offer of products and / or services. Given the importance of weak signals in the definition of the strategy, the research goal is to treat the issue of pre-emptive strategy in the context of strategic decision making. An answer is provided for the following questions: (1) What is a weak signal? (2) How to tell if it is anticipative, relevant and useful for the decision makers? and (3) How to turn a weak signal in an effective anticipative clue? Based on investigator's experience in strategic consulting for small, medium and large companies, the weak and strong signals are explained and a set of criteria which are representative of their identification is presented.

Ultrasonic testing of thick-walled polyethylene pipes is challenged by energy loss, resulting in weak echo signals from deep defects. To enhance the detection of these weak signals, a time-frequency energy concentration method is presented. The fractional adaptive superlet transform combines multiple wavelet transform results with distinct bandwidths through geometric averaging, providing superior time-frequency analysis capabilities than single wavelet transforms. However, its time-frequency representation exhibits the issue of instantaneous frequency deviation. The proposed method addresses the issue via instantaneous frequency-embedding, leading to improved accuracy in instantaneous frequency estimation. Numerical signal analysis reveals higher accuracy in instantaneous frequency estimation using this method, compared to other time-frequency processing methods. When applied to detecting deep defects in thick-walled polyethylene pipes, the method shows an 18.9% increase in weak signal enhancement capability compared to the continuous wavelet transform. Finally, the results demonstrate the method’s accuracy in clarifying instantaneous frequency changes and enhancing instantaneous amplitudes of weak signals, offering a promising approach for the detection of deep defects in thick-walled polyethylene pipes.

This study aims to propose methods for identifying entrepreneurial discovery processes with weak/strong signals of technological changes and incorporating technology foresight in the design and planning of the Smart Specialization Strategy (S3). For this purpose, we first analyse patent abstracts from 2000 to 2009, obtained from the European Patent Office and use a keyword-based text mining approach to collect weak and strong technology signals; the word2vec algorithm is also employed to group weak signal keywords. We then utilize Correlation Explanation (CorEx) topic modelling to link technology weak/strong signals to invention activities for the period 2010-2018 and use the ANOVA statistical method to examine the relationship between technology weak/strong signals and patent values. The results suggest that patents related to weak rather than strong signals are more likely to be high-impact innovations and to serve as a basis for future technological developments. Furthermore, we use latent Dirichlet allocation (LDA) topic modelling to analyse patent activities related to weak/strong technology signals and compute regional topic weights. Finally, we present implications of the research.

Instantaneous frequency measurement (IFM) of microwave signals using photonic methods provides a novel and efficient approach for fast and broadband radio frequency (RF) signal analysis. Here, we propose and experimentally demonstrate a photonic-assisted IFM method utilizing a few-mode fiber-based microwave photonic technique. By offset splicing the few-mode fiber (FMF) with a single mode fiber, both LP01 and LP11 modes can be excited, which is used to develop a microwave photonic filter (MPF). A detailed analysis of the FMF as the true time delay line is presented. An amplitude comparison function (ACF) that is the ratio of frequency response traces of an MPF pair is established, which is used to determine the unknown microwave frequency instantaneously. The proof-of-concept experiment demonstrates a frequency measurement range of 0.5 GHz to 17.5 GHz and a measurement accuracy of ±0.2 GHz in most of the frequency points. The proposed system has the merits of simplicity, cost-effectiveness, compactness and robustness.

Blocking and desensitization are among the nonlinear effects that result when bipolar and MOSFET differential amplifiers are operating in a multisignal electromagnetic interference (EMI) environment. When a weak desired signal is processed along with a strong undesired interferer signal, the weak signal may experience a reduced gain; that is, desensitization. If the amplitude of the undesired signal is sufficiently large, the gain of the desired signal may drop to zero; that is, blocking. Using a Fourier-series model for the differential input-voltage/output-current characteristics, closed-form expressions for predicting the desensitization in bipolar and MOSFET differential amplifiers are presented. These expressions are valid over a wide range of desired and blocking interfering signals amplitudes.

Duffing oscillator is one of the classic nonlinear system that can generate chaotic motion. Given the sensitivity to regular signals but immunity to noise of its chaotic attractor, the Duffing oscillator can be used for weak signal detection. Our recent study on other attractors of Duffing oscillator showed that the state transition of its steady attractor not only has the two major advantages of the chaotic attractor, but also has a specific advantage, that is, it has no transitional zone. In the nearby area of the steady attractor, noise may even cause stochastic resonance, which significantly increases the output signaltonoise ratio. For the first time, we present a measure function for the state transition of the steady attractor of Duffing oscillator and then proposed a novel estimation method for weak sinusoidal signal buried in strong noise. Simulations were conducted to show the efficiency of the proposed method, and results indicate that the proposed method can achieve estimation of amplitude and frequency for sinusoidal signal. Moreover, the proposed method has a higher estimation accuracy and a stronger anti-noise performance than the classical spectrum and maximum likelihood estimation method.

Using numerical simulations, we show that two coupled qubits can amplify a weak signal about hundredfold. This can be achieved if the two qubits are biased simultaneously by this weak signal and a strong pump signal, both of which having frequencies close to the inter-level transitions in the system. The weak signal strongly affects the spectrum generated by the strong pumping drive by producing and controlling mixed harmonics with amplitudes of the order of the main harmonic of the strong drive. We show that the amplification is robust with respect to noise, with an intensity of the order of the weak signal. When deviating from the optimal regime (corresponding to strong qubit coupling and a weak-signal frequency equal to the inter-level transition frequency) the proposed amplifier becomes less efficient, but it can still considerably enhance a weak signal (by several tens). We therefore propose to use coupled qubits as a combined parametric amplifier and frequency shifter.

Single User, Transmission Control Protocol Downstream Throughput (TCPDST) probability models in an IEEE802.11b WLAN have been developed, validated and evaluated for performance. Measurement of single user TCPDST were taken using Tamosoft throughput test while that of signal to noise ratio (SNR) were taken using inSSIDer 2.1. The Tamosoft throughput tests were conducted using different quality of service (QoS) traffic. These QoS traffic (which were sent through an infrastructure based network) correspond to different wireless multimedia tags. Measurements were taken in free space, small offices and open corridor environments. By assuming a normal distribution, single user TCPDST Cumulative distribution function (CDF) probability models were developed for different signal categories namely: (i) all the SNR considered, (ii) strong signals only, (iii) grey signals only and (iv) weak signals only. The models were validated and their performances evaluated using root mean square (RMS) errors. RMS errors were computed by comparing model predicted values with validation data. The RMS errors for single user CDF all signals model was 0.1466%. RMS errors for strong signals models, grey signals model and weak signals model respectively were 0.1466%, 0.6756% and 0.1233% indicating acceptable performances. All signals, strong signals, grey signals and weak signals CDF probability models predicted probabilities of obtaining TCPDST values greater than 5Mbps as 74.79%, 90.55%, 13.00% and 4.77% respectively while probabilities of obtaining TCPDST values less than 2Mbps were predicted as 4.91%, 0.00%, 18.98% and 52.41% respectively. These probability models will provide additional useful information needed to design efficient distributed data networks.
 Keywords: Throughput, TCP, WLAN, probability models

In this paper, sub-Nyquist sampling technology and fast discrete Fourier transform algorithm are applied to the design of instantaneous frequency measurement (IFM) receiver and the structure of the digital IFM receiver based on sub-Nyquist sampling is given. The proposed digital IFM receiver, which is designed with sub-Nyquist sampling technology and fast discrete Fourier transform algorithm, not only reduces the A/D converter and the back-end digital processor requirements and increases the input signal instantaneous bandwidth, but also takes on better real-time quality. Meanwhile, the IFM receiver frequency measurement performance is analyzed in detail and reasons that using sub-Nyquist sampling technology in the digital IFM receiver designed with given structure will not affect its frequency measurement performance are indicated in this paper. In addition, through taking advantage of the two-version signals' amplitude information, the digital IFM receiver designed with the given IFM receiver structure demonstrates better simultaneous signals processing performance. Finally, a simulation with two linear frequency modulation signals coming simultaneously is done and the simulation result verifies the frequency measurement and simultaneous signals processing performance of the IFM receiver designed with given structure. Besides, the simulation result also shows the correctness and feasibility of related design theory and methods.

In small target detection under strong sea clutter or impact signal detection under machinery fault diagnosis, a weak sinusoidal signal with random amplitude is often contaminated by heavier chaotic noise, and the target information is difficult to detect. Traditional solutions, such as neural networks or stochastic resonance, can not effectively extract heteroscedasticity of data, which leads to weak signals not being detected. To overcome these limitations and improve the detection efficiency, an empirical likelihood ratio statistical method for detecting weak sinusoidal signals with random amplitude under strong chaotic interference is proposed. First, based on the reconstruction in the phase space of the 1‐D observed time series signal with embedding dimension and time delay, the presented method obtains a multivariate special temporal series as an input. Subsequently, the chaotic single index (CSI) statistical model is established for single‐step prediction, and it can be estimated by the nonparametric locally linear algorithm for minimizing the mean squares error. Finally, the empirical likelihood ratio statistical method is applied to detect weak sinusoidal signals with random amplitude. Simulated data and real data experiment results show that the proposed CSI model can better capture the weak target signal and detect effectively weak target signal under the chaotic interference.

In recent radio-communication applications, receivers may be jammed by high power unwanted signals. In this case, the received signal can be considered as the sum of a strong unwanted signal and a very weak target signal. Even though the two signals don't overlap in the frequency domain, the processing of the weak signal becomes very hard as it can be vanished at the output of the Analog Digital Converter. To avoid this scenario, many nonlinear circuits are proposed in the literature. Our study focuses on the separation of a weak and a very strong signals which are wideband signals and they are very close in the frequency domain. Several circuits have been implemented and simulated. The proposed circuit diagram is also presented. Finally, simulations are presented and discussed.