An apparatus includes a sensor module configured for receiving sensed information indicative of a sensed signal. The sensed signal includes a source signal component and a source noise component. The apparatus also includes a reference module configured for reference information indicative of a reference signal. The reference signal also includes a reference noise component. The apparatus also includes a filter module configured as a fixed lag Kalman smoother. The filter module is configured for adaptively filtering the reference signal to generate an estimate of the source noise component. The apparatus also includes a processing module configured for calculating an output signal based on the sensed signal and the estimate of the source noise component. The apparatus also includes an interface module configured for transmitting an indication of the output signal. The filter module is further configured for, based on the output signal, tuning the Kalman smoother.

A novel and useful discrete time IIR bandpass filter is disclosed that takes advantage of clock phase reuse thereby leading to significant improvements in filtering, especially stop band rejection in comparison to prior art filters. The bandpass filter of the present invention achieves improved filtering performance without adding any additional clock phases to the circuit. In particular, reuse of the already existing clock phases increases the order and performance of the filter. The invention exploits reuse of the clock phases to provide higher order filtering along with a discrete time IIR filter design which is capable of operating at high frequency. Consequently, much better filtering is achieved and the quality factor of the filter is improved leading to sharper transition bands especially for close-in band blockers in modern 4G/5G receivers.

A fuzzy entropy based noisy signal processing method and an iterative singular spectrum analysis (SSA) soft threshold de-noising method are disclosed. The method employs FuzzyEn, which is used to quantify the system complexity in chaos theory, to characterize a noise floor, which provides a more effective path for processing of noisy signal; its fuzzy entropy spectrum based iterative singular spectrum analysis soft threshold (SSA-IST) de-noising method outperforms the conventional truncated singular spectrum, wavelet transform and empirical mode decomposition de-noising approaches in de-noising performance.

An apparatus includes a sensor module configured for receiving sensed information indicative of a sensed signal. The sensed signal includes a source signal component and a source noise component. The apparatus also includes a reference module configured for reference information indicative of a reference signal. The reference signal also includes a reference noise component. The apparatus also includes a filter module configured as a fixed lag Kalman smoother. The filter module is configured for adaptively filtering the reference signal to generate an estimate of the source noise component. The apparatus also includes a processing module configured for calculating an output signal based on the sensed signal and the estimate of the source noise component. The apparatus also includes an interface module configured for transmitting an indication of the output signal. The filter module is further configured for, based on the output signal, tuning the Kalman smoother.

A fuzzy entropy based noisy signal processing method and an iterative singular spectrum analysis (SSA) soft threshold de-noising method are disclosed. The method employs FuzzyEn, which is used to quantify the system complexity in chaos theory, to characterize a noise floor, which provides a more effective path for processing of noisy signal; its fuzzy entropy spectrum based iterative singular spectrum analysis soft threshold (SSA-IST) de-noising method outperforms the conventional truncated singular spectrum, wavelet transform and empirical mode decomposition de-noising approaches in de-noising performance.

The present disclosure provides a device and method for wireless communication and a communication terminal. The device comprises: a spatial filtering unit, configured to perform spatial filtering on a signal received by each antenna in a receiving antenna array, and combine filtered signals, wherein all coefficients adopted by spatial filtering are configured to reduce an equivalent channel time-variant degree of a combined signal.

This application relates to methods and apparatus for controlling slew-rate of components for outputting an analogue output signal. Described is a signal processing circuit having a forward signal path for receiving an input signal and outputting an analogue output signal. The signal processing circuit has a first component located in said forward signal path for outputting the analogue output signal. A predictor is configured to predict a required slew-rate for the first component based on the input signal and a controller is configured to controllably vary an output slew-rate limit of the first component based on the prediction of required slew-rate.