Secondary Path Research Articles

Front-End Development for Radar Applications: A Focus on 24 GHz Transmitter Design.

The proliferation of radar technology has given rise to a growing demand for advanced, high-performance transmitter front-ends operating in the 24 GHz frequency band. This paper presents a design analysis of a radio frequency (RF) transmitter (TX) front-end operated at a 24 GHz frequency and designed using 65 nm complementary metal-oxide-semiconductor (CMOS) technology for radar applications. The proposed TX front-end design includes the integration of an up-conversion mixer and power amplifier (PA). The up-conversion mixer is a Gilbert cell-based design that translates the 2.4 GHz intermediate frequency (IF) signal and 21.6 GHz local oscillator (LO) signal to the 24 GHz RF output signal. The mixer is designed with a novel technique that includes a duplex transconductance path (DTP) for enhancing the mixer's linearity. The DTP of the mixer includes a primary transconductance path (PTP) and a secondary transconductance path (STP). The PTP incorporates a common source (CS) amplifier, while the STP incorporates an improved cross-quad transconductor (ICQT). The integrated PA in the TX front-end is a class AB tunable two-stage PA that can be tuned with the help of varactors as a synchronous mode to increase the PA bandwidth or stagger mode to obtain a high gain. The PA is tuned to 24 GHz as a synchronous mode PA for the TX front-end operation. The proposed TX front-end showed an excellent output power of 11.7 dBm and dissipated 7.5 mW from a 1.2 V supply. In addition, the TX front-end achieved a power-added efficiency (PAE) of 47% and 1 dB compression point (OP1dB) of 10.5 dBm. In this case, the output power is 10.5 dBm higher than the linear portion of the response. The methodologies presented herein have the potential to advance the state of the art in 24 GHz radar technology, fostering innovations in fields such as autonomous vehicles, industrial automation, and remote sensing.

An adaptive algorithm for acoustic feedback compensation and secondary path identification of an active noise control system.

An adaptive variable step-size algorithm is proposed in this paper to address the impact of the real-time acoustic feedback and the real-time secondary path identification on the overall noise reduction performance of an active noise control system. An automated adjustment weight factor is introduced in the algorithm to accelerate the convergence of the acoustic feedback path as well as the secondary path identification, and to prevent possible system divergence. It is shown in this study that the proposed algorithm can resolve the trade-off between a fast convergence and a low misalignment of the virtual and the actual control paths typically found in conventional algorithms. An optimized control structure is also proposed in the study by enabling an adaptive gain adjustment based on the output of the auxiliary filter to enhance the practicality of the control system. The effectiveness of the algorithm is tested using two simulated multi-component signals and a broadband noise signal, and the results confirm that the proposed algorithm can achieve a good noise reduction with only a few iterations.

Variation of the secondary acoustic paths from loudspeakers at the seat shoulder in an automobile cabin

The secondary path is essential in the design of adaptive active noise control systems, and it varies with several factors in practical implementation. In this paper, the acoustic responses from secondary loudspeakers installed at the shoulder of the front passenger seat to the error microphones at the passenger's ears in an automobile cabin are measured, when the factors including the passenger movement, the seat posture and the passenger number change. The upper limit frequency where the phase variation is within 90 deg. compared to the reference position is analyzed. The results show that the state of both the passenger head and the seat back determines the lowest upper limit frequency among all the factors under investigation. If multiple factors change at the same time, the upper limit frequency will be further reduced.

A robust auxiliary noise power scheduling strategy for online secondary path modeling in active noise control systems

Auxiliary noise injected in active noise control (ANC) systems for online secondary path modeling (SPM) contributes to the residual noise, and thus deteriorates the noise control performance of ANC systems. Moreover, sudden changes in secondary path may lead to easy divergence of the existing online SPM methods. This paper improves the existing methods from three aspects to further mitigate these problems: 1) the noise power is varied on the basis of the convergence status of SPM filter so that large-power noise is injected at the start or when secondary path changes and the power is reduced as the filter converges. 2) a novel ON/OFF controlling strategy is used in the method so that the noise injection is suspended at the optimum point when the modeling accuracy is sufficient. 3) continuous check for error outliers is applied in the method to detect changes in secondary path so that the injection of noise can be reactivated in the case of path perturbation. Comparative simulations show the effectiveness of the improved method both in convergence and noise reduction performance. In addition, the improved method is robust against sudden changes in secondary path.

Convex combination of online secondary path modeling and improved cuckoo search for active noise control

This paper proposes an active noise control (ANC) algorithm based on convex combination of the online secondary path modeling (OSPM) and improved cuckoo search (ICS) algorithms. The ICS algorithm does not request the secondary path model to optimize the control filter. Therefore, the ICS algorithm is theoretically not affected by a varying secondary path, but its convergence is very slow. In contrast, the OSPM algorithm achieves faster convergence but is vulnerable to a sudden change in the secondary path. The proposed ANC algorithm combines the merits of both the OSPM and ICS algorithms by convex combination. Simulation results show that the proposed ANC algorithm can achieve better performance than the conventional ANC algorithm especially when the secondary path changes drastically.

A Computation-efficient Online Secondary Path Modeling Technique for Modified FXLMS Algorithm

This paper proposes an online secondary path modelling (SPM) technique to improve the performance of the modified filtered reference Least Mean Square (FXLMS) algorithm. It can effectively respond to a time-varying secondary path, which refers to the path from a secondary source to an error sensor. Unlike traditional methods, the proposed approach switches modes between adaptive ANC and online SPM, eliminating the use of destabilizing components such as auxiliary white noise or additional filters, which can negatively impact the complexity, stability, and noise reduction performance of the ANC system. The system operates in adaptive ANC mode until divergence is detected due to secondary path changes. At this moment, it switches to SPM mode until the path is remodeled and then returns to ANC mode. Furthermore, numerical simulations in the paper demonstrate that the proposed online technique effectively copes with the secondary path variations.

Frequency domain online secondary path modelling for active noise control without auxiliary noise

The identification of the secondary path transfer function is of vital importance for active noise control (ANC) system. Online secondary path modeling method is preferable to offline modeling due to its tracking capability in time-varying acoustic environments. However, the auxiliary noise of online modeling mixed with the residual error signal diminishes the noise control performance. In this paper, we propose a frequency domain online secondary path modelling algorithm based on our previous work which achieves simultaneous acoustic paths modeling and noise control without auxiliary noise. The uniqueness of the solution is theoretically analyzed, and the computational burden is shown significantly lower than the previously proposed method. Simulations are conducted to verify that the proposed algorithm also has a better convergence behavior.

A Modified Simultaneous Perturbation Stochastic Approximation Algorithm for Active Noise Control

Secondary path modeling is an important step in active noise control. Keeping the active noise control system stable, when the secondary path changes over time, is a widely studied problem. Using the simultaneous perturbation stochastic approximation algorithm, can update the control filter without secondary path modeling, avoiding the problem of increased noise caused by secondary path modeling. In this paper a variant of the simultaneous perturbation stochastic approximation algorithm is proposed, which incorporates historical coefficients of the control filter, to correct the direction of the gradient calculation. Based on an experimental setup with a realistic transfer path, this paper demonstrates that the proposed algorithm has superior performance in terms of convergence speed and noise reduction amount, than the general simultaneous perturbation stochastic approximation algorithm.

Study of filtered-x least mean square algorithm and its different variants for active noise control of complex real-world noises

In various simulation studies for active noise control, noises are taken as tonal, multi-tonal, random broadband, and simulated chaotic noise. In reality, noises from real machines combine all these types of noises and background noise. This paper presents a comparative analysis of filtered-x least means square, filtered-e least mean square, filtered-x recursive least mean square, filtered-u least mean square, and leaky FxLMS for active noise control. Experimentally recorded noises of Band saw, CNC, Compressor, and welding processes are utilized for the analysis. The analysis is carried out for (a) linear primary and linear secondary paths, and (b) nonlinear primary and linear secondary paths. Their performance is evaluated based on steady-state residual noise and convergence time for various noises.

Simultaneous perturbation algorithm for active noise control with an asynchronous wireless microphone

This paper investigates the issues on the use of an asynchronous wireless microphone in an active noise control (ANC) system. In a previous work, the jitter effect of the wireless error microphone is addressed by adopting the simultaneous variable perturbation (SVP) algorithm to update the control filter of the ANC system. However, another critical issue has been overlooked. The wireless microphone can have a different sampling rate from that of the ANC controller, because the analog-to-digital convertors for the ANC controller and the wireless microphone are triggered by separate crystal oscillators. This may deteriorate the convergence performance of the ANC system. In this paper, the simulation results demonstrate that both the SVP and online secondary path modeling (OSPM) algorithms are not affected by the sampling rate of the wireless error microphone, when the sampling rate difference is less than 0.01%. However, the sampling rate difference of the wireless reference microphone can lead to about 7 dB and 5 dB degradations in the noise reduction performance of the SVP and OSPM algorithms, respectively.

Study of machine learning based algorithms for active noise control of machinary noise

Machine learning is becoming a part of every field. After the discovery of deep learning, its application in various fields are increasing day by day. This paper presents a study of three machine learning based active noise control algorithms to reduce machinary noise. Three algorithms studied in this work are: filtered-x backpropagation neural network (FxBPNN), radial basis function (RBF) and deep recurrent neural network (DRNN). Experimentally recorded noises of band saw, CNC and compressor are used in the simulation study. Two cases of primary path are considered: a) linear path and b) nonlinear path, along with a linear secondary path. Performance of the three algorithms are evaluated based on steady state residual noise.

An analysis of a feedback active noise control system using the remote microphone technique

This paper examines strategies in virtual sensing feedback active noise control systems and compares the noise reduction performance of three strategies: using physical microphones for direct feedback control, using virtual microphones for direct feedback control, and using physical microphones for indirect virtual feedback control. For a primary sound field with incident waves in a single direction, simulation results indicate that employing a physical microphone for indirect virtual feedback control consistently yields superior noise reduction performance than that using a physical microphone for direct feedback control. However, if the causal requirement between physical and virtual microphones is not satisfied, the increase in the equivalent secondary path delay deteriorates noise reduction performance while utilizing physical microphones for indirect virtual feedback control. For a primary sound field containing waves from multiple directions, the simulation results show that the noise reduction performance is affected by both the coherence and causality between the physical and virtual microphone signals.

Adaptive analog and digital active noise control

Digital adaptive controllers are widely used for active noise control. The secondary path delay, including the anti-aliasing, sampling, and reconstruction effects, must be shorter than that in the primary path to maintain good broadband performance. To eliminate the added delay of the sampling, a mixed analog and digital adaptive feedforward controller is developed. The analog controller is based on a state-filtered adaptive linear combiner, while the digital one uses an adaptive finite-impulse-response filter. The adaptation of the analog controller is derived based on a sampled version of the normalized projection algorithm, while the digital controller adaptation is based on the filtered-reference least-mean-squares algorithm. Two configurations are considered for the analog combiner. One aims to minimize the electrical noise by introducing individual state filters, all driven by the same reference signal. The other gives a superior bandwidth by driving each component filter with the previous filter output. The performance of the proposed controller is assessed and compared with separate analog or digital controllers. The results highlight that the suggested controller obtains significant attenuation levels close to the causality limit using the analog controller but also allows long impulse responses using the sampled controller.

Enhancing active noise control of road noise using deep neural network to update secondary path estimate in real time

The performance of active noise control (ANC) is significantly influenced by the accuracy of the secondary path estimate. In the case of a vehicle, dynamic environments, which constantly change the secondary path, can degrade the ANC performance. To address this issue, we proposed a deep learning-based method to continuously update the secondary path estimate in real time. The sensitivity of secondary paths to various conditions was analyzed, with vertical, horizontal, roll, and yaw conditions identified as primary factors; subsequently, a dataset corresponding to these conditions was created. Estimation models were formulated using Delaunay triangulation-based interpolation and three deep neural network (DNN)-based methods which incorporated diverse representations of the secondary path domain. All the methods precisely estimated the secondary path under different conditions. Notably, DNN-based methods required significantly less data storage than the interpolation method, with the principal component analysis (PCA)-based approach performing the best, achieving a 98.5 % reduction. Using this method, updating the secondary path estimate in real time led to reductions of 10.2 and 17.2 dB in road noise and 500–1,000 Hz random noise, respectively. The improvement was notable for both types of noises, indicating that the suggested method can expand the frequency range of ANC.

Perencanaan Lanskap Daya Tarik Wisata Pantai Melasti, Desa Ungasan, Kecamatan Kuta Selatan, Badung

Melasti Beach Tourism Attractions Landscape Planning, Ungasan Village, South Kuta District, Badung. Melasti Beach is one of the natural tourist attractions in Badung Regency which is used for tourism activities and socio-religious activities. The increasing number of tourism activities in Melasti Beach demands that landscape arrangement must takes into account the biophysical and socio-religious aspects in it. The purpose of this research is to make a planning of the Melasti Beach landscape that is harmonious between biophysical and socio-religious aspects. The method used is a survey with data collection techniques through observation, interviews and literature study. The research stages in this planning consist of preparation, inventory, analysis, synthesis and landscape planning. Analysis and synthesis of data was carried out spatially using an assessment level with the help of a Geographic Information System (GIS) to obtain three spatial zones that refer to the Tri Mandala concept, consisting of zones for tourism use, buffers and religious sacred ones. The circulation plan is divided into primary and secondary circulation paths with the circulation designation for Visitors with Religious Interests (PDKK) and tourists on each circulation path. The main activity plan consists of tourism activities and religious activities.

Experimental Study of Radiated Emission Due to Secondary Common-Mode Current in Buck Converters

High switching frequency and fast switching speed of wide bandgap power semiconductor device-based power converters may increase radiated emissions more than conventional silicon-power device-based power converters. Based on the background, this article experimentally investigates radiated emission sources in buck converters. The experimental system constructed in this article has two common-mode noise paths, the primary common-mode and the secondary common-mode. First, this article outlines that a secondary common-mode voltage source is caused by a primary common-mode current and an imbalance of power supply side impedances, and the secondary common-mode current path acts as a monopole antenna. The common-mode currents and the radiated emissions are measured in an anechoic chamber when the common-mode inductor for each mode is connected. The measurement results show that the primary common-mode current generates the secondary common-mode noise, and the secondary common-mode noise is the dominant source of radiated emission in the experimental system.

Updating the secondary path using deep learning models to enhance the performance of active road noise control

This research investigates the importance of accurately updating the secondary path estimation to enhance the performance of active road noise control (ARNC), particularly in response to variable conditions within a vehicle. The study analyzes the impact of four spatial conditions on the secondary path and introduces prediction models using Delaunay triangulation-based interpolation and deep learning methods, using multi-layer perceptron (MLP) networks. MLP networks were constructed based on the representation domain chosen for the secondary path. The deep learning methods demonstrated higher prediction accuracy and smaller data storage requirements compared to the interpolation method. Among these deep learning methods, the approach that stood out as the best performer was representing the secondary path through principal component analysis (PCA) and learning the weight of each basis vector. Through this technique, it was experimentally confirmed that the performance of ARNC under variable conditions in the vehicle improved through the accurate update of real-time secondary path estimation. These advantages are particularly prominent when dealing with high frequencies, which are likely to be the foundational technology for the frequency expansion of ARNC.

Simulation of active vibration control of a moving stage

Active vibration control (AVC) has gained considerable interest due to its inherent adaptability and cost-effectiveness, with its ability to suppress vibrations in the controlled system across various applications. Existing studies focus on the AVC of non-moving systems and usually assume that the vibration signal to be controlled is known and can serve as the ideal reference signal in feedforward control systems. However, in many practical applications, the ideal reference signal is not accessible, and a sensor must be used to detect the reference signal. This can degrade the AVC performance, especially for a moving system. This study, therefore, aims to explore the potential application of piezoelectric stack actuators (PSA) to control the vibration of a moving stage driven by a stepper motor. The secondary path was estimated offline, and vibration data were collected at different moving speeds. The effects of the location of the reference sensor were initially investigated. Then, extensive simulations were conducted to evaluate the performance of different adaptive control algorithms regarding vibration reduction, convergence speed, computational complexities, etc. This research underlines the potential of integrating PSA within a moving system to effectively control its vibration.

A low-complexity multi-channel active noise control system using local secondary path estimation and clustered control strategy for vehicle interior engine noise

Conventional multi-channel active noise control (ANC) system based on the adaptive notch filtered-x least mean square algorithm is typically used for active control of vehicle interior engine noise with prominent harmonic characteristics. However, due to the large estimated secondary path length and the centralized control strategy, the system has high computational complexity and fragile stability. To alleviate this problem, an efficient multi-channel ANC system using the local secondary path (LSP) estimation and clustered control strategy is proposed in this paper. Based on the modified LSP estimation method, the system may utilize a set of low-order but accurate LSP models to simplify the convolution operations of reference filtering. Besides, the proposed clustered control strategy combines the advantages of centralized and decentralized control strategies, and thus the system not only enjoys good noise attenuation performance and stability, but also its computational burden is further greatly reduced. Numerical simulations are performed to evaluate the noise attenuation performance and frequency mismatch effect of the proposed multi-channel ANC system. In addition, a series of real vehicle ANC experiments are conducted. The results show that, under stationary work conditions, the interior overall, 2nd order, 4th order, 6th order engine noises may be attenuated by 12.91 dB(A), 32.98 dB(A), 15.34 dB(A) and 8.74 dB(A), respectively, and under nonstationary work conditions, the maximum overall noise reductions at the four error microphones may reach 6.40 dB(A), 6.14 dB(A), 12.48 dB(A) and 11.07 dB(A), respectively. This fully confirms the effectiveness of the proposed multi-channel ANC system in real-world applications.

Careful feedback active noise and vibration control algorithm robust to large secondary path changes

Feedback active noise and vibration control (ANVC) reduces narrowband noise and vibration when there is no reference signal. However, most ANVC algorithms require a secondary path (plant) model and can become unstable when this model is inaccurate. This work proposes an algorithm less sensitive to plant modeling errors and can cope with large and fast plant changes. This is achieved by modeling the plant and the noise using an autoregressive exogenous input (ARX) model. This model allows obtaining the expected values of the future residual noise and determining the optimal control signal. Since the model coefficient uncertainty (variance) is considered when calculating the expected value, the resulting controller is a careful controller. This allows solving problems due to significant model estimation errors when information is insufficient to estimate the full ARX model.