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Adaptive smart antenna arrays are used for cellular communication. This paper presents a comparative study of beamforming techniques for adaptive smart antenna using hybrid algorithms like Simple Matrix Inversion with Recursive Least Square (SMI-RLS) and Least Mean Square with Recursive Least Square (LMS-RLS) algorithm. The results are compared on the basis of null depth and error plot for different signal –to-noise (SNR) values, ranging from high to low values of SNR for heavily faded case..

본 논문은 자율주행 개인화를 위한 순환 최소자승 기반 융합형 종방향 주행특성 구분 알고리즘에 관한 연구이다. 최근 자율주행 기술은 Level 4 완전 자율주행 단계를 위해 다양한 연구가 수행되고 있다. 자율주행 자동차의 상용화를 위해서는 탑승자의 자율주행에 대한 이질감을 최소화할 수 있어야 하며 이를 위해 자율주행 개인화 기술이 필요하다. 이 문제를 해결하기 위해 본 연구에서는 운전자의 종방향 주행특성을 수학적으로 표현하고 순환 최소자승 기법 기반 실 주행 데이터를 이용하여 주행특성을 도출하는 알고리즘을 제안하였다. 두 명의 실제 운전자 데이터를 이용하여 종방향 주행특성을 도출하였으며 두 명의 운전자를 구분하기 위해 가설검정 기반 확률적 구분 알고리즘을 적용하였다. 제안된 종방향 주행특성 도출 및 구분 알고리즘은 개별 운전자의 주행특성을 합리적으로 나타낼 수 있었으며 가설검정 기반 확률적 구분기법에 의해 주행특성이 구분될 수 있음을 확인하였다.

This paper describes a recursive least-squares based convergent algorithm for driving characteristic classification for personalized autonomous driving. Recently, various researches on autonomous driving technology have been conducted for level 4 fully autonomous driving. In order for commercialization of the autonomous vehicle, personalized autonomous driving is required to minimize passenger’s insecureness to the autonomous vehicle. To address this problem. this study proposes mathematical model that represents driving characteristics and recursive least-squares based algorithm that can estimate the defined characteristics. The actual data of two drivers has been used to derive driving characteristics and the hypothesis testing method has been used to classify two drivers. It is shown that the proposed algorithms can derive driving characteristics and classify two drivers reasonably.

Elimination of noise from the signal is the major task in signal processing applications. Wiener filter removes noise efficiently but it requires large number of computations and it was updated with speed issue with adaptive filter. Adaptive filter has several algorithms to remove noise from the signal. This paper performs cancellation of noise from the signal using wiener filter and adaptive filter algorithms namely LMS, NLMS and RLS algorithms in real time environment. All these methods are compared using several parameters like step size, mean and variance of noise, mean square error, signal to noise ratio, speed, no. of. Iterations etc. In the existence work, the authors have compared the performance of the wiener filter & LMS algorithm in real time environment with sinusoidal input. This paper is extended by comparing different adaptive filter algorithms with the input taken in real time environment. It is observed that RLS algorithm performs noise cancellation better than all other algorithms. But it has high degree of complexity & cost while NLMS algorithm has moderate speed of performance and it is quietly chosen for several applications.

This paper proposes a method that enables us to implement the recursive least squares (RLS) algorithm at, high throughput rate using fewer processing elements (PEs). It is known that the pipeline processing can provide a high throughput rate. But, pipelining is effective only when enough number of PEs are available. The proposed method achieves high throughput rate using a few PEs. The effectiveness of the proposed method is verified through simulations on programmable digital signal processors (in the following, DSP processors).

시변 신호 추적 특성을 향상시키기 위하여, Gauss-Newton Variable Forgetting Factor RLS (GN-VFF-RLS) Algorithm을 제안한다. 최적화된 망각인자를 가정한 기존의 RLS 알고리듬과 비교하여, 제안된 방법은 특히 신호의 변화가 급격히 일어날 경우 주목할만한 추정 성능의 향상을 보여준다. 제안된 알고리듬의 시변 추정 특성을 신호 대 잡음비와 시변 정도에 대하여 모의 실험하고 기존의 추정 알고리듬들과 비교한다.

RLS algorithm is a kind of the adaptive algorithms in smart antennas and adapts the weight vector using the difference between the output signal of array antennas and the known training sequence. In this paper, we propose a new algorithm based on the RLS algorithm. It calculates the error signal with reference signal derived from blind scheme. Simulation results show that the proposed algorithm yields more user capacity by 67∼74% than other blind adaptive algorithms(LS-DRMTA, LS-DRMTCMA) at the same BER and the beamformer forms null beams toward interference signals and the main beam toward desired signal.

적응 횡단선 필터에서 수렴 속도의 개선을 위해 기존의 최소 평균 자승 알고리즘을 확장한 반복적 최소 자승 알고리즘의 탭 가중치 갱신 메커니즘에 재순환 데이터 버퍼를 이용함으로서 수렴특성을 개선시키는 효율적인 기법을 제시하였다. 본 논문은 기존의 적응 횡단선 필터에 데이터 재순환 버퍼 구조를 제안하여 새로운 RLS 탭 가중치 갱신 알고리즘을 유도하여 조화 평균 학습 곡선의 평균 자승 에러 값에 대한 반복수에 대해서 데이터 재순환 버퍼를 사용한 학습 곡선의 수렴 속도가 버퍼가 없는 경우의 재순환 버퍼 RLS 알고리즘의 수렴 속도보다 비례하여 빠르게 수렴한다는 것을 수학적인 연산을 통해 증명하였다. 채널 진폭의 왜곡의 정도와 재순환 데이터 버퍼 수에 따른 평균 자승 에러에 대한 삼차원 시뮬레이션 결과로부터 고유치 확산이 증가함에 따라 특정 값에 수렴하기 위한 요구된 샘플의 반복수가 비례하여 증가하였으며, 재순환 데이터 버퍼 수 B가 증가함에 따라 요구된 샘플의 반복수가 B배만큼 감소함으로서 제안된 구조에서 RLS 가중치 갱신 알고리즘의 수렴특성이 개선됨을 입증하였다.

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-l, we may compute the updated estimate of this vector at iteration n upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RLS algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the B times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.

This paper presents two algorithms based on the concept of the frequency domain adaptive filter(FDAF). First the frequency domain recursive least squares(FRLS) algorithm with the overlap-save filtering technique is introduced. This minimizes the sum of exponentially weighted square errors in the frequency domain. To eliminate discrepancies between the linear convolution and the circular convolution, the overlap-save method is utilized. Second, the sliding method of data blocks is studied Co overcome processing delays and complexity roads of the FRLS algorithm. The size of the extended data block is twice as long as the filter tap length. It is possible to slide the data block variously by the adjustable hopping index. By selecting the hopping index appropriately, we can take a trade-off between the convergence rate and the computational complexity. When the input signal is highly correlated and the length of the target FIR filter is huge, the FRLS algorithm based on the block processing technique has good performances in the convergence rate and the computational complexity.

본 논문에서는 음향반향제거기를 구현하기 위한 적응알고리즘을 제안한다 특히 긴 임펄스 응답을 가지는 시스템의 식별을 위해 웨이블릿 필터를 사용하여 입력신호를 서브밴드로 분해함으로써 기존의 RLS알고리즘의 계산량을 줄여 수렴속도를 향상시켰다. 이 과정에서 적응필터를 다위상 구조로 구성하여 컨벌루션 과정을 병렬처리가 가능하도록 하였다. 제안된 알고리즘의 성능분석을 위하여 실제 음성신호를 입력신호로 하여 컴퓨터 모의실험을 수행하였으며 전대역 RLS알고리즘과 비교하였다.

RLS (Recursive Least Squares) 알고리즘은 적응 알고리즘의 대표적인 알고리즘이다. 하지만, 기본적인 RLS 알고리즘은 빠르게 움직이는 신호와 같은 비정상 (non-stationary) 신호환경에서는 좋은 성능을 가질 수 없다는 단점이 있다. 이를 해결하기 위하여 가변 망각 인자를 가지는 RLS 알고리즘이 등장하였으나, 기존의 가변 망각 인자 RLS 알고리즘은 연산량이 너무 많다는 단점이 있다. 본 논문에서는 이를 해결하기 위하여, 상대적으로 적은 연산량으로 AFF-RLS 알고리즘과 비슷한 성능을 내는 RLS 알고리즘을 제안한다.

The RLS (Recursive Least Squares) method is a broadly used adaptive algorithm for signal processing in electronic engineering. The RLS algorithm shows a good performance and a fast adaptation within a stationary environment, but it shows a Poor performance within a non-stationary environment because the method has a fixed forgetting factor. In order to enhance 'tracking' performances, BLS methods with an adaptive forgetting factor had been developed. This method shows a good tracking performance, however, it suffers from heavy computational loads. Therefore, we propose a modified AFF-RLS which has relatively low complexity m this paper.

In this paper, we present an effective RLS algorithm with forgetting factor of Erlang function for the system identification. In the proposed algorithm, the forgetting factor decreases monotonically in the first stage, and then it increases monotonically in the second stage in contrary to the conventional forgetting factor RLS algorithms. In addition, annealing effect and an asymptotically stability of the proposed algorithm is discussed based on the analysis of convergency property on. Simulation results for the system identification problem indicate the superiority of the proposed algorithm in comparison to the RLS algorithm such as NLMS and Kalman filter based algorithm.

This paper proposes an easy algorithm for finding tapped-delay-line (TDL) filter coefficients in an adaptive filter algorithm using orthogonal input signals. The proposed algorithm can be used to obtain the coefficients and errors of a TDL filter without using an inverse orthogonalization process for the orthogonal input signals. The form of the proposed algorithm in this paper has the advantages of being easy to use and similar to the familiar recursive least-squares (RLS) algorithm. In order to evaluate the proposed algorithm, system identification simulation of the $11^{th}$-order finite-impulse-response (FIR) filter was performed. It is shown that the convergence characteristics of the learning curve and the tracking ability of the coefficient vectors are similar to those of the conventional RLS analysis. Also, the derived equations and computer simulation results ensure that the proposed algorithm can be used in a similar manner to the Levinson-Durbin algorithm.

This paper deals with RLS algorithm using Newton-Raphson method based adaptive forgetting factor for a passive telemetry RF sensor system in order to estimate the time variant parameter to be included in RF sensor model.

This paper deals with RLS algorithm using Newton-Raphson method based adaptive forgetting factor for a passive telemetry RF sensor system in order to estimate the time variant parameter to be included in RF sensor model. For this estimation with RLS algorithm, phasor typed RF sensor system modelled with inductive coupling principle is used. Instead of applying constant forgetting factor to estimate time variant parameter, the adaptive forgetting factor based on Newton-Raphson method is applied to RLS algorithm without constant forgetting factor to be determined intuitively. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper.

무선 이동체에서 어댑티브 어레이 안테나를 적용하는 경우 가중계수벡터를 전파 환경의 변화에 빠르게 적응시키는 것이 중요하다. 4소자 등간격 선형 어레이 안테나 시스템에 대하여 일정 진폭의 포락선을 갖는 BPSK신호에 LMS 와 RLS 알고리즘을 컴퓨터 시뮬레이션에 적용시켜 본 결과 LMS 알고리즘의 수렴 특성에 비하여 RLS 알고리즘의 수렴 특성이 매우 뛰어남을 알 수 있었다.

Adaptive array is using the array of antenna elements spatially and its output is the sum of each antenna elements output signal which is multiplied by the controlled weight coefficients corresponding to each elements For the 4 elements equidistance linear array antenna system LMS and RLS algorithm was used as the adaptive instruction principles and the application results to the constant amplitude envelope signals such as BPSK can be seen that the computer simulation results are very fast in the convergence characteristics of directional patterns and the signal following characteristics.

In this paper, recursive least square algorithm is presented to estimate the parameters of step motor under low-speed operation. Parameter estimation is important for compensating the input current by calculating the ratio of the motor torque constant and detent torque constant that causes torque-ripple in low-speed applications. On-line parameter estimation process is a preliminary procedure to apply step motor to adaptive control. Computer simulation shows that the estimated parameters converge in finite time.

In this study, We derived Recursive Least Squares(RLS) algorithm with adaptive maximum -likelihood channel estimate for digital pulse amplitude modulated sequence in the presence of intersymbol interference and additive white Gaussian noise. RLS algorithms have better convergence characteristics than conventional algorithms, LMS Least Mean Squares) algorithms.

RLS algorithms are required for applications to adaptive line enhancers, adaptive equalizers for voiceband telephone and HF modems, and wide-badn digital spectrum mobile raio in which their convergence time and tracking speed are significant. The fast QR RLS algorithm satisfies above the requirements. Its computational complexity is linearly proportional to the tap number of a filter, N and its performance remains numerically stable. From the result of simumulation, the fast QR RLS algorithm represented Cioffi is better than gradient based algorithm in its initial performance when being applied to an adaptive line enhancer for cancelling noise.

This paper presents a newly developed speed sensorless drive using RLS(Recursive Least Squares) based on Neural Network Training Algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known back-propagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.

While driving, the low frequency interior noise below 200Hz causes the main component that irritates the auditory acoustic sense. But these passive control methods bring out increment in cost and weight of the vehicle and result in low efficiency. Recently, various ANC(Active Noise Control) methos to suppress the low frequency noise began to launch into application. In this study, we implemented the active noise control system for passenger vehicle to cancel the engine booming noise using DSP-based control unit, 4 micorphones, and 2 speakers. We used MEFX-LMS (Multiple Error Filtered X-Least Mean Square) algorithm since it can be easily implemented in real time. Also, MEFX-RLS algorithm was taken to enhance the suppression of the harmonic components of the engine booming noise inspite of its computational complexity. The performance of two adaptive algorithms were analyzed with experimental resutls.