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Based on the support characteristics of new phased array radar, the evaluation index system of maintenance support capability of new phased array radar is constructed. And combining AHP with GRG, the index weight in the index system is calculated. Combined with the theory of center of gravity of cloud, the cloud model of the evaluation of maintenance supporting capability for the new phased array radar is constructed. The cloud model is used to evaluate the maintenance support capability of a typical new phased array radar, and the effectiveness of the method is verified by an example.

Increased RF noise floor, crowded and scarce wireless spectrum, and security are issues challenging present-day aerial wireless communication. Optical wireless (or free-space optical (FSO)) is an alternative communication system applicable to air-to-air (A2A) or air-to-ground (A2G) applications. An FSO link is able to provide high bandwidth, low-power, and secure communication for various unmanned aerial systems (UASs) and ground nodes, including mobile and fixed stations. However, unlike RF, optical communication is highly sensitive to misalignment and platform instability that causes unavoidable communication interruptions. Developing aerial optical wireless communication systems requires modeling the effects of UAS instability on the FSO system performance, including FSO link throughput. This work aims to develop a rigid and simple modeling framework for A2A FSO communication links. The framework is abstract, general, and platform-independent. The model relates instability to averaged percentage alignment (APA), which can be used to predict throughput and other performance-related parameters. A planer arrays of optical transmitters and receivers is used and proposed as a promising technology for FSO communication with large field-of-view and diversity-ready performance. Also, a novel alignment model using geometric intersection is developed and analyzed against simulated and real multi-rotor platforms.

Since the need for high alignment accuracy of the transmitter and receiver to construct a viable connection is a primary challenge for mobile free-space optical (FSO) links, developing an efficient transmitter control system for acquisition and tracking is an urgent demand. This paper investigates a novel control algorithm within different transmitter designs in FSO mobile networks. The fiber bundles are utilized so that the beam deflection properties of a lens can be exploited to affect beam steering. After acquiring and processing the data from GPS, fisheye system as well as the receiver, the adaptive control system would help tracking the mobile receiver and providing optimum coverage over the target area. Initial analysis of the design indicates refinements in design and performance required to develop a practical control system.

Implementation of digital signal processing (DSP) algorithms in hardware, such as field programmable gate arrays (FPGAs), requires a large number of multiplications. In this paper, we introduce a novel multiplier structure that converts from 2's complement to canonical signed digit (CSD) representation in real time. The proposed algorithm increases the number of zero partial products (which can be simplified by shift operations) to approximately 66.7% compared with 50% for modified Booth's recoding. Also, the proposed hardware structure reduces the non-zero partial products to a minimum, and consequently the number of arithmetic operations in the carry-save structure is reduced. So, our proposed hardware decreases both the time required for multiplication and the power consumption of the multiplier. Furthermore, because the proposed structure uses real time CSD recoding, and does not require a fixed value for the multiplier input to be known a priori, the proposed multiplier can be used to implement digital filters with non-fixed filter coefficients, such as adaptive filters.

We present a new particle filter (PF) algorithm, which uses a mathematical tool known as Galerkin's projection method to generate the proposal distribution. By definition, Galerkin's method is a numerical approach to approximate the solution of a partial differential equation. By leveraging this method with L2 theory and the FFT, this new proposal is fundamentally different to various local linearization or Kalman filter based proposals. We apply this algorithm to a bearings-only tracking problem. As shown in the theory and indicated by our simulations, this proposal renders more support from the true posterior distribution, thereby significantly enhances the estimation accuracy compared to standard bootstrap filters. In addition, because of this improved proposal distribution, the new particle filter can achieve a given level of performance with less sample size.

Most available control algorithms for active noise control (ANC) require identification of the secondary path. This estimation not only increases the control system complexity, but it can add to the residual noise power and even cause the adaptive system to diverge when the identification is not sufficiently close to the real system. We use a geometrical analysis of the filtered-x LMS algorithm to introduce a new ANC algorithm for a single frequency and narrowband noise where no identification of the secondary path is required. We then extend our new ANC algorithm without secondary path identification to the active control of broadband noise through the use of a sub-band ANC implementation. When compared to other available control algorithms requiring no secondary path identification, our method possesses a simple structure, good performance, and a reasonable convergence rate. Simulation results confirm the effectiveness of our proposed algorithms.

The adaptive Volterra filter has been successfully applied in nonlinear acoustic echo cancellation (AEC) systems and nonlinear line echo cancellation systems, but its applications are limited by its required computational complexity and slow convergence rate, especially for systems with long memory length. In this paper, by leveraging a multi-channel configuration of the Volterra filter and the sampling theory for nonlinear systems, we extend linear sub-band delay-less adaptive filter techniques to develop an efficient sub-band implementation of the adaptive Volterra filter. The developed sub-band configuration of the adaptive Volterra filter can greatly improve the convergence rate and reduce the computational complexity of nonlinear echo cancellers, which is shown by analyses and simulations.

This paper introduces a novel signal exclusive adaptive average (SEAA) filter that offers good image denoising performance in applications characterized by impulsive or impulse-like noise. The proposed algorithm works well in suppressing impulse noise with noise ratios from 3% up to 60%. We begin by introducing a digital differentiation preprocessing step to quantify the increments in each local neighborhood of the noisy image. A homogeneity level map is then derived by adaptive thresholding and used to designate pixels as noise candidates. The initial selection is refined using a novel connected components labeling algorithm. Finally, the noise is attenuated by estimating the values of the noisy pixels with a linear filter applied exclusively to those neighborhood pixels not labeled as noise candidates. This approach bears similarity to several nonlinear techniques including alpha-trimmed means, selective averaging, and WMMR filters. Simulation results indicate that SEAA is better able to preserve 2-D edge structures from the original image and delivers better performance with less computational overhead as compared to competing nonlinear denoising algorithms

It has been proven that adaptive nonlinear predistorters based on direct adaptation have many advantages over ones based on indirect adaptation. In this work, we develop two affine projection based direct adaptation algorithms for adaptive nonlinear predistorter: nonlinear filtered-x affine projection algorithm and nonlinear adjoint affine projection algorithm. Compared with available least mean square (LMS) based direct adaptation predistorters, predistorters based the new developed algorithms have the advantages fast convergence and efficient computational complexity. The nonlinear adjoint affine projection algorithm is computationally more efficient than the nonlinear filtered-x affine projection algorithm. Simulation results confirm the better performance of our developed algorithms.

Implementation of digital signal processing (DSP) algorithms in hardware, such as field programmable gate arrays (FPGAs), requires a large number of multipliers. In this paper, we introduce a new multi-input Canonical Signed Digit (CSD) multiplier unit, which requires fewer shift/add/subtract operations and less CSD number conversion overhead; as a result, we can greatly reduce the power consumption and area requirements in the hardware implementation of DSP algorithms. Furthermore, because all the products are produced simultaneously, the multiplication speed and thus the throughput are improved. The developed multi-input multiplier unit is applied to implement digital filters with non-fixed filter coefficients, such as adaptive filters. The implementation cost of these digital filters can be further reduced by limiting the wordlength of the input signal with little or no sacrifice to the filter performance, which is confirmed by our simulation results. The proposed multiplier unit can also be applied to other DSP algorithms, such as digital filter banks or matrix and vector multiplications.

The constrained minimum output variance method (CMOV), which is also known as the constrained minimum output energy method (CMOE), has been applied to directly blind equalize a linear channel and proven effective with white inputs (Tsaisanis et al. (1999)). It is believed that the method introduced in Tsaisanis can work for a linear system with colored source. In this paper, we correct this misunderstanding, and prove that a colored input will cause the equalizer to incorrectly converge. Consequently, we introduce a new blind channel equalizer algorithm based on the CMOV, but with different constraints. Our proposed algorithm works for channels with either white or colored input, and performs equivalently to the trained minimum mean-square-error (MMSE) equalizer under high SNR. Our proposed algorithm may be regarded as a generalized version of the CMOV in Tsaisanis. However, unlike CMOV, the proposed method exploits the input sequence statistics. As a result, the proposed method performs better than the original CMOV even when the input is white.

In this paper, we present a geometric based analysis of the "filtered" LMS algorithms such as the filtered-x LMS algorithm and the adjoint LMS algorithm. Using this method, the maximum step size, the effects of the secondary path estimation error and the relationship between the adjoint, secondary path equalization and FXLMS LMS algorithms can be easily obtained. Furthermore, our introduced method yields these conditions with some geometrical meaning and intuitive explanation. Based on our introduced method, we also discuss the convergence speed of the filtered-x LMS algorithm for the first time

In this paper, we discuss the sub-space based blind nonlinear channel equalization problem. The conditions required for exciting the zero force equalizer and an MMSE equalizer based on the sub-space algorithm are given. Using these conditions, we propose several efficient sub-space-based blind nonlinear channel equalization algorithms. These provide: 1) a blind zero force equalizer for a nonlinear channel whose linear portion has maximum memory length, 2) an MMSE equalizer for a nonlinear channel whose nonlinear distortion is weak and 3) an MMSE equalizer for a nonlinear channel whose linear part has minimum memory length while the inputs to the linear and nonlinear parts are uncorrelated to each other. Our proposed algorithms are more efficient than currently available methods and work over a broader range of channels. As a result, our algorithms have significant practical application

In this work, we develop novel control algorithms suitable for the smart shock absorber (SSA) component of the intelligent vehicle and bridge system (IVBS). The SSA control serves two IVBS purposes: 1) working as the vehicle suspension to increase driver comfort and controllability when the vehicle is on the pavement, and 2) reducing the vibration of highway bridges and thus prolonging bridge life. We propose and discuss in this paper algorithms targeted at the first purpose. Based on the application of active noise control techniques to an active suspension system, we introduce the clipped active noise control (clipped ANC) algorithm. The main advantage of this algorithm is that because it is adaptive, it can effectively balance the comfort and controllability. A simplified control algorithm based on the clipped ANC is also proposed to reduce the implementation cost. The simplified algorithm has no requirements for system modeling, so it substantially reduces the implementation cost. The effectiveness of our proposed algorithm is indicated by analyses, simulations and laboratory testing.

Vibration mitigation has become increasingly vital for the current highway bridge system. Since the main source of bridge vibrations comes from heavy trucks passing by, we propose a control system that can be installed on the trucks to help reduce bridges' vibration. The approach is based on the nonlinear black-box model of the truck-bridge interaction.

Adaptive filtering is a widely used technique in active noise control (ANC). In order to make the adaptive filter in an FXLMS (filtered-x LMS) ANC system stable, the reference signal must pass through an estimation filter whose phase response is within /spl plusmn/90/spl deg/ of the phase of the secondary path. In this paper, we study the quantization effects on the filter phase response and the relationship between the phase response and the location of the zeros and poles. In addition, we propose a filter structure and nonuniform quantization method in which we quantize the filter coefficients so they each contain a small number of nonzero bits, based on the distance of the zeros/poles to the unit circle, to guarantee that the /spl plusmn/90/spl deg/ allowable phase deviation is met - greatly reducing the implementation cost. We combine these ideas with that of multiplierless implementations of adaptive FIR filters to realize an efficient active noise control using field programmable gate arrays or other digital hardware.

The filtered-X LMS algorithm is successfully applied in linear active noise control and linear active vibration control. However, when it comes to non-linear noise or vibration control, or when the secondary path has a non-linearity, this algorithm must be modified to ensure that it converges and that it is unbiased. Some modified algorithms have been proposed for active control of nonlinear noise with a linear secondary path [L.Z. Tan and J. Jiang, Aug. 2001] or a weakly non-linear secondary path [V. DeBrunner and D. Zhou, 2002]. In this paper, we extend 'a non-linear filtered-X LMS' algorithm to solve the active non-linear noise control (ANNC) and active non-linear vibration control (ANVC) with non-linearities in the secondary path using an adaptive Volterra filter for the first time. Two improved versions of the non-linear adaptive filters that improve the stability and reduce the computational complexity are proposed and discussed in detail. Simulations show their effectiveness.

The filter-X LMS algorithm is successfully applied in linear active noise control and linear active vibration control. However, when it comes to nonlinear noise or vibration control, or the secondary path has a nonlinearity, this algorithm needs to be modified to ensure that it converges and that it is unbiased. We proposed configurations [V. DeBrunner, et al., 2003] that solve both the main path nonlinearity and the secondary path nonlinearity problems where the nonlinearity can be expressed by Volterra series. However, in practices some nonlinear system can be expressed in much simple form such as a linear filter followed by a memoryless nonlinearity (Wiener model), a memoryless nonlinearity followed by linear filter (Hammerstein model), a linear filter, a memoryless nonlinearity and another linear filter connected in series (LNL model). Such kinds of nonlinear system structure can greatly reduce the computation burden and simplify the implementation structure. In this paper, we give out active nonlinear noise control (ANNC) with these kinds of secondary path nonlinearity. We find with these kinds of nonlinear structure in the secondary path, the ANNC will have much simpler structure and the computation burden can be further reduced.

Xu, Y. et al. (see IEEE T-IP, vol.3, no.6, 1994) proposed an effective wavelet based spatially selective denoising algorithm. The performance of the algorithm depends on the noise power estimation. Pan, Q. et al. (see IEEE T-SP, vol.47, no.12, 1999) tried to improve the performance via a small modification. However, our simulation shows that both of these methods are sensitive to noise estimation. We analyze the sensitivity of these two methods and introduce a new spatially selective noise filter based on the UDWT (undecimated wavelet transform) that uses spatial correlation thresholding. Theoretic analysis and simulations show our algorithm improves the denoising effect. They also show that our proposed method is robust to errors in the noise power estimate. Because our approach is robust, we can relax the requirements for the estimation of the threshold without sacrificing performance, and so our method is more computationally efficient. We also put some perspective on the impact of employing nonorthogonal representations. Simulation results show the effectiveness of our proposed algorithm.

The nonlinear predistorter is an effective technique to compensate the nonlinear distortions existing in a digital communication system. However, available adaptive nonlinear predistorters are either based on the indirect learning algorithm, or are complicated in structure and computation. In this paper, we propose a novel adaptive nonlinear predistorter based on a direct learning algorithm: the adjoint nonlinear LMS algorithm. Because of the direct learning algorithm, our adaptive predistorter outperforms the other nonlinear predistorters that are based on the indirect learning method in the sense of mean square error (MSE). Moreover, compared with any other adaptive nonlinear predistorter based on the direct learning architecture, our predistorter has a simpler structure and lower computational complexity. Simulation results show the effectiveness of our nonlinear adaptive predistorter.

In this paper, we address the problem of adaptive control of a nonlinear noise process in the presence of a weakly nonlinear secondary path; This problem is common in many cases of Active Vibration Control (AVC) and Active Noise Control (ANC) where the secondary path nonlinearity is due to the presence of a weak nonlinear actuator. Our proposed configuration turns out to be subsumed by a method called the filtered-A LMS algorithm proposed by Klippel [1997]. In that paper, Klippel attempted to linearize a weakly nonlinear plant (main or primary path) using an adaptive inverse control method so that the nonlinear plant will track an input signal. He certainly did not consider our configuration or usage. Thus some effort to connect these two algorithms is required. Furthermore, we also determine that as the nonlinear secondary path becomes linear, and there is thus only a nonlinear primary path, the filtered-A LMS algorithm and our configuration gives the so-called Volterra filtered-x LMS (VFLMS) algorithm [2001] developed by Tan and Jiang. In this paper, we connect the algorithms. Detailed implementation information is provided. Extensive simulations show that the algorithm works well for our AVC problem.