Adaptive Detection Scheme Research Articles

Frequency-domain adaptive detectors for single-carrier frequency-domain equalisation in multiuser direct-sequence ultra-wideband systems based on structured channel estimation and direct adaptation

Here, the authors propose two adaptive detection schemes based on single-carrier frequency-domain equalisation (SC‐FDE) for multiuser direct-sequence ultra-wideband systems, which are termed structured channel estimation (SCE) and direct adaptation (DA). Both schemes use the minimum mean square error (MMSE) linear detection strategy and employ a cyclic prefix. In the SCE scheme, adaptive channel estimation is performed in the frequency domain and the despreading is implemented in the time domain after the FDE. In this scheme, the MMSE detection requires the knowledge of the number of users and the noise variance. For this purpose, simple algorithms are proposed for estimating these parameters. In the DA scheme, the interference suppression task is fulfilled with only one adaptive filter in the frequency domain and a new signal expression is adopted to simplify the design of such a filter. Least mean squares, recursive least squares and conjugate gradient adaptive algorithms are then developed for both schemes. A complexity analysis compares the computational complexity of the proposed algorithms and schemes, and simulation results for the downlink illustrate their performance.

Efficient detection scheme in MIMO-OFDM for high speed wireless home network system

This paper proposes a MIMO-OFDM scheme reducing the computational complexity and providing comparable performance to QRD-M detection scheme in IEEE 802.11n. The proposed scheme checks the channel condition per subcarrier and then selects either ordered DFE scheme or adaptive QRD-M detection scheme according to that information. The post-detection SNR which judges channel condition depends on Moore-Penrose pseudo-inverse matrix G. The proposed scheme selects the ordered DFE detection scheme in case of good channel condition and the adaptive QRD-M detection scheme in case of bad channel condition per subcarrier. The adaptive QRDM detection scheme selects the number of variable survival symbols M in accordance with the channel condition at each layer. Compared with the conventional QRD-M (M=16) detection scheme, the proposed detection scheme reduces the computational complexity about 67 percents and achieves similar performance.

Adaptive Group Detection Based on the Sort-Descending QR Decomposition for V-BLAST Architectures

Combining the sphere decoding (SD) algorithm and the sequential detection method, we propose an adaptive group detection (AGD) scheme based on the sort-descending QRD (S-D-QRD) for V-BLAST architectures over an i.i.d. Rayleigh flat fading channel. Simulation results show that the proposed scheme, which encompasses the SD algorithm and the sequential detection method as two extreme cases in a probability sense, can achieve a very flexible tradeoff between the detection performance and computational complexity by adjusting the group parameter.

Advanced Radar Detection Schemes Under Mismatched Signal Models

Adaptive detection of signals embedded in correlated Gaussian noise has been an active field of research in the last decades. This topic is important in many areas of signal processing such as, just to give some examples, radar, sonar, communications, and hyperspectral imaging. Most of the existing adaptive algorithms have been designed following the lead of the derivation of Kelly's detector which assumes perfect knowledge of the target steering vector. However, in realistic scenarios, mismatches are likely to occur due to both environmental and instrumental factors. When a mismatched signal is present in the data under test, conventional algorithms may suffer severe performance degradation. The presence of strong interferers in the cell under test makes the detection task even more challenging. An effective way to cope with this scenario relies on the use of detectors, i.e., detectors capable of changing their directivity through the tuning of proper parameters. The aim of this book is to present some recent advances in the design of tunable detectors and the focus is on the so-called two-stage detectors, i.e., adaptive algorithms obtained cascading two detectors with opposite behaviors. We derive exact closed-form expressions for the resulting probability of false alarm and the probability of detection for both matched and mismatched signals embedded in homogeneous Gaussian noise. It turns out that such solutions guarantee a wide operational range in terms of tunability while retaining, at the same time, an overall performance in presence of matched signals commensurate with Kelly's detector. Table of Contents: Introduction / Adaptive Radar Detection of Targets / Adaptive Detection Schemes for Mismatched Signals / Enhanced Adaptive Sidelobe Blanking Algorithms / Conclusions

Simple and Efficient Received Signal Detection Technique Using Channel Information for MIMO-OFDM

Generally, among the MIMO detection scheme, many paper suggested V-BLAST detection technique which can achieve high data rate. As V-algorithm which represented in [1] is one of detection scheme for V-BLAST, it can more accurately detect initial state symbol in processing of OSIC or MMSE to achieve approximately optimal BER performance, because the first detected symbol usually shows the worst BER due to the interlayer interference in processing of OSIC or DFE. Moreover, V-algorithm has lower complexity than ML detection scheme, but the more high value of V is adapted, the more complicated V-algorithm is. So, we propose adaptive detection scheme based on [1] using the post-detection SNR. When you use the proposed detection scheme, you can acquire optimal BER performance of V-algorithm with much lower complexity. The proposed scheme uses the allotment of V for each subcarrier according to the post-detection SNR.

A Novel Adaptive Detection Scheme based on Parallel Interference Cancellation for V-BLAST System

In this paper, in order to reduce the computational complexity of the detector of vertical Bell Laboratories layered space-time (V-BLAST) system over time-varying channels, an adaptive detection scheme is proposed based on parallel interference cancellation (PIC). The presented scheme has three units, which are primary adaptive detector, detection ordering determiner, and adaptive PIC detector. The proposed scheme can employ many of known adaptive algorithms for detection of V-BLAST system. In this paper, we present computational complexity of the proposed scheme using LMS, RLS, APA and AMSER adaptive algorithms and evaluate its performance with numerical simulations.

An adaptive multiuser detector for DS-CDMA systems in multipath fading channels

In this paper, we investigate the performance of an adaptive multistage detection scheme for direct-sequence code-division multiple-access (DS-CDMA) systems. The first stage consists of an adaptive...

An adaptive multiuser detector for DS‐CDMA systems in multipath fading channels

AbstractIn this paper, we investigate the performance of an adaptive multistage detection scheme for direct‐sequence code‐division multiple‐access (DS‐CDMA) systems. The first stage consists of an adaptive multiuser detector which is based on the linear constrained minimum variance (LCMV) criterion. The interference cancellation (IC) occurs in the second stage. The performance of the iterative receiver over both flat and frequency‐selective fading channels is investigated and compared to the single‐user bound. In all cases, and under heavy system loads with near‐far problems, the iterative receiver is shown to offer substantial performance improvement and large gain in user‐capacity relative to the standard LCMV. In flat‐fading channels, our results show that the performance of the iterative detector is very close to the single‐user bound. For the frequency‐selective channel, this performance is noted to be in the order of 1 dB far from the single‐user bound. Copyright © 2007 John Wiley & Sons, Ltd.

Design and analysis of a knowledge-aided radar detector for doppler processing

In this paper we discuss the combined use of a priori information and adaptive signal processing techniques for the design and the analysis of a knowledge-aided (KA) radar receiver for Doppler processing. To this end, resorting to the generalized likelihood function (GLF) criterion (both one-step and two-step), we design and assess data-adaptive procedures for the selection of training data. Then we introduce a KA radar detector composed of three elements: a geographic-map-based data selector, which exploits some a priori information concerning the topography of the observed scene, a data-adaptive training selector which removes dynamic outliers from the training data, and an adaptive radar detector which performs the final decision about the target presence. The performance of the KA algorithm is analyzed both on simulated as well as on real radar data collected by the McMaster University IPIX radar. The results show that the new KA system achieves a satisfactory performance level and can outperform some previously proposed adaptive detection schemes

A new edge detection approach based on image context analysis

A new adaptive edge detection approach based on image context analysis is presented in this paper. The proposed approach uses the information from predictive error values produced by the GAP predictor to detect edges. The experimental results indicate that both the visual evaluations and objective performance evaluations of the detected image in the proposed approach are superior to the edge detection of Sobel, Canny and the scheme presented by Tsai et al. [P. Tsai, C.C. Chang, Y.C. Hu, An adaptive two-stage edge detection scheme for digital color images, Real-Time Imaging 8 (4) (2002) 329–343]. To meet the needs of users, the flexibility in the threshold selection in the proposed approach is the same as that of the edge detection scheme [P. Tsai, C.C. Chang, Y.C. Hu, An adaptive two-stage edge detection scheme for digital color images, Real-Time Imaging 8 (4) (2002) 329–343]. The proposed approach, which is far more accurate than the detection scheme in [P. Tsai, C.C. Chang, Y.C. Hu, An adaptive two-stage edge detection scheme for digital color images, Real-Time Imaging 8 (4) (2002) 329–343], can precisely locate object contours in the image, especially for complex scenes. This feature, which the edge detection scheme [P. Tsai, C.C. Chang, Y.C. Hu, An adaptive two-stage edge detection scheme for digital color images, Real-Time Imaging 8 (4) (2002) 329–343] lacks, is of extreme importance to some applications such as data hiding, watermarking, morph, and pattern recognition. In addition, the approach can be integrated into a prediction-based lossless image compression scheme to provide both the lossless compression codes and edge maps of objects, which facilitate the image transmission and objects recognition for medical diagnoses and other applications.

Mutual information-based approach to adaptive homodyne detection of quantum optical states

The author proposes an approach to adaptive homodyne detection of digitally modulated quantum optical pulses in which the phase of the local oscillator is chosen to maximize the average information gain, i.e., the mutual information, at each step of the measurement. The properties of this adaptive detection scheme are studied by considering the problem of classical information content of ensembles of coherent states. Simulations of quantum trajectories and visualizations of corresponding measurement operators demonstrate that the proposed measurement scheme adapts itself to the features of each ensemble. For all considered ensembles of coherent states, it consistently outperforms heterodyne detection and Wiseman's adaptive scheme for phase measurements [H.M. Wiseman, Phys. Rev. Lett. 75, 4587 (1995)].

Subspace-based blind adaptive multiuser detection using Kalman filter

A new blind adaptive multiuser detection scheme based on a hybrid of Kalman filter and subspace estimation is proposed. It is shown that the detector can be expressed as an anchored signal in the signal subspace and the coefficients can be estimated by the Kalman filter using only the signature waveform and the timing of the desired user. The resulting subspace-based algorithm brings the benefit of lower computational complexity than the full-rank approach, and the theoretical analysis indicates that the proposed algorithm is also superior in convergence performance. The adaptive implementation in a dynamic environment such as a variable number of users is obtained by seamlessly integrating a subspace tracking algorithm. The new subspace-based method is effective in AWGN channels as well as in slowly time-varying Rayleigh fading channels. Moreover, the proposed detector is much more robust against the signalling waveform mismatch and inaccurate knowledge of the amplitude of the desired signal than the full-rank one, as demonstrated by computer simulations.

Using unknown input observers for robust adaptive fault detection in vector second-order systems

The purpose of this manuscript is to construct natural observers for vector second-order systems by utilising unknown input observer (UIO) methods. This observer is subsequently used for a robust fault detection scheme and also as an adaptive detection scheme for a certain class of actuator faults wherein the time instance and characteristics of an incipient actuator fault are detected. Stability of the adaptive scheme is provided by a parameter-dependent Lyapunov function for second-order systems. Numerical example on a mechanical system describing an automobile suspension system is used to illustrate the theoretical results.

Mitigation Techniques for Non-Gaussian Sea Clutter

This paper deals with the problem of coherent radar detection of targets embedded in clutter modeled as a compound-Gaussian process. We first provide a survey on clutter mitigation techniques with a particular emphasis on adaptive detection schemes ensuring the constant false-alarm rate (CFAR) property with respect to all of the clutter parameters. Thus, we propose a novel decision rule based on a recursive covariance estimator, which exploits the persymmetry property of the clutter covariance matrix. Remarkably, the devised receiver is fully CFAR in that its threshold can be set independently of the clutter distribution as well as of its covariance, even if the environment is highly heterogeneous; namely, the disturbance distributional parameters vary from cell to cell. At the analysis stage, we compare the performance of the novel detector with some classical radar receivers such as that of Kelly and the adaptive matched filter both in the presence of simulated as well as on real radar data, which statistical analysis has shown to be compatible with the compound-Gaussian model. The results show that the new receiving structure generally provides higher detection performance than the others and, for a fluctuating target, it uniformly outperforms the counterparts. We also provide a discussion on the CFAR behavior of the analyzed receivers as well as on their computational complexity.

Downlink capacity of interference-limited MIMO systems with joint detection

The capacity of downlink cellular multiple-input multiple-output (MIMO) systems, where co-channel interference is the dominant channel impairment, is investigated in this paper, mainly from a signal-processing perspective. Turbo space-time multiuser detection (ST MUD) is employed for intracell communications and is shown to closely approach the ultimate capacity limits in Gaussian ambient noise for an isolated cell. Then, it is combined with various multiuser detection methods for combating intercell interference. Among various multiuser detection techniques examined, linear minimum-mean-square-error (MMSE) MUD and successive interference cancellation are shown to be feasible and effective. Based on these two multiuser detection schemes, one of which may outperform the other for different settings, an adaptive detection scheme is developed, which together with a Turbo ST MUD structure offers substantial performance gain over the well-known V-BLAST techniques with coding in this interference-limited cellular environment. The obtained multiuser capacity is excellent in the high to medium signal-to-interference ratio scenario. Nonetheless, numerical results also indicate that a further increase in system complexity, using base-station cooperation, could lead to further significant increases of the system capacity. The asymptotic multicell MIMO capacity with linear MMSE MUD preprocessing is also derived, and this analysis agrees well with the simulation results.

Performance of adaptive MC-CDFMA detectors in rapidly fading rayleigh channels

Multicarrier code-division multiple access (MC-CDMA) combines multicarrier transmission with direct sequence spread spectrum. Different approaches have been adopted which do not assume a perfectly known channel. We examine the forward-link performance of decision-directed adaptive detection schemes, with and without explicit channel estimation, for MC-CDMA systems operating in fast fading channels. We analyze theoretically the impact of channel estimation errors by first considering a simpler system employing a threshold orthogonality restoring combining (TORC) detector with a Kalman channel estimator. We show that the performance deteriorates significantly as the channel fading rate increases and that the fading rate affects the selection of system parameters. We examine the performance of more realistic schemes based on the minimum mean square error (MMSE) criterion using least mean square (LMS) and recursive least square (RLS) adaptation. We present a discussion which compares the decision-directed and pilot-aided approaches and explores the tradeoffs between channel estimation overhead and performance. We find that there is a fading rate range where each method provides a good tradeoff between performance and overhead. We conclude that the MMSE per carrier decision-directed detector with RLS estimation combines good performance in low to moderate fading rates, robustness in parameter variations, and relatively low complexity and overhead. For higher fading rates, however, only pilot-symbol-aided detectors are appropriate.

An Adaptive Two-Stage Edge Detection Scheme for Digital Color Images

An adaptive two-stage edge detection scheme for digital color images is proposed in this paper. In the first stage of this scheme, each three-dimensional color image is reduced to a one-dimensional gray-level image using the moment-preserving thresholding technique. Then, a new edge detection technique based on the block truncation coding scheme is introduced to detect the edge boundary in the second stage. The edge detection process makes use of the bit plane information of each BTC-encoded block to detect the edge boundary. The experimental results show that the performance of the detected edge image of the proposed scheme is as good as in Yang's scheme and in the Sobel operator. However, the computational cost consumed by the proposed scheme is less than that of Yang's scheme. In addition, the proposed scheme provides an adaptive edge quality decision mechanism. This mechanism can provide different edge images to meet various applications and the subjective evaluation. Moreover, this scheme locates the edge boundaries to the sub-pixel accuracy, which is an advantage to applications such as data hiding and image watermarking.

A Non-Canonical Linearly Constrained Constant Modulus Algorithm for a Blind Multiuser Detector

We investigate an alternative blind adaptive multiuser detection scheme based on a non-canonical linearly constrained constant modulus (LCCM) criterion and prove that, under the constrained condition, the non-canonical linearly constrained constant modulus algorithm (LCCMA) can completely remove multiple-access interference. We further demonstrate that the non-canonical LCCM criterion function is strictly convex in the noise-free state, and that under the constrained condition, it is also strictly convex even where small noise is present. We present a simple method for selecting the constant as well as a stochastic gradient algorithm for implementing our scheme. Numerical simulation results verify the scheme's efficiency.

Multiple Model Adaptive Estimation for Multiuser Detection in CDMA Communication

This paper introduces a blind adaptive multiuser detection scheme for code division multiple acces (CDMA) communication, based on the interacting multiple model algorithm. The system estimates the received power and input data sequence which are corrupted by multiple access interference (MAI) and noise. A hybrid system is used to model the synchronous code division multiple access, and multiuser detection is implemented using the interacting multiple model (IMM) technique. The performance of the IMM based multiuser detector is studied and simulations are used to compare with the decorrelating decision feedback detector (DDFD) and decision-feedback blind adaptive multiuser detector (DFBD).

Adaptive recursive algorithm for complementary subspace-based blind multiuser detection

A novel blind adaptive multiuser detection scheme, the linear decorrelating detector (LDD), based on a concept of the complementary subspace is proposed. Under this scheme, the information bits can be estimated from the received signal with the prior knowledge of the desired user's signature waveform and its timing. The technique has two key elements: a geometric notion of the LDD and the subspace-based concept to estimate the orthogonal complement of the interfering space. It is then shown that the LDD can be formulated in terms of the interference space constructed from the received signal, and the desired user's signature waveform. The interference space components can be adaptively estimated using the modified power-based methods with only O((K-1) N) flops of computation at each iteration, where N is the processing gain, and K is the number of active users in the channel. The novel blind adaptive detector offers lower computational complexity. The proposed blind multiuser detection offers the optimal near-far resistance, low computational complexity, and resistance against the orthogonality error of the estimated interference space.