Showing papers by "Rodrigo C. de Lamare published in 2010"

Reduced-Rank STAP Schemes for Airborne Radar Based on Switched Joint Interpolation, Decimation and Filtering Algorithm

Abstract: In this paper, we propose a reduced-rank space-time adaptive processing (STAP) technique for airborne phased array radar applications. The proposed STAP method performs dimensionality reduction by using a reduced-rank switched joint interpolation, decimation and filtering algorithm (RR-SJIDF). In this scheme, a multiple-processing-branch (MPB) framework, which contains a set of jointly optimized interpolation, decimation and filtering units, is proposed to adaptively process the observations and suppress jammers and clutter. The output is switched to the branch with the best performance according to the minimum variance criterion. In order to design the decimation unit, we present an optimal decimation scheme and a low-complexity decimation scheme. We also develop two adaptive implementations for the proposed scheme, one based on a recursive least squares (RLS) algorithm and the other on a constrained conjugate gradient (CCG) algorithm. The proposed adaptive algorithms are tested with simulated radar data. The simulation results show that the proposed RR-SJIDF STAP schemes with both the RLS and the CCG algorithms converge at a very fast speed and provide a considerable SINR improvement over the state-of-the-art reduced-rank schemes.

Adaptive Reduced-Rank Constrained Constant Modulus Algorithms Based on Joint Iterative Optimization of Filters for Beamforming

Abstract: This paper proposes a robust reduced-rank scheme for adaptive beamforming based on joint iterative optimization (JIO) of adaptive filters. The novel scheme is designed according to the constant modulus (CM) criterion subject to different constraints. The proposed scheme consists of a bank of full-rank adaptive filters that forms the transformation matrix, and an adaptive reduced-rank filter that operates at the output of the bank of filters to estimate the desired signal. We describe the proposed scheme for both the direct-form processor (DFP) and the generalized sidelobe canceller (GSC) structures. For each structure, we derive stochastic gradient (SG) and recursive least squares (RLS) algorithms for its adaptive implementation. The Gram-Schmidt (GS) technique is applied to the adaptive algorithms for reformulating the transformation matrix and improving the performance. An automatic rank selection technique is developed and employed to determine the most adequate rank for the derived algorithms. A detailed complexity study and a convexity analysis are carried out. Simulation results show that the proposed algorithms outperform the existing full-rank and reduced-rank methods in convergence and tracking performance.

Adaptive Constrained Constant Modulus Algorithm Based on Auxiliary Vector Filtering for Beamforming

Abstract: A constrained constant modulus (CCM) algorithm with the auxiliary vector filtering (AVF) technique is introduced for robust adaptive beamforming. The proposed scheme decomposes the adaptive filter into constrained (reference vector filters) and unconstrained (auxiliary vector filters) components. The weight vector is iterated by subtracting the scaling auxiliary vector from the reference vector, which are computed according to the CCM criterion. The proposed algorithm provides an iterative exchange of information between the scalar factor and the auxiliary vector, resulting in a faster convergence and an improved steady-state performance as compared with existing techniques with large filters. The convergence properties of the proposed algorithm are analyzed. Simulation results show that the proposed beamforming algorithm outperforms existing techniques and is robust against signature mismatch problems.

Knowledge-aided STAP algorithm using convex combination of inverse covariance matrices for heterogenous clutter

Abstract: Knowledge-aided space-time adaptive processing (KA-STAP) algorithms, which incorporate a priori knowledge into radar signal processing methods, have the potential to substantially enhance detection performance while combating heterogeneous clutter effects. In this paper, we develop a KA-STAP algorithm to estimate the inverse interference covariance matrix rather than the covariance matrix itself, by combining the inverse of the covariance known a priori, R0−1, and the inverse sample covariance matrix estimate R−1. The computational load is greatly reduced due to the avoidance of the matrix inversion operation. We also develop a cost-effective algorithm based on the minimum variance (MV) criterion for computing the mixing parameter that performs a convex combination of R0−1 and R−1. Simulations show the potential of our proposed algorithm, which obtain substantial performance improvements over prior art.

Reduced-rank DOA estimation based on joint iterative subspace recursive optimization and grid search

Abstract: In this paper, we propose a reduced-rank direction of arrival (DOA) estimation algorithm based on joint and iterative subspace optimization (JISO) with grid search . The reduced-rank scheme includes a rank reduction matrix and an auxiliary reduced-rank parameter vector. They are jointly and iteratively optimized with a recursive least squares algorithm (RLS) to calculate the output power spectrum. The proposed JISO-RLS DOA estimation algorithm provides an efficient way to iteratively estimate the rank reduction matrix and the auxiliary reduced-rank vector. It is suitable for DOA estimation with large arrays and can be extended to arbitrary array geometries. It exhibits an advantage over MUSIC and ESPRIT when many sources exist in the system. A spatial smoothing (SS) technique is employed for dealing with highly correlated sources. Simulation results show that the JISO-RLS has a better performance than existing Capon and subspace-based DOA estimation methods.

Joint iterative power allocation and interference suppression algorithms for cooperative spread spectrum networks

Abstract: This work presents joint iterative power allocation and interference suppression algorithms for spread spectrum networks with multiple relays and the amplify and forward cooperation strategy. A joint constrained optimization framework that considers the allocation of power levels across the relays subject to individual and global power constraints and the design of linear receivers for interference suppression is proposed. Constrained minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the parameters of the linear receivers are derived. In order to solve the proposed optimization problems efficiently, stochastic gradient (SG) algorithms for adaptive joint iterative power allocation, and receiver and channel parameter estimation are developed. The results of simulations show that the proposed algorithms obtain significant gains in performance and capacity over existing cooperative and non-cooperative schemes.

Joint Iterative Power Allocation and Interference Suppression Algorithms for Cooperative DS-CDMA Networks

Abstract: This work presents joint iterative power allocation and interference suppression algorithms for DS-CDMA networks which employ multiple relays and the amplify and forward cooperation strategy. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to individual and global power constraints and the design of linear receivers for interference suppression. We derive constrained minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the parameters of the linear receivers. In order to solve the proposed optimization problems efficiently, we develop recursive least squares (RLS) algorithms for adaptive joint iterative power allocation, and receiver and channel parameter estimation. Simulation results show that the proposed algorithms obtain significant gains in performance and capacity over existing schemes.

Low-Complexity Channel Estimation for Cooperative Wireless Sensor Networks Based on Data Selection

Abstract: In this paper, we consider a general cooperative wireless sensor network (WSN) and the problem of channel estimation. We develop a matrix-based set-membership normalized least mean squares (SM-NLMS) algorithm for the estimation of the complex channel parameters in order to reduce the computational complexity significantly and extend the lifetime of the WSN by reducing its power consumption. The proposed SM-NLMS channel estimation method requires the setting of a bound for appropriate performance. However, an inappropriate and fixed error bound will result in overbounding and underbounding problems which degrade the performance significantly. Therefore, we present and incorporate an error bound function into the SM-NLMS channel estimation method which can adjust the error bound automatically with the update of the channel estimates. Computer simulations show good performance of our proposed algorithms in terms of convergence speed and steady state, reduced complexity and robustness to the time-varying environment and different signal-to-noise ratio (SNR) values.

Knowledge-aided reduced-rank STAP for MIMO radar based on joint iterative constrained optimization of adaptive filters with multiple constraints

Abstract: In this paper, a reduced-rank knowledge-aided technique for MIMO radar space-time adaptive processing (STAP) design is proposed. We focus on the advantage of MIMO radars in achieving better spatial resolution by employing the colocated antennas. The scheme is based on knowledge-aided constrained joint iterative optimization of adaptive filters (KAC-JIOAF) and takes advantage of the a priori covariance matrix by employing additional linear constraints in the design. A recursive least squares (RLS) implementation is derived to reduce the computational complexity. We evaluate the algorithm in terms of signal-to-interference-plus-noise ratio (SINR) and probability of detection PD performance and compare it with the state-of-the-art reduced-rank algorithms. Simulations show that the proposed algorithm outperforms existing reduced-rank algorithms.

Multiple feedback successive interference cancellation with shadow area constraints for MIMO systems

Abstract: In this paper, we propose a novel low-complexity, multiple feedback successive interference cancellation (SIC) strategy for uncoded multiple-input multiple output (MIMO) spatial multiplexing systems. Since the complexity of the ML and the existing near ML algorithms such as the sphere decoder (SD) is still high in systems with bad channel conditions and/or low signal-noise ratio (SNR), there is a need for flexible and cost effective MIMO detectors. In the proposed multiple feedback successive interference cancellation with shadow area constraints (MF-SIC-SAC) algorithm, feedback diversity (FD) is introduced to combat the error propagation effect in decision feedback systems and achieve a close to ML performance. For our scheme, the computational complexity is as low as the SIC algorithm with very low additional complexity added. Simulation results show that the proposed detection significantly outperforms the conventional SIC scheme while maintaining a low detection complexity. The SNR has about 0.5dB degradation at the target bit error rate BER = 0.001 compared with the MLD with QPSK modulation.

BEACON channel estimation for cooperative wireless sensor networks based on data selection

Abstract: In this paper, we consider a general cooperative wireless sensor network (WSN) and the problem of channel estimation. A matrix-based set-membership recursive least squares (RLS) algorithm called BEACON is developed for the estimation of the complex channel parameters in order to reduce the computational complexity significantly and extend the lifetime of the WSN by reducing its power consumption. Then, we present and incorporate an error bound function into the BEACON channel estimation method which can adjust the error bound automatically with the update of the channel estimates. Computer simulations show good performance of our proposed algorithms in terms of convergence speed and steady state mean square error, reduced complexity and robustness to the time-varying environment and different signal-to-noise ratio (SNR) values.

Robust auxiliary vector filtering algorithm based on constrained constant modulus design for adaptive beamforming

Abstract: This paper proposes an auxiliary vector filtering (AVF) algorithm based on a constrained constant modulus (CCM) design for robust adaptive beamforming. This scheme provides an efficient way to deal with filters with a large number of elements. The proposed beamformer decomposes the adaptive filter into a constrained (reference vector filters) and an unconstrained (auxiliary vector filters) components. The weight vector is iterated by subtracting the scaling auxiliary vector from the reference vector. The scalar factor and the auxiliary vector depend on each other and are jointly calculated according to the CCM criterion. The proposed robust AVF algorithm provides an iterative exchange of information between the scalar factor and the auxiliary vector and thus leads to a fast convergence and an improved steady-state performance over the existing techniques. Simulations are performed to show the performance and the robustness of the proposed scheme and algorithm in several scenarios.

Multi-branch MMSE decision feedback detection algorithms with error propagation mitigation for MIMO systems

Abstract: In this work we propose novel decision feedback (DF) detection algorithms with error propagation mitigation capabilities for multi-input multi-output (MIMO) spatial multiplexing systems based on multiple processing branches. The novel strategies for detection exploit different patterns, orderings and constraints for the design of the feedforward and feedback filters. We present constrained minimum mean-squared error (MMSE) filters designed with constraints on the shape and magnitude of the feedback filters for the multi-branch MIMO receivers and show that the proposed MMSE design does not require a significant additional complexity over the single-branch MMSE design. The proposed multi-branch MMSE DF detectors are compared with several existing detectors and are shown to achieve a performance close to the optimal maximum likelihood detector while requiring significantly lower complexity.

An iterative Widely Linear interference suppression algorithm based on Auxiliary Vector Filtering

Abstract: We introduce a new Widely Linearly (WL) framework that combines the WL filter with the Auxiliary Vector Filtering (AVF) technique for non-circular signals. Moreover, an adaptive interference suppression algorithm is developed for a high data rate Direct Sequence Ultra Wideband (DS-UWB) system. The proposed algorithm exploits the second-order behavior of the received signal and takes full advantage of the improper property nature of the non-circular data. It utilizes an iterative procedure to update the WL weight vector. The key properties of the proposed algorithm corresponding to the WL processor are analyzed. Simulation results are provided to show the superior performance of the proposed algorithm over its linear counterpart and conventional linear/WL Minimum Mean Square Error (MMSE) adaptive algorithms.

Low-Complexity Reduced-Rank Interference Mitigation Algorithms for DS-UWB Systems

Abstract: We consider a two-stage framework for linear interference mitigation, in which a transformation performs dimensionality reduction followed by a reduced-rank filter. A generic reduced-rank scheme that jointly optimizes the transformation and the reduced-rank filter by using the minimum mean squared error (MMSE) criterion is investigated. Then, we impose constraints on the design of the transformation and propose the switched approximations of adaptive basis functions (SAABF) scheme, in which the transformation is chosen instantaneously from a set of mapping matrices and adaptive basis functions. Least-mean squares (LMS) algorithms and model-order selection algorithms are also proposed. A complexity analysis shows that the proposed scheme is significantly simpler than the existing reduced-rank schemes. Simulations show remarkable interference mitigation performance in direct-sequence ultra-wideband (DS-UWB) systems.

Adaptive Detector for SC-FDE in Multiuser DS-UWB Systems Based on Structured Channel Estimation with Conjugate Gradient Algorithm

Abstract: In this work, we propose a conjugate gradient (CG) based structured channel estimation (SCE) scheme for single-carrier frequency domain equalization (SC-FDE) in multiuser direct-sequence ultra-wideband (DS-UWB) systems. The minimum mean square error (MMSE) linear detection strategy is used and a cyclic prefix is employed. We perform the adaptive channel estimation in the frequency domain and implement the despreading in the time domain after the FDE. In this scheme, the linear MMSE detection requires the knowledge of the number of users and the noise variance. For this purpose, we propose algorithms for estimating these parameters. A CG adaptive algorithm is then developed for the SCE scheme. With lower complexity than the recursive least squares (RLS) algorithm and better performance than the Least mean squares (LMS) algorithm, the SCE-CG achieves a better tradeoff between the complexity and the performance.

Adaptive reduced-rank interference suppression for DS-UWB systems based on the Widely Linear Multistage Wiener Filter

Abstract: We propose a Widely Linear Multistage Wiener Filter (WL-MSWF) receiver to suppress the multi-user interference and the inter/intra-symbol interference in a high data rate Direct Sequence Ultra Wideband (DS-UWB) system. To this end, a non-circular modulation, e.g., Binary Phase Shift Keying (BPSK), is applied. We develop reduced-rank versions of the Least Mean Square (LMS) and the Recursive Least Squares (RLS) algorithms based on the theory of the WL-MSWF. The convergence performance of the proposed WL-MSWF algorithms is shown via simulations in a line-of-sight office scenario and then compared to the linear estimation counterparts. In order to reduce the power consumption due to the high-speed and high-resolution Analog-to-Digital Converters (ADCs), we also evaluate the performance of the WL-MSWF algorithms when a one-bit ADC is employed. The proposed WL-MSWF receiver does not only have a lower complexity compared to the full-rank scheme, but also exhibits a superior performance to the corresponding linear receiver employing the existing MSWF.

Adaptive reduced-rank LCMV beamforming algorithm based on the set-membership filtering framework

Abstract: This paper proposes a new adaptive algorithm for the implementation of the linearly constrained minimum variance (LCMV) beamformer. The proposed algorithm utilizes the set-membership filtering (SMF) framework and the reduced-rank joint iterative optimization (JIO) scheme. We develop a stochastic gradient (SG) based algorithm for the beamformer design. An effective time-varying bound is employed in the proposed method to adjust the step sizes, avoid the misadjustment and the risk of overbounding or underbounding. Simulations are performed to show the improved performance of the proposed algorithm in comparison with existing full-rank and reduced-rank methods.

An adaptive LCMV beamforming algorithm based on dynamic selection of constraints

Abstract: A novel approach to linearly constrained minimum variance (LCMV) beamforming based on dynamic selection of constraints (DSC) is proposed. The method employs a multiple parallel processors (MPP) framework, where each processor is optimized subject to a particular linear constraint. A selection criterion is employed at the output of the scheme to select the best processor for each time instant. We also present low-complexity stochastic gradient (SG) and recursive least squares (RLS) adaptive algorithms for the efficient implementation of the proposed scheme. Furthermore, a complexity analysis of the proposed and existing schemes in terms of the number of multiplications and additions is carried out. Simulations results with a uniform linear array (ULA) model show that the proposed scheme and algorithms significantly improve the performance of conventional LCMV beamforming.

Reduced-rank STAP for airborne radar based on switched joint interpolation, decimation and filtering algorithm

Abstract: We present an adaptive reduced-rank signal processing technique for airborne phased array radar applications. The proposed method performs dimensionality reduction by using a reduced-rank switched joint interpolation, decimation and filtering algorithm (RR-SJIDF). A multiple-processing-branch (MPB) framework, which contains a set of jointly optimized interpolation, decimation and filtering units, is employed to process the observations. The output is switched to the branch with the best performance among the available ones. In order to design the decimation unit, we present the optimal decimation scheme and also a low-complexity decimation algorithm. We then develop a low-complexity recursive least squares (RLS) algorithm for the proposed scheme. Simulations results show that the proposed RR-SJIDF STAP algorithm converges at a very fast speed and provides a considerable signal-to-interference-plusnoise-ratio (SINR) performance improvement over the state-ofthe-art reduced-rank schemes.

Blind joint iterative optimization reduced-rank adaptive receiver for DS-UWB systems based on constrained constant modulus criterion

Abstract: A novel linear blind reduced-rank scheme based on the constrained constant modulus (CCM) design criterion is proposed for interference suppression in direct-sequence ultra-wideband (DS-UWB) systems. The proposed two-stage blind receiver consists of a projection matrix that performs dimensionality reduction and is followed by a reduced-rank filter that produces the output. The projection matrix and the reduced-rank filter are obtained with a joint-iterative optimization (JIO) that minimize the constant modulus (CM) cost function subject to a constraint. Low complexity adaptive implementation is achieved by developing normalized stochastic gradient (NSG) algorithms. Simulation results show that the proposed scheme has excellent performance in suppressing the inter-symbol interference (ISI) and multiple access interference (MAI) with low complexity.

Adaptive reduced-rank least squares beamforming algorithm based on the set-membership framework

Abstract: This paper presents a new adaptive algorithm for the linearly constrained minimum variance (LCMV) beamformer design. We incorporate the set-membership filtering (SMF) mechanism into the reduced-rank joint iterative optimization (JIO) scheme to develop a constrained recursive least squares (RLS) based algorithm called JIO-SM-RLS. The proposed algorithm inherits the positive features of reduced-rank signal processing techniques to enhance the output performance, and utilizes the data-selective updates (around 10–15%) of the SMF methodology to save the computational cost significantly. An effective time-varying bound is imposed on the array output as a constraint to circumvent the risk of overbounding or underbounding, and to update the parameters for beamforming. The updated parameters construct a set of solutions (a membership set) that satisfy the constraints of the LCMV beamformer. Simulations are performed to show the superior performance of the proposed algorithm in terms of the convergence rate and the reduced computational complexity in comparison with the existing methods.

Reduced-rank BEACON algorithm based on joint iterative optimization of adaptive filters

Abstract: This paper presents a novel reduced-rank implementation of the set-membership (SM) Bounding Ellipsoid Adaptive Constrained Least Squares (BEACON) algorithm using the method of joint iterative optimization (JIO) of adaptive filters. The sparsely updating estimation error SM framework is applied to the adaption of the rank reduction matrix and the symbol estimation filter that operates in the reduced-rank signal subspace. A derivation of the scheme, based on least squares (LS) optimization, is given along with an intuitive geometric interpretation of the update procedure. The proposed JIO-BEACON algorithm along with other established reduced-rank algorithms are applied to interference suppression in a multiuser DS-CDMA system. The JIO-BEACON algorithm is shown to exceed the performance of the standard LS JIO and other existing algorithms in terms of convergence and steady state error whilst achieving significant complexity savings.

Linear precoding based on switched interleaving and limited feedback for interference suppression in downlink multi-antenna MC-CDMA systems

Abstract: In this work, a new hybrid transmit processing technique based on switched interleaving and chip-wise precoding is proposed to suppress the multiuser interference (MUI) for downlink multi-carrier code division multiple access (MC-CDMA) multiple antenna systems. A set of possible chip-interleavers are constructed and prestored at both the base station (BS) and mobile stations (MSs), which are also equipped with another codebook of quantized downlink channel state information (CSI). Each MS quantizes its own downlink CSI and feeds back the index to the BS by a low-rate feedback channel, then the selection function at the BS determines the optimum interleaver based on all users' quantized CSIs to transmit signals. Simulation results show that the performance of the proposed techniques is significantly better than prior art.

Switched interleaving turbo codes with transmission of side information for short blocks

Abstract: In this paper, we describe a novel turbo encoding and iterative decoding scheme based on the concept of switched interleaving and transmission of side information. The proposed system consists of an innovative structure for turbo encoding that allows the refinement of the code words produced with the use of a codebook with different and appropriately designed interleavers. We also develop two search procedures to infer the best interleaver in the codebook with the aid of side information containing the index of the interleaver sent to the decoder via a novel embedded transmission. At the decoder the selected interleaver is employed in the decoding procedure. The proposed system allows a significant refinement and improvement of conventional turbo codes, resulting in considerable performance improvements for short blocks and lower error floors than the existing state-of-the-art techniques.

Joint model-order and step-size adaptation with convex combinations of reduced-rank adaptive filters

Abstract: In this work we propose schemes for joint modelorder and step-size adaptation of reduced-rank adaptive filters. The proposed schemes employ reduced-rank adaptive filters in parallel operating with different orders and step sizes, which are exploited by convex combination strategies. The reduced-rank adaptive filters used in the proposed schemes are based on a joint and iterative decimation and interpolation (JIDF) method recently proposed. The unique feature of the JIDF method is that it can substantially reduce the number of coefficients for adaptation, thereby making feasible the use of multiple reduced-rank filters in parallel. We investigate the performance of the proposed schemes in an interference suppression application for CDMA systems. Simulation results show that the proposed schemes can significantly improve the performance of the existing reduced-rank adaptive filters based on the JIDF method.

Switched interleaving techniques with limited feedback for interference mitigation in Uplink Multi-antenna MC-CDMA Systems

Abstract: In this work a novel switched-interleaving algorithm based on limited feedback is proposed to suppress interference for uplink multi-carrier code-division multiple-access (MC-CDMA) multiple antenna systems. A codebook of chip-inter leavers is constructed and prestored at both the base station (BS) and mobile stations (MSs). The transmit chip-interleaver is chosen by the BS from the codebook, and the index of the interleaver is fed back using a limited number of bits. The optimum interleaving pattern is chosen by the selection function of the sum received signal to interference plus noise ratio (SINR). We describe three methods to design the codebook of interleavers. Simulation results show that the performance of the proposed techniques is significantly better than prior art.