Rui Fa

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

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 LCMV beamforming algorithms based on joint iterative optimization of filters: Design and analysis

This paper presents reduced-rank linearly constrained minimum variance (LCMV) beamforming algorithms based on joint iterative optimization of filters. The proposed reduced-rank scheme is based on a constrained joint iterative optimization of filters according to the minimum variance criterion. The proposed optimization procedure adjusts the parameters of a projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe LCMV expressions for the design of the projection matrix and the reduced-rank filter. We then describe stochastic gradient and develop recursive least-squares adaptive algorithms for their efficient implementation along with automatic rank selection techniques. An analysis of the stability and the convergence properties of the proposed algorithms is presented and semi-analytical expressions are derived for predicting their mean squared error (MSE) performance. Simulations for a beamforming application show that the proposed scheme and algorithms outperform in convergence and tracking the existing full-rank and reduced-rank algorithms while requiring comparable complexity.

Reduced-Rank STAP Algorithms using Joint Iterative Optimization of Filters

We develop a reduced-rank space-time adaptive processing (STAP) method based on joint iterative optimization of filters (JOINT) for airborne radar applications. The proposed method consists of a bank of full-rank adaptive filters, which forms the projection matrix, and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe the proposed method for both the direct-form processor (DFP) and the generalized sidelobe canceller (GSC) structures. Adaptive algorithms including the stochastic gradient (SG), the recursive least square (RLS), and their hybrid algorithms are derived for the efficient implementation of the JOINT STAP method. The computational complexity analysis of the proposed algorithms is shown in terms of the number of multiplications and additions per snapshot. Furthermore, the convexity analysis of the proposed method is carried out. Simulations for a clutter-plus-jamming suppression application show that the proposed STAP algorithm outperforms the state-of-the-art reduced-rank schemes in convergence and tracking at significantly lower complexity.

Multi-branch successive interference cancellation for MIMO spatial multiplexing systems: Design, analysis and adaptive implementation

In this study, the authors propose a novel successive interference cancellation (SIC) strategy for multiple-input multiple-output spatial multiplexing systems based on a structure with multiple interference cancellation branches. The proposed multi-branch SIC (MB-SIC) structure employs multiple SIC schemes in parallel and each branch detects the signal according to its respective ordering pattern. By selecting the branch which yields the estimates with the best performance according to the selection rule, the MB-SIC detector, therefore, achieves higher detection diversity. The authors consider three selection rules for the proposed detector, namely, the maximum likelihood (ML), the minimum mean square error and the constant modulus criteria. An efficient adaptive receiver is developed to update the filter weight vectors and estimate the channel using the recursive least squares algorithm. Furthermore a bit error probability performance analysis is carried out. The simulation results reveal that the authors scheme successfully mitigates the error propagation and approaches the performance of the optimal ML detector, while requiring a significantly lower complexity than the ML and sphere decoder detectors.

Multi-Branch Successive Interference Cancellation for MIMO Spatial Multiplexing Systems

In this paper we propose a novel successive interference cancellation (SIC) strategy for multiple-input multiple-output (MIMO) spatial multiplexing systems based on multiple interference cancellation branches. The proposed detection structure employs SICs on several parallel branches which are equipped with different ordering patterns so that each branch produces a symbol estimate vector by exploiting a certain ordering pattern. The novel detector, therefore, achieves higher detection diversity by selecting the branch which yields the estimates with the best performance according to the selection rule. We consider three selection rules for the proposed detector, namely, maximum likelihood (ML), minimum mean square error (MMSE), constant modulus (CM) criteria. The simulation results reveal that our scheme successfully mitigates the error propagation and approaches the performance of the optimal ML detector, while requiring a significantly lower complexity than the ML detector.

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

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 R̂-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 R̂-1. Simulations show the potential of our proposed algorithm, which obtain substantial performance improvements over prior art.

Reduced-rank STAP algorithm for adaptive radar based on joint iterative optimization of adaptive filters

In this paper, we develop a novel reduced-rank space-time adaptive processing (STAP) algorithm based on joint iterative optimization of filters for adaptive radar applications. The proposed algorithm consists of a joint iterative optimization of a bank of full-rank adaptive filters that forms the projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe constrained minimum variance (CMV) expressions for the design of the projection matrix and the reduced-rank filter. Adaptive algorithms including normalized least-mean-squares (NLMS) and recursive least square (RLS) are derived for its efficient implementation. Simulations for a clutter-plus-jamming suppression application show that the proposed STAP algorithm outperforms the state-of-the-art reduced-rank schemes in convergence and tracking at significantly lower complexity.

Multi-Feedback Successive Interference Cancellation with Multi-Branch Processing for MIMO Systems

In this paper, a new successive interference cancellation (SIC) strategy for multiple-input multiple output (MIMO) spatial multiplexing systems is developed to combat the error propagation (EP) in decision feedback systems. The proposed scheme employs a parallel multi-branch (MB) structure. Each branch employs a SIC with multi-feedback (MF) strategy to detects the signals according to their respective ordering pattern. The MF-SIC scheme considers the feedback diversity by using a number of selected constellation points as the feedback set if a previous decision is considered unreliable. The shadow area constraint (SAC) is proposed to reduce the computational complexity by avoiding redundant MF processing with reliable decisions. The MB-MF-SIC achieves a higher detection diversity by selecting the branch which yields the signal estimates with the best performance according to the maximum likelihood rule. The simulation results show that the MB-MF-SIC scheme successfully mitigates the EP and approaches the ML performance while requiring lower complexity than sphere decoders.

Reduced-rank STAP for MIMO radar based on joint iterative optimization of knowledge-aided adaptive filters

MIMO radar has received significant attention in the past five years. In this paper, we focus on the advantage of MIMO radars in achieving better spatial resolution by employing the colocated antennas and propose a reduced-rank knowledge-aided technique for MIMO radar space-time adaptive processing (STAP) design. The scheme is based on joint iterative optimization of knowledge-aided adaptive filters (JIOKAF) and takes advantage of the prior environmental knowledge by employing linear constraint techniques. 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, in comparison with the state-of-the-art reduced-rank algorithms. Simulations show that the proposed algorithm outperforms existing reduced-rank algorithms.

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

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.