Siliang Wu

Low-complexity direction-of-arrival estimation based on wideband co-prime arrays

A class of low-complexity compressive sensing-based direction-of-arrival (DOA) estimation methods for wideband co-prime arrays is proposed. It is based on a recently proposed narrowband estimation method, where a virtual array model is generated by directly vectorizing the covariance matrix and then using a sparse signal recovery method to obtain the estimation result. As there are a large number of redundant entries in both the auto-correlation and cross-correlation matrices of the two sub-arrays, they can be combined together to form a model with a significantly reduced dimension, thereby leading to a solution with much lower computational complexity without sacrificing performance. A further reduction in complexity is achieved by removing noise power estimation from the formulation. Then, the two proposed low-complexity methods are extended to the wideband realm utilizing a group sparsity based signal reconstruction method. A particular advantage of group sparsity is that it allows a much larger unit inter-element spacing than the standard co-prime array and therefore leads to further improved performance.

Extension of Co-Prime Arrays Based on the Fourth-Order Difference Co-Array Concept

An effective sparse array extension method for maximizing the number of consecutive lags in the fourth-order difference co-array is proposed, leading to a novel enhanced sparse array structure based on co-prime arrays (CPAs) with significantly increased number of degrees of freedom (DOFs). One method to exploit the increased DOFs based on nonstationary signals is also proposed, with simulation results provided to demonstrate the effectiveness of the proposed structure.

Joint Transmission and Reception Diversity Smoothing for Direction Finding of Coherent Targets in MIMO Radar

The direction estimation problem of coherent targets in multiple-input multiple-output (MIMO) radar systems is studied and a scheme with joint transmission and reception diversity smoothing is proposed. When both the transmitting and receiving antenna arrays are located closely in space, the new approach leads to much more available covariance matrices for spatial smoothing to decorrelate the coherent signals. As a result, a better estimation performance is achieved compared to the existing transmission diversity smoothing (TDS) method. It can also identify more coherent targets when sparse antenna arrays are employed. On the other hand, the proposed approach can be applied to joint direction of arrival (DOA) and direction of departure (DOD) estimation using existing direction estimation algorithms when the transmit and receive arrays are separated far away from each other (i.e. the bistatic case). Two specific methods are proposed under the scheme, one is based on forward-only (FO) spatial smoothing and one is based on forward-backward (FB) processing. Due to the increased number of covariance matrices for spatial smoothing, a further improved performance is achieved by the FB-based one.

Robust Capon beamforming against large DOA mismatch

In the presence of significant direction-of-arrival (DOA) mismatch, existing robust Capon beamformers based on the uncertainty set of the steering vector require a large size of uncertainty set for providing sufficient robustness against the increased mismatch. Under such circumstance, however, their output signal-to-interference-plus-noise ratios (SINRs) degrade. In this paper, a new robust Capon beamformer is proposed to achieve robustness against large DOA mismatch. The basic idea of the proposed method is to express the estimate of the desired steering vector corresponding to the signal of interest (SOI) as a linear combination of the basis vectors of an orthogonal subspace, then we can easily obtain the estimate of the desired steering vector by rotating this subspace. Different from the uncertainty set based methods, the proposed method does not make any assumptions on the size of the uncertainty set. Thus, compared to the uncertainty set based robust beamformers, the proposed method achieves a higher output SINR performance by preserving its interference-plus-noise suppression abilities in the presence of large DOA mismatch. In addition, computationally efficient online implementation of the proposed method has also been developed. Computer simulations demonstrate the effectiveness and validity of the proposed method.

Computationally efficient 2-D DOA estimation for uniform rectangular arrays

A computationally efficient two-dimensional (2-D) direction-of-arrival (DOA) estimation method for uniform rectangular arrays is presented. A preprocessing transformation matrix is first introduced, which transforms both the complex-valued covariance matrix and the complex-valued search vector into real-valued ones. Then the 2-D DOA estimation problem is decoupled into two successive real-valued one-dimensional (1-D) DOA estimation problems with real-valued computations only. All these measures lead to significantly reduced computational complexity for the proposed method.

Group sparsity based wideband DOA estimation for co-prime arrays

A novel wideband direction-of-arrival (DOA) estimation method is proposed for co-prime arrays. After decomposing the wideband signals into different frequencies/subbands through a discrete Fourier transform or, more generally, a filter bank system, the increased degrees of freedom provided by co-prime arrays are fully exploited with a group sparsity based signal reconstruction method. Simulation results show that this novel method can distinguish much more sources than the number of physical sensors. Compared with the existing narrowband DOA estimation method for co-prime arrays, the proposed wideband method achieves a significant performance improvement.

Extension of nested arrays with the fourth-order difference co-array enhancement

To reach a higher number of degrees of freedom by exploiting the fourth-order difference co-array concept, an effective structure extension based on two-level nested arrays is proposed. It increases the number of consecutive lags in the fourth-order difference coarray, and a virtual uniform linear array (ULA) with more sensors and a larger aperture is then generated from the proposed structure, leading to a much higher number of distinguishable sources with a higher accuracy. Compressive sensing based approach is applied for direction-of-arrival (DOA) estimation by vectorizing the fourth-order cumulant matrix of the array, assuming non-Gaussian impinging signals.

High-speed maneuvering target detection approach based on joint RFT and keystone transform

Increasing the integration time is an effective method to improve small maneuvering target detection performance in radar applications. However, range migration and Doppler spread caused by maneuvering target motion during the integration time make it difficult to improve the coherent accumulation of target’s energy and detection performance. In this study, a new method based on Radon Fourier transform (RFT) and keystone transform (KT) for high-speed maneuvering target detection is proposed. The proposed algorithm utilizes second-order KT to correct the range curvature, and the improved dechirping method to compensate for the Doppler spread. RFT is then used to correct the range walk for target coherent detection. The method is capable of correcting the range migration and the time-varied Doppler frequency of the target without knowing its velocity and acceleration. The advantage of the proposed method is that it can increase the coherent integration time and improve detection performance under the condition of Doppler frequency ambiguity. Compared with the second-order RFT algorithm, the computational burden of the proposed method is greatly reduced under the premise that the two methods have similar estimation accuracy of range, velocity and acceleration. Numerical experiments demonstrate the validity of the proposed algorithm.

Underdetermined wideband DOA estimation of off-grid sources employing the difference co-array concept

A wideband off-grid model is proposed to represent dictionary mismatch under the compressive sensing framework exploiting difference co-arrays. A group sparsity based off-grid method is proposed for underdetermined wideband direction of arrival (DOA) estimation which provides improved performance over the existing group sparsity based method with a same search grid. A two-step approach is then proposed which achieves an even better performance with significantly reduced computational complexity.

Low-complexity compressive sensing based DOA estimation for co-prime arrays

A low-complexity direction-of-arrival (DOA) estimation method is proposed based on the recently proposed co-prime array structure. In an existing method, a virtual array model is generated by directly vectorizing the covariance matrix and then a sparse signal recovery method is used to obtain the DOA estimation result. However, there are a large number of redundant entries in the covariance matrix and they can be combined together to form a model with a significantly reduced dimension, therefore leading to a solution with much lower computational complexity without sacrificing its performance. A further reduction in complexity is achieved by considering that the estimation result for noise power is far from its real value especially in scenarios with low input signal to noise ratio (SNR) and therefore can be removed from the formulation.