Forward-backward Spatial Smoothing Research Articles

Combination of weighted ℓ2,1 minimization with unitary transformation for DOA estimation

Using the centro-symmetry property of uniform linear array (ULA), we propose an algorithm that combines the weighted ℓ2,1 minimization with the unitary transformation to improve the performance of DOA estimation. Exploiting the result of the unitary transformation, more credible weights can be obtained and the jointly sparse constraint can be further enhanced. Moreover, the unitary transformation incorporates the forward–backward spatial smoothing, which improves the performance of the weighted ℓ2,1 minimization for correlated sources. Simulations demonstrate that the proposed method can achieve better performance in terms of resolution and estimation accuracy.

A Low-Complexity Algorithm for Coherent DOA Estimation in Monostatic MIMO Radar

This paper investigates the topic of direction of arrival (DOA) estimation for coherent sources in monostatic multi-input multi-output (MIMO) radar, and proposes a low-complexity algorithm for coherent DOA estimation. The direction vector of MIMO radar can be firstly mapped into a vector of virtual uniform linear array (ULA), and after that, a linear operator is constructed by partial cross-correlations from the received data of the virtual ULA. Finally, the DOAs can be obtained via roots finding method based on this linear operator. The DOAs can be estimated without any eigen-decomposition, nor evaluating all correlations of the received data. The proposed algorithm has much lower complexity as well as much better angle estimation performance than conventional forward backward spatial smoothing (FBSS)-propagator method (FBSS-PM), FBSS- estimation method of signal parameters via rotational invariance techniques (FBSS-ESPRIT), FBSS- root multiple signal classification (FBSS-Root MUSIC), and ESPRIT-like algorithm. Simulations present the effectiveness and improvement of our approach.

Antenna array multipath mitigation method in navigation augmentation system

This paper presents the antenna array multipath mitigation method in the satellite navigation augmentation system. The digital receiver with multiple correlators is proposed. The antenna elements of the array each output the raw sampled digital data to every internal receiver channel. The carrier phase of the satellite is correlated out in the channel, and the direction of arrival is estimated, then the digital beamforming is operated to mitigate the multipath. The direction of arrival estimation and the decorrelation method of the correlated signal of the three dimensional array are researched in the paper. A three dimensional spheriform array with current dipoles is proposed, receiving the correlated signals. The three dimensional array proposed is compact with small volume. The linear arrays on X axis, Y axis and Z axis adopt the forward-backward spatial smoothing technique to decorrelate the signals respectively. The direction of arrival is estimated by MUSIC, and there is no matching needed. Distinguish the direct satellite signal by the result of demodulated navigation message. In the simulation, the direction of the direct satellite signal and the multipath signals estimated are used to optimize the weights to apply the beamforming to the desired satellite, and place a null on the estimated multipath direction.

Preestimation-Based Array Interpolation Approach to Coherent Source Localization Using Multiple Sparse Subarrays

In application to coherent source localization using multiple sparse uniform linear subarrays (ULSAs), the array interpolation technique with Lau or Pesavento formulation causes unacceptable interpolation errors over the entire field of view because of high sparseness of ULSAs. In this letter, we propose a modified array interpolation approach by specifying a union of nonoverlapping narrow subsectors as the in-sector to cover only the source locations preestimated roughly on an assumption that at least a single ULSA is available for coherent source localization. Furthermore, we skip noise prewhitening and employ more subarrays of the virtual uniform linear array (VULA) for forward-backward spatial smoothing (FBSS) that plays a key role in noise floor reduction as well as coherent source decorrelation.

DOA Estimation for Coherent Sources in Unknown Nonuniform Noise Fields

A new algorithm, called generalized covariance differencing (GCD), is proposed for direction of arrival (DOA) estimation in the presence of coherent sources and unknown nonuniform noise. By using the difference between the forward-backward spatial smoothing matrix and its complex conjugation, the GCD method constructs a generalized covariance matrix, which can fully eliminate spatially nonuniform noise and fit for more general noise fields and low signal-to-noise ratio (SNR) environments. In addition, it can also resolve coherent and incoherent sources. The GCD covariance matrix is an imaginary-valued matrix which can reduce computational burden. By analyzing the characteristic of GCD, a rapid root-MUSIC method without eigendecomposition is also given which can further reduce computational complexity. Simulation results demonstrate that the GCD method can effectively eliminate nonuniform noise, and it possesses a lower SNR threshold as well as a smaller variance than that of conventional methods.

2-D spatial smoothing for multipath coherent signal separation

The use of two-dimensional spatial smoothing (SS) to increase channel capacity of wireless communications system is discussed, The necessary and sufficient conditions on array configuration for applying SS to multiple signal classification (MUSIC), and adaptive beamforming algorithms are defined and proved, This array must have an orientational invariance structure with an ambiguity free center array, and the number of subarrays must be larger than or equal to the size of the largest group of coherent signals. We also studied the cause of ambiguities in a multipath environment. We found the necessary and sufficient conditions for a three-sensor array manifold to be ambiguity free and identified several higher order ambiguity situations, If an array is also central symmetric, the forward/backward SS (FBSS) can be used to improve the resolution. Finally, we extended our results to the estimation of signal parameters via rotational invariance techniques (ESPRIT). All the predicted results are verified by simulations.

Performance of spatial smoothing algorithms for correlated sources

The problem of identifying the angles of arrival of a set of plane waves impinging on a narrowband array of sensors and related spectral analysis problems have been addressed with a large number of algorithms. One of the most popular techniques is the multiple signal classification (MUSIC). The major shortcoming of the MUSIC algorithm is that it performs poorly when the sources are highly correlated. Fortunately, two algorithms exist to overcome this problem-spatial smoothing (SS) and forward-backward spatial smoothing (FBSS). The performance of the SS technique depends on signal bearings and spatial separation. For the same smoothing, FBSS can offer improved performance, but this depends on the signal phases. Numerical results for the variance of the algorithms are given to illustrate the points made.