Enhanced "vector-cross-product" direction-finding using a constrained sparse triangular-array

Abstract

A new configuration of sparse array is proposed in this article to estimate the direction-of-arrivals (DOAs) and polarizations of multiple sources. This constrained sparse array is composed of a dipole-triad, a loop-triad, and a single antenna, which can be a dipole, a loop, or a scalar-sensor. These three units comprise a triangular geometry in the space. This geometry creatively synergizes the conventional interferometry method based on the spatial phase-delay across displaced antennas, and the "vector-cross-product" based on Poynting-vector estimator to enhance the DOA estimation accuracy. The investigated algorithm based on this configuration adopts the "vectorcross-product" DOA estimator to provide the coarse estimate and then derives the fine estimate by extracting the inter-sensor phase factors in the sparse array. Following this, the disambiguation approach is adapted to derive the unambiguous estimate, and this estimate is also fine in estimation resolution. The proposed configuration can extend the array aperture and also reduce the mutual coupling. The significant performance of the proposed sparse array composition is demonstrated by Monte Carlo simulations when the inter-sensor spacing far exceeds a half-wavelength.

A novel spatially spread electromagnetic vector sensor for high-accuracy 2-D DOA estimation

“…For most eigenstructure-based sensor-array parameter-estimation algorithms, the estimation of each incident source's steering vector is merely an intermediate step, as shown in Nehorai and Paldi (1994), Wong and Zoltowski (1997), Ko et al (2002), Yuan (2012b), Zoltowski and Wong (2000a, b), Wong and Zoltowski (2000a, b), Yuan et al (2012a, b), Yuan (2012c), Liu (2009), Yuan (2012d), Gu et al (2013), , Wong and Yuan (2011) and Luo and Yuan (2012). Thus, if those algorithms (not including Wong and Zoltowski 1997;Wong and Yuan 2011;Luo and Yuan 2012) are used to estimate the source's steering vector, the source can be modeled as zero-mean, complex Gaussian, random process or other realistic models.…”

“…The source model is also used in , Wong and Yuan (2011) and Luo and Yuan (2012). Note that this source model may not be interesting because different signals can be decomposed in the frequency domain.…”

“…Thus, if those algorithms (not including Wong and Zoltowski 1997;Wong and Yuan 2011;Luo and Yuan 2012) are used to estimate the source's steering vector, the source can be modeled as zero-mean, complex Gaussian, random process or other realistic models. The noise is supposed to be additive complex white Gaussian.…”

“…However, the algorithm investigated in and Wong and Yuan (2011) can only offer one fine estimation of the direction-cosine, which is insufficient for 2-D direction finding. Thus, reference Luo and Yuan (2012) developed an array configuration composed of the "Displaced Dipole-Triad-Plus-Loop-Triad Pair" in and a single antenna. This new configuration gives extended 2-D array aperture and two fine estimations of the direction-cosines.…”

A New Robust Multi-station Direction Finding Cross Algorithm

“…At present all kinds of electronic warfare systems, electronic reconnaissance equipment generally has a measurement of the target direction, one of the most common parameters of target parameters also can obtain the direction of the station [1,2] , multi direction cross positioning method is the earliest and the technology is relatively mature passive positioning method [3,4] . The domestic and foreign scholars in the multi station cross location precision analysis, optimal station analysis, tracking and elimination of false location has done a lot of work and achieved certain results, but compared with other methods [5,6] , the multi station cross location precision of direction finding error influence, in the localization algorithm [7] , optimal station analysis, tracking filter and so there is room for further improvement [8,9] . The multi station DOA algorithm for observability analysis, because the data is less or unreasonable layout of observability resulting in lower case, proposes a modified robust singular value decomposition method based on multi station cross location.…”