Near-Field Multiple Target Localization in Frequency Diverse Array Based on Tensor Decomposition

Abstract

Target localization is a fundamental problem in array signal processing. The problem of locating near-field targets with multiple-input multiple-output (MIMO) radar has been studied extensively; however, most of the conventional matrix-based approaches suffer from limitations in terms of the representation and exploitation of the multidimensional nature of MIMO radar signals. In this paper, we addressed the problem of localizing multiple targets in the near-field region, aiming at pursuing a solution applicable for multidimensional signal that is able to achieve sufficient accuracy. A tensor-based signal model impinging on a monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar was formulated, and a corresponding tensor decomposition-based localization algorithm (TenDLA) that showcases the connection between the tensor-based analysis and the localization problem was developed. Additionally, a correction procedure to mitigate the estimation deviations on the range and angle was presented, yielding significant improvements in estimation accuracy. Numerical examples demonstrated the validity and effectiveness of the proposed approach, and it was shown that this approach is superior to conventional methods due to its high-resolution estimation accuracy as well as its relatively low computational costs.

A graph-based target localization method in non-uniform frequency diversity array radar

“…Compared to the signal model established in [12], the exponential term of (4) becomes non-linear since the array spacing is non-uniform. Depending on this feature, it is possible to decouple the range and angle hidden in the steering vector, without utilizing the double pulses mechanism that is adopted in [12].…”

“…Compared to the signal model established in [12], the exponential term of (4) becomes non-linear since the array spacing is non-uniform. Depending on this feature, it is possible to decouple the range and angle hidden in the steering vector, without utilizing the double pulses mechanism that is adopted in [12]. In other words, even though there are extra challenges that need to be solved when NULA is used, it provides a new way to solve the decoupling problem.…”

“…is the matrix constructed by the eigenvectors of A, and Λ is the diagonal matrix containing the set of eigenvalues of A. According to references [10] and [12], the GFT of a graph signal s is defined as…”

“…As previously mentioned, formulating a well-designed graph that represents the dependency relationship of the acquired data across the array is the primary task. Inspired by [12], we build a direct graph in a similar way, which is depicted in Figure 2, therefore the output corresponding to the NULA-FDA as Eq. ( 4) has been mapped to a graph signal.…”

“…Compared to the conventional MIMO radar, it takes more effort to solve the parameters of interest in FDA radar, due to the coupling problem which is characterized by the range-angle-dependent beampattern. Some novel methods oriented toward parameter estimation in FDA radar have been proposed, including double pulses based 4 , random frequency offset based 5 , subarray-based 6 , tensor decomposition based 7 , etc. Unfortunately, the methods aforementioned also are applicable to the ULA configuration, how to utilize a non-uniform linear array (NULA) to localize targets requires more investigation.…”