Suboptimal Low Complexity Joint Multi-Target Detection and Localization for Non-Coherent MIMO Radar With Widely Separated Antennas

Abstract

In this paper, the problems of simultaneously detecting and localizing multiple targets are considered for noncoherent multiple-input multiple-output (MIMO) radar with widely separated antennas. By assuming a prior knowledge of target number, an optimal solution to this problem is presented first. It is essentially a maximum-likelihood (ML) estimator searching parameters of interest in a high-dimensional space. However, the complexity of this method increases exponentially with the number G of targets.Besides, without the prior information of the number of targets, a multi-hypothesis testing strategy to determine the number of targets is required, which further complicates this method. Therefore, we split the joint maximization into G disjoint optimization problems by clearing the interference from previously declared targets. In this way, we derive two fast and robust suboptimal solutions which allow trading performance for a much lower implementation complexity which is almost independent of the number of targets. In addition, the multi-hypothesis testing is no longer required when target number is unknown.Simulation results show the proposed algorithms can correctly detect and accurately localize multiple targets even when targets share common range bins in some paths.

Geometric Optimization of Distributed MIMO Radar Systems With Spatial Distance Constraints

“…Remark 3. Note that from (1), (2), (14), (15), (16) and 17, the WCR is related to the node positions and the location of the RSA. Once the RSA is determined, the WCR only depends on the node positions, and then Γ(Θ, Ψ) can be simplified as Γ(Θ).…”

“…Such radar systems can fully utilize spatial diversity by observing a target simultaneously from different aspect angles [3], [5], [6]. As shown in existing publications, due to the spatial diversity provided by the widely dispersed radar nodes [3], [4], [7], distributed MIMO radar systems offer a number of unique benefits including better detection performance [8], [9], more degrees of freedom [10], [11], higher spatial resolution [12], better spatial coverage [13], enhanced parameter identifiability [14], and higher localization accuracy [15], [16].…”

Time‐modulated sparse linear array synthesis with minimum elements and a specific sidelobe

“…It will also help to optimise the distribution of elements when the spatial size is limited. Additionally, we extend this method to form multi‐beam with a fundamental component and ±first harmonic, which can be useful in communication [17] and multi‐target detection [18].…”

Direct position determination and effective extraction of multiple transmitters

“…While in [14], all sources in the monitoring area can be localised in turn by eliminating the influence of the declared targets without using the complicated multi‐hypothesis testing strategy [15]. The image expansion (IE) algorithm is based on the pixel duplication, bilinear interpolation or Canny edge‐based methods, and is generally used to analyse image or to extract local maximum values [16–19].…”

“…Here, we use it to estimate the number and extract the locations of the potential transmitters, thus solve the target extraction problem. Compared to the methods that detect the sources one by one in [14], the IE algorithm can deal with all sources at one time, which is simpler and shown to be effective.…”