Extended orthogonal matching pursuit for robust and fast target localisation in multiple‐input multiple‐output radar

Abstract

The multiple-input multiple-output (MIMO) radar combines ultra-wide angular illumination on transmit and narrow transmission angular resolution after processing on receive. Orthogonal waveforms are needed in theory for all independent transmitters, but this property is usually not achievable in practice, therefore leading to a degraded ambiguity function. In this study, the authors focus on phase codes waveform, which gives quasi-optimal angle and range resolution. On the other hand, they induce a harmful "floor" of high-level sidelobes in the range-angle domain. The authors propose to apply the orthogonal matching pursuit (OMP) to the MIMO radar signals. Therefore, it becomes theoretically possible to detect targets initially hidden by high-level sidelobes. Nevertheless, the authors identify phenomenon (grid granularity, neighbour influence) that lead to possible localisation error resulting in ghost targets. Considering this, the authors propose an extension of the OMP rejection step that includes, in addition to the expected target position, the neighbour positions within the uncertainty area. The authors show that this extension allows them to suppress the sensibility of the OMP to localisation error, even in case of very close targets. Finally, the authors demonstrate the performance of the proposed method on simulations as well as on experimental MIMO radar signals.

Robust Orthogonal Matching Pursuit Detector : Applications to off-grid target issues in MIMO Radar

“…This is the direct extension of (14) in 1D (2 neighbors) to 2D (eight neighbors). The rationale is described in detail in [5]. The extended projector response,…”

“…The basic idea is to enlarge the rejection region in the OMP rejection step. The method has been proven to work well on real data, to be computationally very efficient, and was justified using the development in Taylor series at second order of the steering vector in [5].…”

Fractional sparse energy representation method for ISAR imaging

“…Then we select B l = g γ li i = 0, 1, …, μ l − 1 ⊂ D via orthogonal matching pursuit (OMP) [30,31] to satisfy…”

“…In addition, according to (6) and (23), the estimation of the slope is about β = − 4.015 s −2 . Consequently, the rotation rate estimation is ω 0 = 14.821 ∘ /s according to (30).…”

“…The estimation slope β is about −0.4814 s −2 . According to (30), the rotation rate of the target is ω 0 = 5.1323 ∘ /s.…”