Target Localization Accuracy Gain in MIMO Radar-Based Systems

By Hana Godrich, A. Haimovich, Rick S. Blum. IEEE Transactions on Information Theory, 2008

623 citations recorded by the source corpus.

This paper presents an analysis of target localization accuracy, attainable by the use of multiple-input multiple-output (MIMO) radar systems, configured with multiple transmit and receive sensors, widely distributed over an area. The Cramer-Rao lower bound (CRLB) for target localization accuracy is developed for both coherent and noncoherent processing. Coherent processing requires a common phase reference for all transmit and receive sensors. The CRLB is shown to be inversely proportional to the signal effective bandwidth in the noncoherent case, but is approximately inversely proportional to the carrier frequency in the coherent case. We further prove that optimization over the sensors' positions lowers the CRLB by a factor equal to the product of the number of transmitting and receiving sensors. The best linear unbiased estimator (BLUE) is derived for the MIMO target localization problem. The BLUE's utility is in providing a closed-form localization estimate that facilitates the analysis of the relations between sensors locations, target location, and localization accuracy. Geometric dilution of precision (GDOP) contours are used to map the relative performance accuracy for a given layout of radars over a given geographic area.

Subfields: Aerospace Engineering, Biomedical Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/258c17c2d9e1b567a3aa8272b58ede7d1dce4e80

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Citations in the corpus

Accurate Passive Location Estimation Using TOA Measurements (2012, neutral): “…such equations, we can express them in a matrix form as follows h− Sθ = Be+O(e2) ≈ Be, (5) where h = ⎡ ⎢⎢⎢⎢⎣ −a21+b21−r̂212 −a22+b22−r̂222 ... −a2M+b2M−r̂2M2 ⎤ ⎥⎥⎥⎥⎦ , S = − ⎡ ⎢⎢⎢⎣ a1 b1 −r̂1 a2 b2 −r̂2 ... aM bM −r̂M ⎤ ⎥⎥⎥⎦ , θ = [x, y, v]T , e = [e1,e2, . . . , eM ] T , and B = v · I− diag ([r1,…”
Overview of radar waveform diversity (2016, methodology): “This arrangement has been known by many names, including netted radar [295, 296], statistical MIMO [240, 297–299], and more classically as multistatic radar or multisite radar [300–305].”
Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond (2021, methodology): “The signals, instead of the sensed parameters, are collected and fused at the centralized unit, where an important application of such techniques is distributed MIMO radar [15], [169].”
Throughput Maximization for UAV-Enabled Integrated Periodic Sensing and Communication (2022, background): “With the advancements of massive antennas and millimeter wave (mmWave)/terahertz (THz), ISAC base stations (BSs) could also provide higher sensing resolution and accuracy to enable many location-aware intelligent applications with stringent sensing requirements [6].”
A Theoretical Foundation of Network Localization and Navigation (2018, background): “With sufficient transmission power, signal bandwidth, or array aperture, these techniques can potentially achieve submeter localization accuracy [4]–[6].”