Coherent Integration With Range Migration Using Keystone Formatting

Perry, R.P.; DiPietro, R.C.; Fante, Ronald L.

doi:10.1109/radar.2007.374333

Cited by 112 publications

(70 citation statements)

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“…However, when an extended CPI is required, e.g., in very weak signal conditions, target range migration must be properly compensated for. The commonly used range migration compensation methods, such as the Keystone transform [19], can be used to compensate for the linear range migration. However, when acceleration and higher order motion parameters are prominent features of the target motion, target range migration compensation emerges as a challenging problem.…”

Section: Matched Filtering and Range Migration Correctionmentioning

confidence: 99%

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Motion parameter estimation of multiple ground moving targets in multi-static passive radar systems

Subedi

Zhang

Amin

et al. 2014

EURASIP J. Adv. Signal Process.

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Multi-static passive radar (MPR) systems typically use narrowband signals and operate under weak signal conditions, making them difficult to reliably estimate motion parameters of ground moving targets. On the other hand, the availability of multiple spatially separated illuminators of opportunity provides a means to achieve multi-static diversity and overall signal enhancement. In this paper, we consider the problem of estimating motion parameters, including velocity and acceleration, of multiple closely located ground moving targets in a typical MPR platform with focus on weak signal conditions, where traditional time-frequency analysis-based methods become unreliable or infeasible. The underlying problem is reformulated as a sparse signal reconstruction problem in a discretized parameter search space. While the different bistatic links have distinct Doppler signatures, they share the same set of motion parameters of the ground moving targets. Therefore, such motion parameters act as a common sparse support to enable the exploitation of group sparsity-based methods for robust motion parameter estimation. This provides a means of combining signal energy from all available illuminators of opportunity and, thereby, obtaining a reliable estimation even when each individual signal is weak. Because the maximum likelihood (ML) estimation of motion parameters involves a multi-dimensional search and its performance is sensitive to target position errors, we also propose a technique that decouples the target motion parameters, yielding a two-step process that sequentially estimates the acceleration and velocity vectors with a reduced dimensionality of the parameter search space. We compare the performance of the sequential method against the ML estimation with the consideration of imperfect knowledge of the initial target positions. The Cramér-Rao bound (CRB) of the underlying parameter estimation problem is derived for a general multiple-target scenario in an MPR system. Simulation results are provided to compare the performance of the sparse signal reconstruction-based methods against the traditional time-frequency-based methods as well as the CRB.

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Section: Matched Filtering and Range Migration Correctionmentioning

confidence: 99%

“…Since the target velocity does not have a significant impact on the ambiguity function of the chirp Doppler signature, the velocity vector for all the targets is ignored in (19). As such, using (9), the output of the receiver matched filter at the mth azimuthal sample can be expressed as…”

Section: Estimation Of Acceleration Of Multiple Targetsmentioning

confidence: 99%

Motion parameter estimation of multiple ground moving targets in multi-static passive radar systems

Subedi

Zhang

Amin

et al. 2014

EURASIP J. Adv. Signal Process.

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“…Assume that the translational motion of the target has been compensated using conventional methods [15][16][17]. The target can be treated as a platform which rotates around the center O, as shown in Fig.…”

Section: Model Of Range-doppler Isar Imagingmentioning

confidence: 99%

Multi-target simultaneous ISAR imaging based on compressed sensing

Hou

Chen

2016

EURASIP J. Adv. Signal Process.

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Conventional range-Doppler (RD) inverse synthetic aperture radar (ISAR) imaging method utilizes coherent integration of consecutive pulses to achieve high cross-range resolution. It requires the radar to keep track of the target during coherent processing intervals (CPI). This restricts the radar's multi-target imaging ability, especially when the targets appear simultaneously in different observing scenes. To solve this problem, this paper proposes a multi-target ISAR imaging method for phased-array radar (PAR) based on compressed sensing (CS). This method explores and exploits the agility of PAR without changing its structure. Firstly, the transmitted pulses are allocated randomly to different targets, and the ISAR image of each target can be then reconstructed from limited echoes using CS algorithm. A pulse allocation scheme is proposed based on the analysis of the target's size and rotation velocity, which can guarantee that every target gets enough pulses for effective CS imaging. Self-adaptive mechanism is utilized to improve the robustness of the pulse allocation method. Simulation results are presented to demonstrate the validity and feasibility of the proposed approach.

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“…Target motion will influence the target detection in surveillance radar or the focus of imaging in SAR or ISAR [1][2][3]. Many researches on motion compensation containing range migration compensation have been done [3][4][5][6][7][8][9]. Typical migration compensation methods include rangebin realignment [4], envelope interpolation algorithm [9], Keystone transform [6,7], time-frequency analysis [8], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Many researches on motion compensation containing range migration compensation have been done [3][4][5][6][7][8][9]. Typical migration compensation methods include rangebin realignment [4], envelope interpolation algorithm [9], Keystone transform [6,7], time-frequency analysis [8], etc. The computation burden and the processing complexity are the problems facing these methods when used in practice.…”

Section: Introductionmentioning

confidence: 99%

A Practical Method for Range Migration Compensation in Chirp Radar

Li¹,

Liu²,

He³

2009

PIER M

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Abstract-The echo signal characteristic of a chirp pulse train from a moving target is analyzed. It is pointed out that the range migration is caused by the migration exponential item (MEI) and can be compensated by removing the MEI. On this basis, we proposed a new practical compensation method with very low computation burden through shifting control on the matching weight coefficients. The parasitical sidelobes due to the quantization effect can be restrained effectively by storing multi-groups of weight. Simulation results proved the availability of the method.

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Coherent Integration With Range Migration Using Keystone Formatting

Cited by 112 publications

References 1 publication

Motion parameter estimation of multiple ground moving targets in multi-static passive radar systems

Motion parameter estimation of multiple ground moving targets in multi-static passive radar systems

Multi-target simultaneous ISAR imaging based on compressed sensing

A Practical Method for Range Migration Compensation in Chirp Radar

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