Ballistic missile detection via micro-Doppler frequency estimation from radar return

Liu, Lihua; McLernon, Des; Ghogho, Mounir; Hu, Weidong; Jian, Huang

doi:10.1016/j.dsp.2011.10.009

Cited by 61 publications

(29 citation statements)

References 11 publications

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“…In practice, it is very important to extract, decompose, and estimate parameters of these kinds of signals, since they are related to the physical dimensions and other properties of the moving objects [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Most of the techniques used for the detection, extraction, and parameter estimation of these signals are based on two approaches.…”

Section: Introductionmentioning

confidence: 99%

Inverse radon transform–based micro-doppler analysis from a reduced set of observations

Stanković

Daković

Thayaparan³

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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A method for accurate and efficient parameter estimation and decomposition of sinusoidally frequency modulated signals is presented. These kinds of signals are of special interest in radars and communications. The proposed method is based on the inverse Radon transform property to transform a two-dimensional sinusoidal pattern into a single point in a two-dimensional plane. Since the signal is well concentrated (sparse) in the inverse Radon transform domain, its reconstruction can be performed from a reduced set of observations (back-projections). Theory is illustrated on signals with one and more components, including noise and disturbances, as well as time-frequency patterns that deviate from sinusoidal form.

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Section: Introductionmentioning

confidence: 99%

Inverse radon transform–based micro-doppler analysis from a reduced set of observations

Stanković

Daković

Thayaparan³

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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“…[8][9][10][11][12][13][14][15][16][17] Recently, estimation techniques of micro-Doppler frequency are also proposed based on the maximum likelihood and TFD approaches. [18][19][20][21][22][23] However, the characterization of statistical performance bounds on the micro-Doppler parameter estimation is quite limited in the literature, except for a few scenarios involving vibrating and rotating targets. 24,25 Another noticeable commonality among most of these work is the analyses of the micro-Doppler features using only the single or dual carrier-frequency signals.…”

Section: Introductionmentioning

confidence: 99%

Performance bounds on micro-Doppler estimation and adaptive waveform design using OFDM signals

2014

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We analyze the performance of a wideband orthogonal frequency division multiplexing (OFDM) signal in estimating the micro-Doppler frequency of a target having multiple rotating scatterers (e.g., rotor blades of a helicopter, propellers of a submarine). The presence of rotating scatterers introduces Doppler frequency modulation in the received signal by generating sidebands about the transmitted frequencies. This is called the micro-Doppler effects. The use of a frequency-diverse OFDM signal in this context enables us to independently analyze the micro-Doppler characteristics with respect to a set of orthogonal subcarrier frequencies. Therefore, to characterize the accuracy of micro-Doppler frequency estimation, we compute the Cramér-Rao Bound (CRB) on the angular-velocity estimate of the target while considering the scatterer responses as deterministic but unknown nuisance parameters. Additionally, to improve the accuracy of the estimation procedure, we formulate and solve an optimization problem by minimizing the CRB on the angular-velocity estimate with respect to the transmitting OFDM spectral coefficients. We present several numerical examples to demonstrate the CRB variations at different values of the signal-to-noise ratio (SNR) and the number of OFDM subcarriers. The CRB values not only decrease with the increase in the SNR values, but also reduce as we increase the number of subcarriers implying the significance of frequency-diverse OFDM waveforms. The improvement in estimation accuracy due to the adaptive waveform design is also numerically analyzed. Interestingly, we find that the relative decrease in the CRBs on the angular-velocity estimate is more pronounced for larger number of OFDM subcarriers.

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“…Since it is clear that warheads and decoys make specific micro-motions during their ballistic trajectory, the micro-Doppler effects analysis introduced in [1], and widely investigated in the last decade [2], may be used for the purpose of information extraction for target classification. Specifically, the warheads may be characterized by precession and nutation, while the decoys wobble, as described in [3] and [4]. Such a different micro-motions introduce different micro-Doppler signatures in the returned radar signal.…”

Section: Introductionmentioning

confidence: 99%

Micro-Doppler Based Recognition of Ballistic Targets Using 2D Gabor Filters

Persico

Clemente²,

Ilioudis³

et al. 2015

2015 Sensor Signal Processing for Defence (SSPD)

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The capability to recognize ballistic threats, is a critical topic due to the increasing effectiveness of countermeasures and to economical constraints. In particular the ability to distinguish between warheads and decoys is crucial in order to mitigate the number of shots per hit and to maximize the ammunition capabilities. For this reason a reliable technique to classify warheads and decoys is required. In this paper the use of micro-Doppler signatures in conjunction with the 2-Dimensional Gabor transform is presented for this problem. The effectiveness of the proposed approach is demonstrated through the use of real data.

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Ballistic missile detection via micro-Doppler frequency estimation from radar return

Cited by 61 publications

References 11 publications

Inverse radon transform–based micro-doppler analysis from a reduced set of observations

Inverse radon transform–based micro-doppler analysis from a reduced set of observations

Performance bounds on micro-Doppler estimation and adaptive waveform design using OFDM signals

Micro-Doppler Based Recognition of Ballistic Targets Using 2D Gabor Filters

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