Overview of radar waveform diversity

Blunt, Shannon D.; Mokole, Eric L.

doi:10.1109/maes.2016.160071

Cited by 336 publications

(176 citation statements)

References 313 publications

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“…Waveform/code design, as one of the major problems in radar signal processing [1]- [11], active sensing [12]- [14], and wireless communications [15], has attracted significant interests over the past several decades [16]- [20]. In radar signal processing and active sensing applications, waveform design plays an essential role because "excellent" waveforms can ensure higher localization accuracy [1], enhanced resolution capability [5], and improved delay-Doppler ambiguity of the potential target [21].…”

Section: Introductionmentioning

confidence: 99%

Fast Algorithms for Designing Unimodular Waveform(s) With Good Correlation Properties

Vorobyov

2018

IEEE Trans. Signal Process.

129

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In this paper, we develop new fast and efficient algorithms for designing single/multiple unimodular waveforms/codes with good auto-and cross-correlation or weighted correlation properties, which are highly desired in radar and communication systems. The waveform design is based on the minimization of the integrated sidelobe level (ISL) and weighted ISL (WISL) of waveforms. As the corresponding optimization problems can quickly grow to large scale with increasing the code length and number of waveforms, the main issue turns to be the development of fast large-scale optimization techniques.The difficulty is also that the corresponding optimization problems are non-convex, but the required accuracy is high. Therefore, we formulate the ISL and WISL minimization problems as non-convex quartic optimization problems in frequency domain, and then simplify them into quadratic problems by utilizing the majorization-minimization technique, which is one of the basic techniques for addressing large-scale and/or non-convex optimization problems. While designing our fast algorithms, we find out and use inherent algebraic structures in the objective functions to rewrite them into quartic forms, and in the case of WISL minimization, to derive additionally an alternative quartic form which allows to apply the quartic-quadratic transformation. Our algorithms are applicable to large-scale unimodular waveform design problems as they are proved to have lower or comparable computational burden (analyzed theoretically) and faster convergence speed (confirmed by comprehensive simulations) than the state-of-the-art algorithms.In addition, the waveforms designed by our algorithms demonstrate better correlation properties compared to their counterparts.

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Section: Introductionmentioning

confidence: 99%

Fast Algorithms for Designing Unimodular Waveform(s) With Good Correlation Properties

Vorobyov

2018

IEEE Trans. Signal Process.

129

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“…In order to achieve this, the metric to be optimized is the Integrated Sidelobe Ratio (ISR). The ISR is defined as the ratio of area A τ under |R (τ ) | 2 excluding the mainlobe to the area A 0 under the mainlobe of |R (τ ) | 2 [5] and is expressed as…”

Section: B An Illustrative Examplementioning

confidence: 99%

Waveform Design using Multi-Tone Feedback Frequency Modulation

Hague

Kuklinski

2019

2019 IEEE Radar Conference (RadarConf)

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This paper introduces a waveform design method using Multi-Tone Feedback Frequency Modulation (MT-FFM), a generalization of the single oscillator feedback FM method developed by Tomisawa [1]. The MT-FFM utilizes a collection of K harmonically related oscillators each governed by a design parameter z k which are utilized as a discrete set of parameters that may be modified to generate a richer set of modulation functions than in the single oscillator case. The resulting modulation function is represented using a form of Kapteyn series composed of Generalized Bessel Functions. This paper describes the structure of the MT-FFM waveform, derives the Kapteyn series representation of the waveform's modulation function, and demonstrates the design method with a waveform design example.

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“…Specifically, we consider to design a pulsed transmit signal instead of a continuous-waveform signal since the goal is to detecting a moving target [9]. The pulsed waveform transmit signal s(t) at the baseband is of the form: where T r is the pulse repetition interval (PRI) and p(t), the complex signature pulse of the waveform with a duration T p , a k 's are complex-valued coefficients, and K is the number of pulses in a coherent integration interval.…”

Section: (T) = S(t − τ)H(t τ)Dτ;mentioning

confidence: 99%