Adaptive Jamming Waveform Design for Distributed Multiple‐Radar Architectures Based on Low Probability of Intercept

Abstract

This paper investigates the problem of low probability of intercept (LPI)‐based adaptive jamming waveform design for distributed multiple‐radar architectures. Such a smart jammer system adopts a multibeam working mode, where multiple simultaneous jamming beams are synthesized to interfere with multiple radars independently. The primary objective of the smart jammer is to minimize the total jamming power by optimizing the transmitted jamming waveform while the achieved signal‐to‐interference‐plus‐noise ratio (SINR) and mutual information (MI) between the received echoes from the target at each radar receiver and the target impulse responses are enforced to be below specified thresholds. First, the expressions of SINR and MI are derived to characterize target detection and characterization performance, respectively. Then, two different LPI‐based jamming waveform design strategies are proposed to minimize the total noise jamming power by optimizing the jamming waveform while the achieved SINR/MI is enforced to be below a certain threshold. The resulting optimization problems are solved analytically by employing the technique of Lagrange multipliers. With the aid of some numerical examples, it is illustrated that the two schemes result in different jamming waveform design results, which is useful to guide jamming power allocation for various jamming tasks. It is also shown that the LPI performance of the smart jammer can be efficiently improved by exploiting the proposed jamming waveform design criteria.

A method for jamming waveform design in precision electronic warfare scenarios

“…Because of the sensitivity of the military background of electronic warfare systems, the existing literature often involves designing anti-jamming waveforms [14]. However, the problem of jamming waveform design is rarely mentioned, and it is often aimed at a specific target system [15,16], and the signalto-interference-plus-noise ratio (SINR) is used as an indicator [17]. This is not the same as the jamming waveform design problem in PREW.…”

Deception electronic counter‐countermeasure applicable to multiple jammer sources in distributed multiple‐radar system

“…However, the ECCM ability of monostatic radar is limited with its single-view angle and its obtained deficient information. A distributed multiple-radar system [17][18][19][20], which employs multiple widely distributed stations, has natural advantages in countering deception jamming. It benefits from the spatial diversity of target scatterers supported by particular widely distributed stations and has been widely researched recently.…”

Game Theoretic Countermeasure Analysis for Multistatic Radars and Multiple Jammers

“…Nevertheless, radar, target, and jammer are regarded as the major participants in the EW countermeasure game (Chau & Clahsen, 2019; Shi, Wang, et al., 2019). For the countermeasure relationship, game theoretic analysis have been introduced into different directions of radar field, including radar jamming (Bachmann et al., 2011; Deligiannis, Rossetti, et al., 2016; Norouzi & Norouzi, 2012; Song et al., 2012), target tracking and detection (Deligiannis, Lambotharan, & Chambers, 2016; Gogineni & Nehorai, 2012; Lan et al., 2015), waveform design (Han & Nehorai, 2016; Panoui et al., 2016; Piezzo et al., 2013) and resource allocation (Chen et al., 2015; Deligiannis & Lambotharan, 2017; Deligiannis et al., 2017; Feng et al., 2016; Godrich et al., 2011; He & Su, 2019; Li et al., 2020; Shi et al., 2017; Shi, Ding, et al., 2020; Shi, Wang, et al., 2020; Shi, Wang, et al., 2019; Sun et al., 2014; Wang et al., 2017; Yi et al., 2020; Yuan et al., 2019). The work in (Bachmann et al., 2011) use the utility function (UF) of joint detection probability and false alarm probability to establish different supermodular countermeasure games, and the performance of adaptive radar jamming is analyzed.…”