Ambiguity Functions for Spatially Coherent and Incoherent Multistatic Radar

Passive radar provides a means of spectrum sharing in a congested environment. In passive radar, the radar design problem is one of emitter selection, rather than waveform optimisation. This study combines relevant criteria (signal-to-noise ratio, ambiguity function integrated sidelobes, effective multistatic resolution area and contrast ratio) in a multiobjective optimisation to select subsets of available emitters for passive multistatic synthetic aperture radar (SAR). Extensions to wellknown monostatic and bistatic performance criteria are defined for the multistatic SAR case. Assumed limitations in the number of available receiver channels constrain the emitter selection. The proposed emitter selection framework is demonstrated for a simulated scenario of 24 analogue and digital emitters, with a constraint of four receiver channels. Results indicate that the proposed ranking successfully differentiates between emitter sets resulting in good images and those sets resulting in poor images, providing a first step towards formulating a general image quality equation for multistatic SAR.

Section: Ambiguity Function Integrated Sidelobe Levelsupporting

confidence: 73%

Emitter selection criteria for passive multistatic synthetic aperture radar imaging

Stevens

Jackson²

2014

“…In both rules, it is assumed that the fusion centre knows the position of each transmitter and receiver of the multistatic radar network. The most widely adopted technique for choosing the weights is [21] p SNR…”

Section: Selection Of Tx and Rx (Selx) Fusion Algorithmmentioning

confidence: 99%

Non‐cooperative target recognition in multistatic radar systems

Stinco

Greco

Gini

et al. 2014

The ability to recognise quickly and reliably non-cooperative targets is a primary issue in modern radar systems. The use of multistatic radars has already been demonstrated to improve sensibly the recognition capacity of a radar system, through the exploitation of its inherent spatial diversity, given by the presence of multiple observation channels. This calls for the development of suitable fusion strategies for efficiently combining the signals arising from multiple channels of the network. In this study, the authors define a multistatic classification algorithm where the information on the target class is provided by the sensors of the system and the final decision is made using a fusion rule that combines the decisions coming from each channel of the radar network. A suitable selection is made to favour the channels with better performance. The numerical analysis demonstrates the superior performance of the proposed algorithm over the classical fusion rule, that takes into account only the effect of the energy path loss, in terms of probability of correct classification

“…Multistatic radars offer many advantages compared to monostatic radars especially increased coverage and improved target resolution and classification [8], in addition to higher tolerance to sources of interference due to their spatial diversity and the potential for improved physical survivability owing to the multiplicity of stations [9]. However, a minimum level of synchronization between different units is required to achieve multistatic signal processing [9].…”

Section: Introductionmentioning

confidence: 99%

“…However, a minimum level of synchronization between different units is required to achieve multistatic signal processing [9].…”

Section: Introductionmentioning

confidence: 99%

Cognitive waveform and receiver selection mechanism for multistatic radar

Kilani

Nijsure

Gagnon

et al. 2016

A novel Cognitive Radar (CR) approach to improve the extended targets detection and resolution is developed in a multistatic radar context. A cognitive waveform selection mechanism based on target probability of detection maximization in conjunction with adaptive receiver allocation/selection is proposed. Apart from the cognitive waveform selection objective, this process aims at evaluating the optimal positions for the radar receivers in an attempt to iteratively minimize the Geometric Dilution of Precision (GDOP), subsequently resulting in a high precision target geolocation estimate. The cognitive waveform selection mechanism is based on target dynamics involving time varying target scattering characteristics and clutter distribution parameters. Thus with the proposed dual objective approach, the concept of cognition can be extended to both the radar transmitter and receiver sites. Numerical results demonstrate better target detection performance and positioning accuracy using the proposed approach as compared with conventional methods based on static transmission or stationary multistatic receivers topology.