Multistatic ISAR autofocus with an image entropy-based technique

Abstract

A key issue in radar research for many years has been the noncooperative identification (NCI) of moving targets. Such targets can be aircraft, ships, ground vehicles, satellites, or ballistic missiles. Nowadays, identification is mostly based on cooperative methods like the identification friend-foe (IFF) systems in the military sector or automatic dependent surveillance-broadcast (ADS-B) for civil aircraft. However, a number of situations exist in which a cooperative identification is not possible. In conflict situations, hostile targets do not identify themselves. Knowledge of their type by NCI yields a tactical advantage. History has also shown a significant number of cases in which neutral or friendly targets have been destroyed due to their inability to identify themselves. Neutral targets usually do not share IFF codes of conflict parties, and friendly targets may turn off their IFF when performing covert operations. Operations between allied nations have also been a source of possible IFF failure. Furthermore, the desire for NCI is not limited to conflict situations. Unidentified aircraft or ships often are suspected to be terrorists. In all of the above situations, a decision on a possible response is extremely time critical, and possible consequences can be serious. For this reason, NCI remains an important research topic

Spatial resolution of light field sectioning pyrometry for flame temperature measurement

“…It is required to obtain one of the sharpest LF refocus image on the plate. The sharpest LF refocus image is determined by evaluating all the LF refocus images through image sharpness evaluation function [11,42].…”

Passive ISAR part I: framework and considerations

“…Importantly, for PR where the range resolution is often relatively coarse, the ability to isolate potential scatters is significantly compromised, which would significantly degrade the quality of this approach. An alternative method for RMC is based on target tracking, using two netted monostatic radars [12, 42], with similar concepts recently demonstrated for a multistatic set of high resolution, at X‐band radars for ground moving vehicles [19, 43].…”

Passive ISAR part II: narrowband imaging

“…The entropy metric measures the degree of ‘randomness’ in the image; as such, a well‐focused image typically possesses a minimum image entropy . Many TMC autofocus techniques, both parametric and non‐parametric [28, 30–33], are based on this approach. The contrast method computes the RMS standard deviation of the image; a well‐focused image should have maximum image contrast .…”