Institute of Control Sciences, Russian Academy of Sciences

Profsoyuznaya, 65, Moscow, 117806, Russia

A basically new, background approach to detecting moving objects is proposed. According to it, signals about a moving object are obtained by studying the background against which the located object moves. The underlying surface, sea bottom, ionosphere, etc., can be used as the background.

According to the traditional ideas, the problem arising upon detecting a moving object lies in that the sensing radiation is usually reflected by the object to a lesser degree than from the background and that the ''interfering background'' masks the useful signal from the object. This problem arises also if there is no contrast between the object and background. Besides this passive noise, target detection is also embarrassed by purposeful active impact noise «blinding» the locator.

The present publication proposes a basically new approach to detecting moving objects. According to the new ideas, the traditional noise (the coherent component of the radiation reflected from a stationary background) is regarded as the source of information about the detected object, and the traditional useful signal (radiation reflected from the target) is disregarded, that is, set to zero when processing the recorded radiation. A special case of this approach – generation of the holographic image of a moving object was discussed before.

By background is meant the medium behind the moving object that can reflect the radiation. According to the background principle, the signals from a moving object are extracted from the radiation of background against which the object moves (in applications, the underlying surface, ionosphere, sea bottom, etc., can be used as the background).

The background principle of detection is based on coherent reception of the sensing radiation scattered by the background and isolation of its coherent component. When a moving object appears, the coherent component decreases, thus signaling the fact of detection.

The background principle modifies and complements the traditional concepts of signal and noise. It is believed traditionally that a signal can be obtained by directly studying the object and that the radiation from the background is the noise which is to be suppressed. By the background principle, the background radiation is functionally related to the signal, and therefore, one can judge about the signal from the functional dependence of the background (noise) on the signal. This is especially important if the radiation scattered by the background (traditional noise) is much greater than the traditional useful signal.

All existing countermeasures (active and passive noise) were invented for the traditional method for detection of moving targets; no countermeasure yet exists for the proposed method.

Therefore, the background principle gave rise to a new method of detection of a moving object which proves efficient where the traditional methods (including the Doppler effect-based ones) do not work – for example, if the target does not backscatter the sensing radiation and moves perpendicularly to the directivity diagram and, at the same time, impact noise is directed to the receiving antenna. This advantage was validated experimentally in real time and realistic environment using a physical radiolocator. The proposed new method of detection, which is based on the discovery made at the Institute of Control Sciences, Russian Academy of Sciences, is also efficient if there is no any contrast between the moving object and background. The background principle sets one reevaluating the domain of detection of moving objects and changes the sense of notions such as «interfering background», «background-target situation», «signal-noise», etc., and complements the Doppler effect because it is as if its reverse. Both methods must be used hand in hand.

The background principle was realized in detection of moving various objects (including “stealth” aircraft), security devices, biology, and psychology; it can find application also in socio-economic environment, ecology, and for accident forecasting.