In harbour environments operators should perform tasks as detection and classification. Present-day threats of small
objects, as jet skis etc, should be detected, classified and recognized. Furthermore threat intention should be analysed.
As harbour environments contain several hiding spaces, due to fixed and floating neutral objects, correct assessment of
the threats is complicated when detection tracks are intermittently known. For this purpose we have analysed the
capability of our image enhancement and detection technology to assess the performance of the algorithms in a harbour
environment. Data were recorded in a warm harbour location. During these trials several small surfaces targets were
used, that were equipped with ground truth equipment. In these environments short-range detection is mandatory,
followed by immediate classification. Results of image enhancement and detection are shown. An analysis was made
into the performance assessment of the detection algorithms.
We present new techniques for passive ranging with a dual-band IR search and track (IRST) sensor aboard a ship. Three distance estimation methods are described: the atmospheric propagation model, the apparent surface of the target, and target motion analysis (TMA). These methods are tested on the sensor output of real data during cold water trials (CWTs). They are evaluated by comparing with simultaneously obtained radar reference data at the test site. Results of these three passive ranging and three fusion processes, combining the preceding methods, are presented. This demonstrates the effectiveness of IR passive ranging techniques in the anti-air-warfare scenario. Majority voting fusion shows that improvement of the distance estimation is achieved in the CWT scenario when combining these three different methods. A range-error reduction of 41% is obtained, and a typical uncertainty of 5% is at a 8-km distance. During warm water trials (WWTs) the TMA algorithm was adapted to deal with a dynamic environment of the antisurface warfare scenario (ASuW). These WWTs prove that TMA in combination with an IRST system can extend the basic IRST functionality significantly for a dynamic ASuW scenario.