Radar signals are generally characterised by repetitive patterns in time. An ESM receiver must intercept and identify several interleaved radar signals. Time-of-arrival (TOA) deinterleaving is employed in ESM processing to identify and extract the pulses of each radar signal. This task is extremely processor intensive and new techniques are required to operate on complex signals in high pulse densities. A new algorithm employing novel techniques is presented for fast, accurate deinterleaving of several repetitive signals. A cumulative TOA difference histogram gives an indication of probable pulse repetition intervals (PRIs) with a minimum number of computations. Validation and identification is given by searching for a sequence of these pulse intervals. The technique presented is less sensitive to interfering pulses and more robust to missed pulses than conventional published techniques. Weighting is used to enhance detection of sequences and a three-pulse priming sequence dramatically reduces unsuccessful searches. By employing a learning process, the efficiency is increased still further. The application of this algorithm to agile PRI signals is shown.

The paper presents an improved method for the deinterleaving of radar signals, based on a time of arrival analysis and the use of the sequential difference histogram (SDIF) for determining the pulse repetition interval (PRI). The optimal detection threshold in the SDIF histogram is derived, which greatly contributes to the efficiency of the algorithm. The algorithm is applied to classic, frequency-agile and staggered PRI radar signals. It is shown that the new method is very successful in high-pulse-density radar environments and for complex signal types. Special attention is given to an application of this method to the multiple-parameter deinterleaving algorithm.