Signal processing incorporating signal, tracking, estimation, and removal processes using a maximum a posteriori algorithm, and sequential signal detection
A system and method for automating signal tracking, estimation of signal parameters, and extraction of signals from sonar data to detect weaker signals. A maximum a posteriori (MAP) algorithm processing provides a track output of the signal which is used as a guide or template to provide optimal spectral integration on an unstable or frequency varying line. The present invention includes track integration, parameter estimation, signal track normalization, and sequential signal detection. The present invention partitions the input band into frequency subwindows. For each subwindow, the strongest signal is tracked, its parameters are estimated, and then the signal is normalized (removed) from the subwindow. This is repeated until the entire subwindow set is processed. Then the subwindows, now with their strongest signals removed, are recombined to form one input band. This aggregated procedure represents one processing pass. In the next pass, the entire above procedure is repeated with either the same or new subwindow boundaries. This continues until a predetermined number of passes is completed. Sequential signal detection is provided from one data frame to the next, a problem that is beyond the capability of conventional systems and methods for tracking frequency lines of unknown frequency modulation and amplitude.
A system for facilitating the tracking of a target vehicle in connection h successively-received acoustic sensor signal data items. A fast Fourier transform operation is performed in connection with each successive acoustic sensor signal data item to generate a phase and amplitude beam map reflecting spectral signal energy in the received acoustic sensor signal data item. If the beam map for a acoustic sensor signal data item indicates that the item represents a signal having a signal-to-noise ratio above a first predetermined threshold value, a beam map generated for a previous acoustic sensor signal data item is used to generate a bounded beam map, a determination is made as to whether the bounded beam map represents a signal having a second predetermined signal-to-noise ratio. In response to a positive determination for the latter signal-to-noise determination, both the beam maps are used to generate tracking information for the target vehicle.
A method and device for enhanced detection of active sonar input signals provided. The method uses a multi-step manipulation of range-bearing data through lag vectors to map each resolution cell into a feature vector. Feature vectors are used to generate a set number of cluster means. Each feature vector is assigned to a given cluster mean, and the cluster means are used to further partition the input data. To eliminate over-fragmentation, similar clusters are consolidated to provide the correct level of resolution. The device comprises a standard active sonar system having a range-beam partitioner inserted between the initial signal filters and the final two-dimensional adaptive filter of the system. The range-beam partitioner manipulates the input data, forming it into homogeneous partitions according to the method and then provides the enhanced data to the adaptive filter. The use of both the method and device allow for the adaptive filter to more effectively estimate the co-variance structure of the entire range-beam space, eliminating errors from both the consolidation of non-homogeneous regions into single regions and from breaking homogeneous regions into more than a single region.
A system having a device for comparing incoming data with hypotheses prevsly formed from prior data for providing new hypotheses on target information. It has application when the data source is from either single or multiple targets. The incoming datum to the system forms new hypotheses assuming the incoming datum is invalid, forms new hypotheses assuming the new datum begins a new segment of information, and forms new hypotheses assuming the new datum is associated with segments in prior retained hypotheses. The one hypothesis of the thusly formed new hypotheses with the greatest likelihood of target information is then selected for further analyzation and the hypothesis selected and other hypotheses are retained for further processing with new incoming datum.
A method and apparatus for designing perturbation signals to excite a number of input variables of a system, in order to test that system for the purpose of obtaining models for the synthesis of a model-based controller. The method begins with providing input parameters of the system. A plurality of binary multi-frequency (BMF) signals are generated based on these input parameters and the frequency spectra of these BMF signals are calculated. One BMF signal is selected out of the set of BMF signals so that the frequency spectrum of the selected BMF signal most closely matches a desired frequency spectrum specified by the input parameters. The selected BMF signal is used as a first perturbation signal for testing the system. The selected BMF signal is also shifted by predetermined amounts of samples to create delayed copies of the original BMF signal to be used as additional perturbation signals.
The invention concerns a method for processing a signal using an approximate MAP (maximum a posteriori) algorithm for determining a likelihood ratio .LAMBDA..sub.k.sup.X of a set of states X of a lattice at a time k, with each of said states being associated at least one intermediate variable belonging to a group comprising a so-called forward variable and a so-called backward variable, propagated by said MAP algorithm and recursively calculated respectively in a direct orientation and in an indirect orientation at said time k relative to said lattice. The invention is characterized in that said process comprises a step which consists in reducing the number of selected states by said MAP algorithm so as to calculate said likelihood ratio, and, for at least some unselected states, in assigning to said forward variable and/or said backward variable at least one specific value, to calculate an approximate likelihood ratio.