A method of spatially filtering signal parameter vector data includes receiving, at a computing device, a first signal parameter vector at a first time and a second signal parameter vector at a second time occurring after the first time. The first and second signal parameter vectors are derived from a plurality of signals received at a sensor, and include first and second signal data blocks, respectively. The method also includes transmitting, to at least a first and second element of an array data structure representative of a physical spatial domain, the first and second signal data blocks, respectively, and determining an elliptical error region probability object having a center and a pair of axes containing the first and second signal data blocks. The center represents a highest probability location of a signal emitter at the second time and the pair of axes represents the spatial error of the center.

An example method to determine an object spin rate may include training a neural network with a set of initial data. The set of initial data may be generated based on a plurality of initial radar signals of a plurality of initial objects in motion. The method may include receiving a radar signal of a particular object in motion. The method may include converting the radar signal into an input vector. The input vector may include time and frequency information of the particular object in motion. The method may include providing the input vector as input to a trained neural network. The method may include determining a spin rate of the particular object in motion based on an analysis performed by the trained neural. The analysis may include analyzing the input vector including time and frequency information of the object in motion in view of the set of initial data.

A device and methods are provided for determining data points with an integrated radar sensor. In one embodiment, a method includes determining position of a device, scanning one or more objects, wherein scanning includes detecting data points by an integrated radar sensor of the device and capturing image data of the one or more objects, and determining data points for one or more objects based on the scanning. The method may also include correlating data points to one or more portions of the image data, assigning correlated data points to one or more portions of the image data, and storing, by the device, image data with data points. The device and methods may advantageously be employed for one or more of mapping, modeling, navigation and object tracking.

A real-time radar surveillance system comprises at least one land-based non-coherent radar sensor apparatus adapted for detecting maneuvering targets and targets of small or low radar cross-section. The radar sensor apparatus includes a marine radar device, a digitizer connected to the marine radar device for receiving therefrom samples of radar video echo signals, and computed programmed to implement a software-configurable radar processor generating target data including detection data and track data, the computer being connectable to a computer network including a database. The processor is figured to transmit at least a portion of the target data over the network to the database, the database being accessible via the network by at least one user application that receives target data from the database, the user application providing a user interface for at least one user of the system.

A real-time radar surveillance system comprises at least one land-based non-coherent radar sensor apparatus adapted for detecting maneuvering targets and targets of small or low radar cross-section. The radar sensor apparatus includes a marine radar device, a digitizer connected to the marine radar device for receiving therefrom samples of radar video echo signals, and computer programmed to implement a software-configurable radar processor generating target data including detection data and track data, the computer being connectable to a computer network including a database. The processor is figured to transmit at least a portion of the target data over the network to the database, the database being accessible via the network by at least one user application that receives target data from the database, the user application providing a user interface for at least one user of the system.

An example method to determine an object spin rate may include training a neural network with a set of initial data. The set of initial data may be generated based on a plurality of initial radar signals of a plurality of initial objects in motion. The method may include receiving a radar signal of a particular object in motion. The method may include converting the radar signal into an input vector. The input vector may include time and frequency information of the particular object in motion. The method may include providing the input vector as input to a trained neural network. The method may include determining a spin rate of the particular object in motion based on an analysis performed by the trained neural. The analysis may include analyzing the input vector including time and frequency information of the object in motion in view of the set of initial data.

A method of spatially filtering signal parameter vector data includes receiving, at a computing device, a first signal parameter vector at a first time and a second signal parameter vector at a second time occurring after the first time. The first and second signal parameter vectors are derived from a plurality of signals received at a sensor, and include first and second signal data blocks, respectively. The method also includes transmitting, to at least a first and second element of an array data structure representative of a physical spatial domain, the first and second signal data blocks, respectively, and determining an elliptical error region probability object having a center and a pair of axes containing the first and second signal data blocks. The center represents a highest probability location of a signal emitter at the second time and the pair of axes represents the spatial error of the center.

A majorize-minimize (MM) mathematical principle is applied to least squares regularization estimation problems to effect efficient processing of image data sets to provide good quality images. In a ground penetrating radar application, these approaches can reduce processing time and memory use by accounting for a symmetric nature of a given radar pulse, accounting for similar discrete time delays between transmission of a given radar pulse and reception of reflections from the given radar pulse, and accounting for a short duration of the given radar pulse.

There is provided a display device with which the density of ship traffic can be intuitively ascertained, without the display looking complicated even in areas through which ships frequently pass. This ship display device comprises a past track storage component and a display component. The past track storage component stores information related to the past tracks of ships. The display component graphically displays a track density distribution, which is a density distribution of the past tracks. More specifically, the display component displays the track density distribution with a grid divided up in regular intervals. The colors of the sections varies to change from one color to another as the density of the past tracks increases.

An image processing device which generates an image where a target object can be discriminated from an object other than the target object. An image processor (15), for echo signals read from a sweep memory (14), calculates a ratio of echo signals indicating a predetermined level or higher among the echo signals of a predetermined number of samples, and generates image data having a display element according to the calculated ratio. This ratio indicates a lower value for an object more isolated and a higher value for an object existing as a larger mass. The image processor (15)acquires color values according to the calculated ratio. Since the target object, such as a ship, is an isolated object and the ratio becomes low, the color values indicate red, and since inland is an object existing as a large mass and the ratio becomes high, the color values indicate green.