A method for improving dynamic frequency selection (DFS) includes receiving, by an access point, a plurality of pulses in a DFS channel of the access point, determining, by the access point, a plurality of characteristics of the plurality of pulses, varying, by the access point, a threshold for radar detection, and determining, by the access point and based on at least one of the plurality of characteristics, whether the plurality of pulses are radar.

Distribution fitting Constant False Alarm Rate (CFAR) detection is described. Noise data in cells or bins around a target cell are fit to a noise distribution model, such as a Rayleigh distribution model. With a suitable noise distribution curve from the distribution model, a CFAR threshold for that cell along the curve can be determined. A quantile function of the noise distribution model for a bin or cell provides the CFAR threshold to use for that bin or cell. Distribution fitting CFAR enables a more-accurate CFAR threshold to be set for each bin or cell and may use far fewer computing resources than Ordered-Statistics CFAR. A radar detector can better prevent false alarm detections across multiple different driving scenarios by adapting to different environments and dynamically changing the noise distribution curve used depending on best-fit analysis by a noise distribution model of noise characteristics of the neighboring bins or cells.

A method of using a directional sensor for the purposes of detecting the presence of a vehicle or an object within a zone of interest on a roadway or in a parking space. The method comprises the following steps: transmitting a microwave transmit pulse of less than 5 feet; radiating the transmitted pulse by a directional antenna system; receiving received pulses by an adjustable receive window; integrating or combining signals from multiple received pulses; amplifying and filtering the integrated receive signal; digitizing the combined signal; comparing the digitized signal to at least one preset or dynamically computed threshold values to determine the presence or absence of an object in the field of view of the sensor; and providing at least one pulse generator with rise and fall times of less than 3 ns each and capable of generating pulses less than 10 ns in duration.

A vehicle radar system, apparatus and method use a radar control processing unit to generate a target response signal in at least a first dimension from compressed radar data signals and to perform cell-averaging constant false alarm rate (CA-CFAR) target detection by convolving the target response signal with a weighted kernel window signal in a frequency domain using a Fast Fourier Transform hardware accelerator, an element-wise multiplier, and an Inverse Fast Fourier Transform hardware accelerator to generate an output signal having a sign that indicates a target detection decision.

A vehicle radar system, apparatus and method use a radar control processing unit generate compressed radar data signals, to apply the compressed radar data signals to a log detector to generate log detector sample values, and to generate a first log cell-average constant false alarm rate (CA-CFAR) threshold from the log detector sample values by computing and adding an average sample value S.sub.AVG from the log detector sample values, a probability of false alarm factor α, and a log CA-CFAR correction factor β, where the first log CA-CFAR threshold may be used with a second log CA-CFAR threshold to generate an ordered statistics CA-CFAR threshold for the compressed radar data signals by sorting the first and second log CA-CFAR thresholds by magnitude to form a sorted list of log CA-CFAR thresholds, and then selecting a kth threshold from the sorted list of log CA-CFAR thresholds as the OS-CA-CFAR threshold.

Determining a target's range profiles is an important issue for coastal surveillance radars because it can give us the knowledge about the target, for example, target's type, target's structure and its length along radial direction. Some modern radars nowaday are equipped with the feature of target's range profile extraction, but the results are not accurate due to limitations in processing algorithms. The invention “system and method of determining target's range profiles for coastal surveillance radars” solves the above problem in the direction of proposing a system of technical solutions and associated algorithm improvements.

A range profile digitization circuit for converting a repeating analog input signal into a time series of digital amplitude values, the converter comprising: a signal quantizer arranged to receive the analog input signal and a threshold input and arranged to output a binary value quantized output signal based on a comparison of the input signal with the threshold signal; a plurality of samplers each arranged to sample and hold its input signal upon receipt of a trigger signal; and for each sampler: a plurality of decoders and a demultiplexer arranged to receive an output from the sampler and pass it to a selected one of said decoders based on a selector input. With a plurality of decoders associated with each of the samplers, each sampler can be re-used during the building up of the range profile.

Empirical data fitting with Ordered Statistic Constant False Alarm Rate (CFAR) detection is described. An empirical approach is used to derive data for indicated expected target responses to provide a CFAR in a variety of different noise distributions. Multiple (e.g., at least two) ordered-statistics are extracted from radar data, which are then used identify a ratio for mapping to an appropriate CFAR multiplier of quantile function for a distribution at hand. Empirical data fitting evaluates an ordered-statistic ration (OSR) against expected OSR values. From evaluating the expected OSR values derived from multiple test frames, a mapping between measured OSR values and their appropriate CFAR multiplier is derived. Through this empirical data fitting, a radar system can perform CFAR detection to account for shape shifts or other variations in a noise distribution beyond just fluctuations in noise strength.

A method for retrieval of lost radial velocity in weather radar includes expanding a radial velocity area to non-meteorological echoes including sea clutter and chaff echo using raw radar data for use of a wind field calculation area, correcting radial velocity by replacing the radial velocity determined as noise using a median sign comparison method with a median calculated within a window to which the radial velocity belongs, distinguishing a lost radial velocity area by comparing the corrected radial velocity with radar reflectivity data, and retrieving lost radial velocity using a Velocity Azimuth Display (VAD) fit function representing radial velocity of particles observed along a radar radiation source at a certain elevation in the lost radial velocity area as a function of an azimuth angle. Accordingly, it is possible to improve the quality of calculated wind field using the improved radar radial velocity, and provide more accurate dynamic structure information of the precipitation system.

A method of using a directional sensor for the purposes of detecting the presence of a vehicle or an object within a zone of interest on a roadway or in a parking space. The method comprises the following steps: transmitting a microwave transmit pulse of less than 5 feet; radiating the transmitted pulse by a directional antenna system; receiving received pulses by an adjustable receive window; integrating or combining signals from multiple received pulses; amplifying and filtering the integrated receive signal; digitizing the combined signal; comparing the digitized signal to at least one preset or dynamically computed threshold values to determine the presence or absence of an object in the field of view of the sensor; and providing at least one pulse generator with rise and fall times of less than 3 ns each and capable of generating pulses less than 10 ns in duration.