Method and apparatus for entering a flight plan into an aircraft navigation system

An apparatus for entering a flight plan into an aircraft navigation system, the apparatus comprising: an acoustic sampler adapted for sampling a microphone signal and generating an acoustic signal; a waypoint identifier adapted for generating an identified waypoint from the acoustic signal and the flight plan; and a navigation interface adapted for incorporating the identified waypoint into the flight plan and for transmitting and receiving navigation data to and from the aircraft navigation system.

BACKGROUND

The present invention relates generally to the field of speech recognition and more specifically to the use of speech recognition to enter a flight plan into an aircraft navigation system.

Recent advances in navigation devices for General Aviation (GA) aircraft have allowed these devices to convey a great deal of valuable information to the pilot. These devices share a common weakness, however, in their ability to accept detailed information back from the pilot. This weakness is particularly acute with regard to the entry of waypoints for a typical instrument flight plan.

In typical current designs, panel space restrictions have forced avionics designers to use concentric knobs for waypoint identifier entry. Current procedures for entering a flight plan entail rotating a knob through the entire alpha-numeric alphabet for each character in each waypoint. For complex flight plans, such procedures are cumbersome and time consuming and significantly interfere with the pilot's need to scan instrument gauges, maintain visual separation from other aircraft, and attend to other critical tasks.

Opportunities exist, therefore, to improve safety and efficiency in the piloting of GA aircraft by providing a speech recognition interface for entering a flight plan into the aircraft navigation system.

SUMMARY

The opportunities described above are addressed, in one embodiment of the present invention, by an apparatus for entering a flight plan into an aircraft navigation system, the apparatus comprising: an acoustic sampler adapted for sampling a microphone signal and generating an acoustic signal; a waypoint identifier adapted for generating an identified waypoint from the acoustic signal and the flight plan; and a navigation interface adapted for incorporating the identified waypoint into the flight plan and for transmitting and receiving navigation data to and from the aircraft navigation system.

Another aspect of the present invention is embodied by a method for entering a flight plan into an aircraft navigation system, the method comprising the acts of: sampling a microphone signal; generating an acoustic signal; generating an identified waypoint from the acoustic signal and the flight plan; incorporating the identified waypoint into the flight plan; and transmitting and receiving navigation data to and from the aircraft navigation system.

DETAILED DESCRIPTION

In accordance with one embodiment of the present invention,FIG. 1illustrates a block diagram of an apparatus100for entering a flight plan170into an aircraft navigation system200. Apparatus100comprises an acoustic sampler130, a waypoint identifier150, and a navigation interface180. In operation, acoustic sampler130samples a microphone signal120and generates an acoustic signal140; waypoint identifier150generates an identified waypoint160from acoustic signal140and flight plan170; and navigation interface180incorporates identified waypoint160into flight plan170and transmits and receives navigation data190to and from aircraft navigation system200. The transmitted portion of navigation data190includes, without limitation, flight plan170; the received portion of navigation data190includes, without limitation, current aircraft position. To initialize flight plan170, waypoint identifier150generates a first identified waypoint from acoustic signal140and from the current aircraft position.

In accordance with another embodiment of the present invention, acoustic sampler130additionally generates a speech flag signal240indicating which portions of acoustic signal140correspond to a combination of pilot speech and cabin noise and which portions correspond to cabin noise only. Waypoint identifier150then uses speech flag signal240to assist in generating identified waypoint160.

In accordance with a more specific embodiment of the embodiment ofFIG. 1,FIG. 2illustrates a block diagram wherein waypoint identifier150comprises a vocabulary filter270, a geography filter310, and a waypoint constructor330. In operation, vocabulary filter270filters a vocabulary database280to yield a feasible vocabulary set290; geography filter310filters a geography database300using flight plan170to yield a feasible waypoint set320; and waypoint constructor330constructs identified waypoint160from feasible vocabulary set290and feasible waypoint set320. In some embodiments, acoustic signal140and speech flag signal240are also used by vocabulary filter270to filter vocabulary database280.

In accordance with a more specific embodiment of the embodiment ofFIG. 2, vocabulary database280comprises a phonetic alphabet285. Examples of phonetic alphabet285include, without limitation, the International Civil Aviation Organization alphabet wherein the words “alpha,” “bravo,” “charlie,” etc. respectively represent the letters “A,” “B,” “C,” etc.

In accordance with a more specific embodiment of the embodiment ofFIG. 2,FIG. 3illustrates a block diagram wherein waypoint constructor330comprises a waypoint filter360, a model generator380, a feature extractor340, and a waypoint selector400. In operation, waypoint filter360filters feasible waypoint set320using feasible vocabulary set290to yield a candidate waypoint set370; model generator380generates a waypoint model set390from candidate waypoint set370; feature extractor340constructs a signal feature set350from acoustic signal140; and waypoint selector400selects identified waypoint160by matching signal feature set350to an element of waypoint model set390.

In accordance with a more detailed embodiment of the embodiment ofFIG. 3, waypoint model set390comprises a set of hidden Markov word models. In some embodiments, each of the hidden Markov word models comprises a set of semi-hidden Markov triphone models. In some embodiments, waypoint selector400uses a Viterbi search method to match signal feature set350to an element of waypoint model set390. Hidden Markov word models, semi-hidden Markov triphone models, and Viterbi searches are techniques known to persons of ordinary skill in the art of speech recognition and are described in any modern text on speech recognition.

In accordance with a more detailed embodiment of the embodiment ofFIG. 3, feature extractor340uses a zero crossings with peak amplitudes (ZCPA) method. The ZCPA method is known to persons of ordinary skill in the art of speech recognition and is described in D. Kim, S. Lee, and R. M. Kil, “Auditory processing of speech signals for robust speech recognition in real-world noisy environments”, IEEE Trans. Speech Audio Processing, vol. 7, no. 1, pp. 55–69, January 1999.

In accordance with another more specific embodiment of the embodiment ofFIG. 2,FIG. 4illustrates a block diagram wherein vocabulary filter270comprises a zero crossing detector490and a comparator510. In operation, zero crossing detector490detects zero crossings of acoustic signal140to yield a zero crossing set500. Comparator510compares zero crossing set500to zero crossing data from vocabulary database280to yield feasible vocabulary set290.

In accordance with another more specific embodiment of the embodiment ofFIG. 1,FIG. 5illustrates a block diagram wherein acoustic sampler130comprises an analog-to-digital converter210, a speech detector230, a noise model250, and a subtracter265. In operation, analog-to-digital converter210converts microphone signal120to a raw acoustic signal220; speech detector230generates speech flag signal240from raw acoustic signal220; noise model250generates a noise estimate260from raw acoustic signal220and speech flag signal240; and subtracter265subtracts noise estimate260from raw acoustic signal220to yield acoustic signal140.

In accordance with a more detailed embodiment of the embodiment ofFIG. 5, speech detector230generates speech flag signal240using a linked hidden Markov model. Use of linked hidden Markov models for this purpose is known to persons of ordinary skill in the art of speech recognition and is described in S. Basu, “A linked-HMM model for robust voicing and speech detection”, Proc. Int. Conf. Acoustic, Speech, and Signal Processing (ICASSP), vol. 1, pp. 816–819, 2003.

In accordance with a more specific embodiment of the embodiment ofFIG. 5,FIG. 6illustrates a block diagram wherein noise model250comprises a noise extractor410, a magnitude calculator430, a phase calculator450, and a waveform constructor470. In operation, noise extractor410extracts a cabin noise signal420from raw acoustic signal220using speech flag signal240; magnitude calculator430calculates an estimated magnitude set440from cabin noise signal420; phase calculator450calculates an estimated phase set460from cabin noise signal420; and waveform constructor470constructs noise estimate260from a set of noise signatures480using estimated magnitude set440and estimated phase set460.

All of the elements described above of embodiments of the present invention may be implemented, by way of example, but not limitation, using singly or in combination any electric or electronic devices capable of performing the indicated functions. Examples of such devices include, without limitation: analog devices; analog computation modules; digital devices including, without limitation, small-, medium-, and large-scale integrated circuits, application specific integrated circuits (ASICs), and programmable logic arrays (PLAs); and digital computation modules including, without limitation, microcomputers, microprocessors, microcontrollers, and programmable logic controllers (PLCs).

In some embodiments of the present invention, the elements described above are implemented as software components in a general purpose computer. In some embodiments, aircraft navigation system200is also a software component implemented in the same computer as apparatus100. Such software implementations produce a technical effect of recognizing pilot speech and entering a flight plan into an aircraft navigation system.